dataesr/scientific-paragraphs-categorization huggingface dataset:
Tests on the IRSTV dataset output an APE RMSE of 3. 3% of the traveled distance, which is a satisfying result for most 17 Journal of Sensors pedestrian localization applications. However, Vins-Mono struggles a lot with the initialization, probably due to the absence of a specific initialization phase at the beginning of the acquisition, contrary to EuRoC. It can generally initialize when motion with sufficient parallax is detected. However, the quality of the IMU acceleration bias estimated at this moment is uncertain, which greatly impacts the accuracy and robustness of the results. We also found out that glass door reflections were repetitively the cause of failures. Furthermore, the real-scale estimation on the IRSTV dataset is far less precise than in the close environment of EuRoC (around 43% of error!).
Nonetheless, we believe these results to be improvable. Adding specific motion for dynamic initialization at the beginning of the dataset would probably have improved the results and robustness. Globally, Vins-Mono is found to be robust to the difficulties in the presented scenarios, and its great RPE RMSE is promising for lifelong experiments. Although ORB-SLAM2's results are still overall better, the real benefit of Vins-Mono is its ability to accurately estimate the real scale. Consequently, the results can easily be interpreted online as real-world poses on a given map for instance. However, results on the IRSTV dataset show that it can be difficult to meet the requirements in terms of hardware to get the best out of Vins-Mono or that specific user movement might be needed to correctly initialize the IMU bias.
DSO correctly estimates the path and reconstructs the environment for the IRSTV dataset. The scale estimation fails when the camera moves from a street to a large place with a trajectory estimated with two different scales. We only 18 Journal of Sensors considered the second part of the trajectory for the APE error estimation since it is the only part well reconstructed by the three methods (Figure  11 ). With a 3. 8% APE RMSE over the traveled distance, the results are less precise results than those of ORB-SLAM2 and Vins-Mono. Let us notice that DSO did not face difficulty to initialize, contrary to Vins-Mono cf. Section 7. 3. 2. The lower accuracy, as well as the scale estimation issue, can be explained by the use of a rolling shutter camera, whereas direct methods perform better with global shutters.
LSD-SLAM Result Analysis. As far as we know, there are no public tests of LSD-SLAM on EuRoC available. Its package is the oldest of all the algorithms tested here, so we are not refuting the possibility that an adaptation to EuRoC is possible (for instance the Stereo version [90] might be better performing here. (The open-source github project neither seems to be maintained anymore, nor to be upgraded to recent Ubuntu and ROS versions. ) However, by using the regular package, parameters, and recommendations accessible today, we managed to run LSD-SLAM on MH03 and MH05. Despite encouraging results on MH01 (Figure  12 ), its robustness to movement and maybe lack of texture was not sufficient to produce decent results for MH03 and MH05. Also, the tracking failed before the end for every MH03 run we made and on three out of five runs on MH05.
Note that to better support the initialization of LSD-SLAM, we cut off the beginning of the dataset, where the drone moves erratically, to initialize its IMU parameters. Observing the very random behavior obtained on the multiple runs, it seems clear that LSD-SLAM's failure here is due to bad initialization. Most probably, the LSD-SLAM results do not reflect the real abilities of this method in ideal conditions. This result highlights the fact that methods requiring specific attention during initialization are less suited for many applications where such care cannot be brought. The need for specific user intervention and control are critical elements to consider when choosing a SLAM method. With the current settings and initialization process, 19 Journal of Sensors LSD-SLAM was found to be not robust enough for scenarios similar to those presented in EuRoC Machine Hall.
7. 3. 5. ROVIO Result Analysis. Looking at the dataset MH01 and MH03, among all methods, ROVIO outputs the worst RPE RMSE. The cumulative error distribution plots, shown in Figure  13 ), show that 80% of the positioning errors for MH01 and MH03 are below 25 cm along the x axis, 20 cm along the y axis, and 15 cm along the z axis. Globally, the approach is still accurate with, for example, the maximal errors along the y axis for MH01 and MH03, with 40 cm and 80 cm, respectively. Overall, it gives one of the worst APE (LSD-SLAM failures apart), with an APE RMSE 3 to 20 times worse than the other methods, and an APE max 3 to 12 times worse. All ROVIO runs give the same results on a dataset; there are no stochastic parameters. The general scale estimation is good enough for real-scale trajectory estimation.
