dataesr/scientific-paragraphs-categorization huggingface dataset:
The effect of including extinct and introduced species on diversity scaling relationships For ten true island data sets (Canaries, Cook Islands, Hawaii, Lesser Antilles, Marianas, Society Islands, Cape Verde, New Zealand, Azores, and Ryukyu Islands), there were a relatively large number of species introduced to each archipelago (ranging from 11 to 60% [median = 19%] of the total contemporary archipelago bird fauna). For these data sets, we also created alternative versions representing the current faunas with introduced species included (we only considered currently established introduced species; see Appendix S1). For the first eight of those, we were also able to build data sets representing the historic fauna, that is, the island composition around 1500 CE, including extinct species and extirpated extant species.
For five data sets where (coarse) data were available (Hawaii, Marianas, Cook Islands, New Zealand, Canaries), we also built data sets representing the prehistoric fauna (i. e. prior to human colonization of the islands; including all species known to have gone extinct in the last ~125,000 years) excluding marine species (Appendix S2). For the Marianas and Cook Island prehistoric data sets, we removed a number of islands as we decided to focus on islands where more fossil data were available. The historic and pre-historic data sets (i. e. including extinct species) were built using a range of literature sources (see Appendix S2 for details). The functional traits of extinct species were initially sourced through measurements made on specimens in various museums and literature searches. For 135 of the 158 extinct species, we were able to acquire at least one measurement from skin or skeleton (or both) specimens in museums, with body mass being estimated for the remaining species (see Appendix S2).
All gaps were then imputed using Bayesian Hierarchical Probabilistic Matrix Factorization  (Schrodt et al. , 2015). We ran the imputation ten times, averaging the imputed values across the ten runs. As a sensitivity test, we re-ran the analyses using a randomly selected individual imputation run rather than averaging. Extinct species were also grafted onto our consensus phylogeny. Appendix S2 provides a detailed description of the extinct species data collection, trait inference and phylogeny grafting. We then re-ran the power and linear model fitting for the historic and introduced species data sets, storing the power model z-values for the ISAR, IFDAR, IPDAR and the slopes (of the linear model) of the two ES-area relationships. We compared the values with those from our main analyses (i.
R E SU LT S In total, we sourced 51 data sets (26 true island and 25 habitat island archipelagos), incorporating 1051 islands and 2111 species (1953 extant and 158 extinct species) (Appendix S1). The size of habitat islands ranged from 0. 004 to 1592 km 2 , and true islands from 0. 001 to 150,437 km 2. A map of the locations of these data sets is provided as Figure  S1  in Appendix S3. All best fit models, and the power and linear model parameters, for all five relationships across all data sets are provided in Table  S3  in Appendix S3.
Q1 and Q2: ISAR, IFDAR and IPDAR model form The non-asymptotic convex-upward Kobayashi, power and logarithmic models were always the three models with the highest mean AIC c weight values for the ISAR, IFDAR and IPDAR (but not always in the same order), across the 51 data sets (Figure  2 ). Inspecting the plots of model fits provided further evidence for the convexupward nature of most of the ISARs, IFDARs and IPDARs (e. g. Figure  3 ). In terms of the number of best fits (i. e. cases of lowest AIC c for a given IDAR and data set), the top model was always the power model, with the linear, logarithmic and Kobayashi models alternating in F I G U R E 2 Generally, the shape of IDARs consistently had a convex-upward nature with some variation in exact model shape between the ISAR, IFDAR and IPDAR.
The two bird photographs show example species from each archipelago: the middle right plot shows a lava gull (Larus fuliginosus), the rarest gull in the world and a species endemic to the archipelago, and the bottom right plot shows a Rüppell's warbler (Sylvia rueppelli), a species that breeds in Greece, Turkey and the Aegean Islands. Middle row photograph by Andy Morffew and under licence (https://creat iveco mmons. org/licen ses/by/2. 0/); bottom row photo by Mick Sway and under licence (https://creat iveco mmons. org/licen ses/by-nd/2. 0/). case when considering only true islands or only habitat island data sets. Figure  5  provides the results of the exploratory modelling of correlations between IDAR slopes and archipelago features. When considering all data sets, ISAR, IFDAR and IPDAR slopes were significantly positively correlated with Ni (number of islands), and significantly negatively correlated with (maximum) elevation and temperature.
Q4: The effect of including extinct and introduced species on diversity scaling relationships The power model z-value for the ISAR, IFDAR and IPDAR followed an interesting and relatively consistent pattern across the three data set types: historic fauna (A), current fauna excluding introduced species (B) and current fauna including introduced species (C) (Figure  7 ). For these three IDAR types, z decreased or remained roughly constant between A and B, and then generally increased between B and C. This pattern was stronger for certain data sets (e. g. Society Islands, Marianas) compared to others (Figure  7 ). The (paired) Wilcoxon signed-rank tests indicated that the differences between A and B were significant for the ISAR (p = 0. 03), IFDAR (p = 0. 02) and IPDAR (p = 0. 02). The differences between B and C were also significant for all three IDAR types (p = 0.