Its predictability is interesting for repetitive tasks and known scenarios. However, textureless environments seem to introduce serious issues and the lack of consistency in the results is problematic for applications that require to continuously assess the precision. ROVIO failed on the IRSTV dataset. are shown in Figures  14 15 16. As for rotation estimation only, all methods perform relatively good without any distinctive difference. We observe a small tendency to drift in ROVIO's yaw estimation. It is illustrated with the rotation estimation for MH03 in Figure  17. Due to the potential lack of loop and our focus on the correctness of live pose estimation, a strong VO or VIO base is needed. Therefore, we added the results for Vins-Mono  [74]  and ORB-SLAM2  [76]  without loop closure in Table  7.
Choosing ORB-SLAM2 for its more precise results may be a dangerous bet if loop closure is difficult to perform in the considered environment. The reason is that its results are heavily impacted by a problem in pose estimation due to a momentary lack of texture. Finally, the computation on the IRSTV dataset finds ORB-SLAM2 to be the most robust approach. It handles urban space difficulties (glass reflections, scale changes, and pedestrian motion) and even indoor-to-outdoor transitions since illumination changes do not cause major ORB-SLAM2 failure as in MH05 without loop closure. In conclusion, DSO, Vins-Mono, and ORB-SLAM2 are all suitable choices for our use case, i. e. , pedestrian urban navigation with handheld sensors. The choice also depends on the type of hardware available: GPU+global shutter for DSO, tightly synchronized IMU, and camera for Vins-Mono.
Absolute pose error for each tested method on each dataset, averaged on five Results in italics indicate that the algorithm failed on some of the five runs by losing track. The numbers are thus average on the other runs. The numbers in parentheses are the standard deviation of the RMSE (which is averaged on five runs). Results on IRSTV's dataset are further explained in the result analysis of each concerned method. None of the methods reconstructed the full IRSTV path but only some parts of it: DSO 292. 75 m, ORB-SLAM2 596. 80 m, and Vins-Mono 212. 83 m. APE was obtained with evo package github. com/MichaelGrupp/evo.
Data Availability The data used in this article are from the EuRoC dataset from  [8]  available at https://projects. asl. ethz. ch/datasets/ doku. php?id=kmavvisualinertialdatasets. The IRSTV dataset is provided as supplementary material following the same formatting as EuRoC dataset. Available data are as follows: visual-inertial sensor unit-images (VIRB 30 Ultra, 20 Hz)-MEMS IMU (VN300 (VectorNav), 200 Hz)-ground truth STIM300 (Sensonor) in 2D and calibration with (i) camera intrinsics and (ii) camera-IMU extrinsic. They can be downloaded here: 10. 5281/zenodo. 4415641.
Stimuli Two-second (s) silent movies of point-light displays were created from the "Communicative Interaction Database -5AFC format" (CID-5;  Manera et al. 2016 ). The database includes movies featuring an interaction between two human bodies, each defined by 13 white dots in correspondence with the major reference points (top of the head, shoulders, elbows, hands, hips, knees and ankles). From those movies, we isolated 28 single bodies performing an action with an invisible object or without an object, and asked a group of participants external to the fMRI experiment (n=30), to describe the action of each single body presented alone. Ambiguous stimuli, i. e. those whose description did not match the description in the original database, were eliminated. The twenty bodies that obtained the most consistent description across participants were selected to create ten dyads in which two bodies acted simultaneously, with no contact or obvious meaningful interaction.
Stimuli in this task and in the following functional localizer tasks were back-projected using an LCD projector (frame rate: 60 Hz, screen resolution: 1024 x 768 pixels, screen size: 40 x 30 cm) onto a translucent screen positioned at the rear of the magnet. Participants viewed the screen binocularly (7° of visual angle) through a mirror above the head coil. Stimulus presentation, response collection and synchronization with the scanner were controlled with the Psychotoolbox  (Brainard 1997; Pelli 1997 ) through MATLAB (MathWorks, MA, USA).
Regions of interest (ROIs) definition Using the data from the functional localizer tasks, we identified in each participant two ROIs: For the remaining participants who did not perform this task (n = 5), EBA was defined by selecting, for the contrast of interest, the 200 best voxels obtained at the group level (second level). PPA. Using the data collected during the functional localizer task for the EBA, we defined the PPA as a high-level control ROI, to assess the specificity of the effects of body positioning in the two target ROIs (EBA and bm-pSTS). PPA was defined in 15 participants, by entering the individuals' data into a GLM with two regressors of interest (places, objects), one regressor for baseline blocks and six regressors for movement correction parameters as nuisance covariate.
ROIs analysis Mean activity values (mean β-weights minus baseline) for facing and non-facing dyads were extracted separately from the left and right ROIs (bm-pSTS, EBA, PPA and EVC). These data were analysed in a 2 Hemisphere (left vs. right) x 4 ROI x 2 Spatial relation (facing vs. nonfacing) ANOVA. Post-hoc comparisons between conditions were performed using pairwise ttests (α = 0. 05, two-tailed). All statistical analyses were performed with Statistica (StatSoft, Europe, Hamburg) and power analyses were realized using G*Power  (Faul et al. 2009 ).
After the probe, a blank remained on the screen until the participant gave a response. The next trial began after 500 ms. Participants were instructed to judge if the probe was identical or different with respect to the sample, by pressing one of two keys on a keyboard in front of them ("q" for "same" and "m" for "different"), as soon as possible. Participants sat on a height-adjustable chair, 60 cm away from a computer screen, with their eyes aligned to the center of the screen (17-in. CRT monitor; 1024 x 768 pixel resolution; 85-Hz refresh rate). Stimuli on the screen did not exceed 7° of visual angle. Between two runs, participants were invited to take a break. A run of familiarization preceded the experiment (20 trials). The experiment lasted approximately 40 min. Stimulus presentation and response collection (accuracy and reaction times, RTs) were controlled with Psychtoolbox  (Brainard 1997 ) through MATLAB.
As Schrödinger himself stated in § 3 of the paper his "current concern" was to point out the existence of a classical probabilistic structure such that the probability density is given by "the product of a certain solution of " [the forward diffusion equation] "and a certain solution of " [the backward diffusion equation] and thus presenting "a striking analogy with quantum mechanics" ( § 4). The observation of this analogy initiated parallel, and somewhat intertwined, lines of research aiming at either finding classical probabilistic analogues of quantum mechanics or more directly a classical probabilistic interpretation of quantum mechanics. Already in 1933, Reinhold Fürth showed  [12, I]  (see  [31, I]  for a translation) what in current mathematical language could be phrased as the existence of uncertainty relations satisfied by the variance of a martingale of a diffusion process times the variance of the martingale's "current velocity"  [27, I].
After the second world war, Fürth's work became the starting point of the "stochastic mechanics" program proposed by Imre Fényes  [11, I]  and Edward Nelson  [26, 27, I]  as a probabilistic interpretation of quantum mechanics. A similar program was also pursued by Masao Nagasawa  [24, 25, I]  and Robert Aebi  [1, I]. The collection  [10, I]  offers a recent appraisal of the state of the art focusing on Nelson's contributions. In a spirit perhaps closer to Schrödinger's original idea, "Euclidean Quantum Mechanics"  [37, I]  applies modern developments of stochastic calculus of variations and optimal control theory (see  [20, 38, I]  for recent surveys) to study how "relations between quantum physics and classical probability theory" may "lead to new theorems in regular quantum mechanics"  [37, I].
ACKNOWLEDGMENTS We warmly thank two anonymous reviewers for their careful reading of our manuscript and many very useful suggestions. Their feedback allowed us to greatly improve the quality of the translation and of the paper overall. The authors are also glad to acknowledge useful comments and encouragement from  Angelo Vulpiani ,  John Bechhoefer ,  Hugo Touchette ,  Massimo Cencini ,  Brecht Donvil , and  Ruben Pasmanter. Special thanks go to  Michael McAuley  who helped us to improve the language quality of the text. R. C. is supported by the  French National Research Agency  through the projects  QTraj  ( ANR-20-CE40-0024-01 ),  RETENU  ( ANR-20-CE40-0005-01 ), and  ESQuisses  ( ANR-20-CE47-0014-01 ).
, 2020 ). An R-code was first proposed in 2016  (Aldenberg, 2019 ) that allowed users to analyze the data collected only from the OCED test guideline 305. In the same line of thought, a spreadsheet was recently proposed by  Gobas et al. (2020). Our paper goes beyond these two studies by considering all types of species-compound accumulation-depuration data (not only those of the OECD test guideline 305), which analysis leads to one or several bioaccumulation metrics of interest. Ratier et al. (2019)  recently proposed a full revisit of the TK modeling approach based on a unified inference method to estimate parameters of TK models for both accumulation and depuration phases, simultaneously, automatically associating the uncertainties. This innovative framework has been thought to make it possible to further incorporate the TK part into complete TKTD models.
Calculations Bayesian inference. Computations underlying MOSAIC bioacc results are performed with JAGS  (Plummer, 2017, version 4. 3. 0) and the R software (R Core Team, 2020, version 4. 0. 2) via the rjags and jagsUI packages  (Kellner, 2019; Plummer, 2019). Models are fitted to bioaccumulation data using Bayesian inference via Monte Carlo Markov Chain (MCMC) sampling. For each model, calculation running starts with a short sampling on three MCMC chains (5000 iterations after a burn-in phase of 10 000 iterations) using the Raftery and Lewis method  (Raftery & Lewis, 1992)  to set the necessary thinning and the appropriate number of iterations in order to reach a precise and accurate estimation of each model parameter. Thanks to rjags, model parameters are retrieved as a joint posterior distribution from the likelihood of the observed data combined with prior information on parameters.
All details on this approach can be found in the original research paper  (Ratier et al. , 2019)  but also in many other papers in the field of ecotoxicology  (Billoir et al. , 2011; EFSA PPR Panel, 2018). Choice of prior distributions. For simplicity reasons, we hid the choice of priors to MOSAIC bioacc users; hence, they cannot be changed, except by downloading the open-source programming code and handling it directly within the R software. To ensure genericity of priors we ∑ = = U k c i I u i 1 i Sum of all uptake terms ∑ = = E k j J e 1 j Sum of all elimination terms for the parent compound l l ∑ = = M k L m 1 Sum of all elimination terms for all metabolites l chose noninformative (-5, 5) log10-uniform distributions for all uptake and elimination rate constants, and noninformative (0, A) uniform distributions for all standard deviations with a large A, here defined as five times the maximum internal measured concentration, which is then removed from the data set, as usually proceeded  (Gelman, 2006).
The overall expectation is to get a narrower posterior distribution compared to the prior one for each parameter, reflecting that data contributed enough to precisely estimate parameters (Figure  3B ). Users have the possibility to select plots for deterministic (e. g. , k uw and k ee ) or stochastic (e. g. , σ Cp ) parameters. Then, MOSAIC bioacc provides a colored matrix in order to see at a glance the most correlated or anti-correlated parameters, in order to quickly diagnose potential problems of precision due to highly correlated parameters. Moreover, MOSAIC bioacc provides plots to visualize correlations between parameters (Figure  S1a ). Such a plot is obtained by projecting the joint posterior distribution as a matrix in planes of parameter pairs where contours have shapes reflecting both the sign and the strength of the correlations (subdiagonal).
Downloads At the bottom of the result web page, all outputs, either separately or as a full report, can be downloaded. Users can also download the entire R code corresponding to all calculations and graphs from the uploaded data set. This ensures transparency and reproducibility of all MOSAIC bioacc results. This R code can be used as a stepping stone to change default options or to perform further analyses directly in the R software. For example, users can modify figures at their convenience or make several analyses on several data sets at the same time.
CONCLUSION Offering MOSAIC bioacc as a new online service, free, user-friendly, and ready-to-use, raises important methodological issues: (1) Automation of the inference process, in particular for Bayesian inference; (2) options and choices in a transparent and facilitated way for users; (3) default outputs whose order has been chosen to facilitate their step-by-step interpretation; (4) easy accessibility to figures, tables and R code to be downloaded under different convenient formats; and (5) a final full report of all MOSAIC bioacc analyses. Besides its user-friendliness, MOSAIC bioacc is free of use while ensuring the privacy of the uploaded data as well as transparency and reproducibility of results, together with a short response time. MOSAIC bioacc is particularly useful to estimate parameters of TK models leading to predictions of chemical concentrations bioaccumulated within living organism (whatever the species, aquatic, aerial, or terrestrial) from accumulation-depuration data, even standard ones.
MOSAIC bioacc could, thus, be of particular interest for risk assessors and decision makers in their daily work of evaluating dossiers, for example, for market authorization of active substances (EFSA PPR Panel, 2018). Indeed, all results provided by MOSAIC bioacc account for uncertainty and correlations between parameters, making it possible to reproduce any previous analysis that would need to be confirmed. MOSAIC bioacc can also be used for more exploratory research purposes by any environmental scientists or ecotoxicologists when accumulation-depuration data are collected and need to be analyzed. MOSAIC bioacc allows analyses for any species-compound combinations under consideration even with biotransformation processes, allowing users to easily perform TK analyses accounting for several exposure routes and phase I metabolites.
Since recently, we offer access to an online database providing a wide collection of TK data sets illustrating the diversity of MOSAIC bioacc capabilities  (Ratier & Charles, 2021). MOSAIC bioacc is available as a new statistical service dedicated to TK modeling approaches embedded within the MOSAIC platform  (Charles et al. , 2018). This makes MOSAIC an all-in-one facility for many applications. MOSAIC bioacc will be shortly extended with a prediction tool, in order to help in designing new TK experiments, for example, when a new species-compound combination requires attention or when additional data are needed to get better precision on bioaccumulation metrics. To gain again in generality, an R-package (rbioacc) is already in progress to include all the functionalities encompassed in MOSAIC bioacc , which will also make it possible to extend the use of TK models when the exposure concentrations vary over time, and when it is necessary to consider non-first order kinetics.
ACKNOWLEDGMENT The authors are thankful to ANSES for providing the financial support. The MOSAIC bioacc web tool is hosted at the Rhône-Alpes Bioinformatics Center PRABI  (PRABI, 2020). This work benefited from the French GDR "Aquatic Ecotoxicology" framework which aims at fostering stimulating scientific discussions and collaborations for more integrative approaches. The authors are truly grateful to the anonymous colleagues who participated in testing MOSAIC bioacc and for giving their feedback. The authors also are grateful to Benoît BRET for creating the logo for MOSAIC bioacc. This work is part of the ANR project APPROve (ANR-18-CE34-0013) for an integrated approach to propose proteomics for biomonitoring: Accumulation, fate, and multimarkers (https://anr. fr/Projet-ANR-18-CE34-0013).
This work was also made under the umbrella of the Graduate School H2O'Lyon (ANR-17-EURE-0018) and "Université de Lyon" (UdL), as part of the program "Investissements d'Avenir" run by "Agence Nationale de la Recherche" (ANR). available at https://mosaic. univ-lyon1. fr/bioacc. Learn more about the Open Practices badges from the Center for Open Science: https://osf. io/tvyxz/wiki.
This approach contributes in addressing important issues associated with artificial intelligence in sensitive contexts (e. g. , ethical and organizational)  (Keskinbora, 2019; Triberti et al. , 2020a) , as it encourages health professionals to actively engage in iterative discourse to preserve humanitarian sensitivity in the future models of care. Tariq, Babar et al. related to category E, propose and test a framework based on Apache Spark for efficiently processing the big datasets resulting from user comment activities triggered by videos on social media. The article shows the potential effectiveness of the devised implementation, which was able to perform the planned analytics operations on social media dataset in a time that well-scales with the data size. Specifically, they provide a new concrete demonstration of processing big data coming from an extended social hub named Dailymotion within a time frame of few minutes using Apache Spark.
We present Raptor, a system for approximately searching many queries such as next-generation sequencing reads or transcripts in large collections of nucleotide sequences. Raptor uses winnowing minimizers to define a set of representative kmers, an extension of the interleaved Bloom filters (IBFs) as a set membership data structure and probabilistic thresholding for minimizers. Our approach allows compression and partitioning of the IBF to enable the effective use of secondary memory. We test and show the performance and limitations of the new features using simulated and real datasets. Our data structure can be used to accelerate various core bioinformatics applications. We show this by re-implementing the distributed read mapping tool DREAM-Yara.