Sensitivity analyses The full results of all sensitivity analyses are presented in Appendices S4-S7. First, re-running the analyses using body-size corrected traits to construct the functional dendrogram resulted in very similar findings (Appendix S4). Second, re-running the analyses after subsetting the data sets to only include land birds also generated mostly similar results (Appendix S5). The main differences here related to the exploratory correlations (e. g. no significant associations involving the number of islands or isolation), and the introduced and extinct species analysis: while some data sets followed the same pattern as the main results, the general pattern was less clear and none of the ISAR, IFDAR or IPDAR F I G U R E 5 Some characteristics of archipelagos are correlated with IDAR slopes.
The figures show Pearson's rank correlation heatmaps, with IDAR slope on the x-axis and various archipelago-level predictors on the y-axis. For the ISAR, IFDAR and IPDAR, slope was measured as the power model z-value. Correlations for IFDAR and IPDAR z-values were also undertaken using partial correlation using ISAR zvalues as a covariate. For the ES-area relationships, the slope was the slope of a linear model fitted in semi-log 10 space. Correlations were undertaken twice, once using all 51 data sets (a), and once using only the 26 true island data sets (b). Significant coefficient values (p < 0. 05) are indicated using black circles. Cell colour indicates correlation strength. Grey cells indicate a correlation was not undertaken for that variable combination. Predictor acronyms are GA, Gamma; C, power model c-value; AA, ArchArea; AS, AreaScale; NI, number of islands; MD, MeanDist; IS, isolation; EL, elevation; and TP, temperature.
Note that for the IFDAR and IPDAR correlations, Gamma was the total functional or phylogenetic richness. paired Wilcoxon tests were significant, although this is perhaps expected given the smaller number of data sets involved (Appendix S5). Third, undertaking the model selection using residual assumption checks resulted in very similar results (Appendix S6). The power model passed the assumption checks for 41, 41 and 45 data sets for the ISAR, IFDAR and IPDAR, respectively. Using the z-values from the linear (log 10 -log 10 ) power model also generated similar results (Appendix S6). Fourth, using a randomly selected trait imputation run in combination with an individual  Jetz et al. (2012)  phylogeny resulted in very similar findings (Appendix S7).
As expected (Q2), we observed that, for a given data set, the ISAR was generally steeper than the IPDAR, which was in turn steeper than the IFDAR. These results indicate that as island area increases, more species are sampled from the archipelagic pool. These additional species initially add novel traits and phylogenetic branches to the island communities, but this process slows down with increasing richness as an increasing proportion of these species are functionally and to a slightly lesser extent phylogenetically, redundant (see also discussion in  Karadimou et al. , 2016; Schrader et al. , 2021; Ferreira-Arruda et al. , 2022). Our exploratory correlation modelling indicated that IDAR slope was significantly negatively associated with elevation, when focusing on all data sets and true islands data sets separately.
For true islands, this may seem counterintuitive as many of the relatively isolated (mostly oceanic) archipelagos (e. g. Hawaii, Cape Verde, New Zealand) have high elevation, and previous studies have theorized and shown that ISAR slope increases with isolation  (Whittaker et al. , 2017). This pattern could be specific to birds: due to their relatively high dispersal ability and the fact that many of the oceanic archipelago data sets are lacking very small islands, it is possible that many bird species are present on most islands, thus lowering IDAR slope. There could also be an effect of anthropogenic extinctions given that these archipelagos are also those that have likely experienced the most extinctions, and our results indicate that in many cases (when excluding introduced species) these extinctions have lowered IDAR slope.
For the true island data set correlations, while few associations were significant, OLS regression models including all predictors explained a relatively large proportion of the variation in the slope of the ISAR, IFDAR and IPDAR (R 2 values: 0. 72-0. 76; adjusted R 2 values: 0. 56-0. 63). This matches the results of previous studies (e. g. Matthews, Rigal, et al. , 2019; Triantis et al. , 2012)  and suggests that the lack of significant correlations here may be due to smaller sample sizes and thus a lack of power.
However, analysis of the scaling of these assembly patterns (effect size [ES] values) reveals a more complex picture, at least in certain cases. For many data sets, the relationships between ES values (assembly patterns) and island area are indeed relatively flat. This indicates that, for these data sets, the convex-upward scaling of unstandardized FD and PD with area was primarily a result of increasing richness with island area, rather than changes in the dominant community assembly processes. However, there were numerous exceptions to this pattern, particularly regarding the FD. ES-area relationship, where the linear model provided a better fit for a third of the 51 data sets. Based on previous work (e. g. Chen et al. , 2020; Matthews et al. , 2020) , we had hypothesised that, owing to limited habitat availability, smaller true islands would be characterized by functional and phylogenetic clustering.
Thus, it could be the case that our theoretical prediction is more applicable to large isolated oceanic island systems, than to other island types. However, it should be noted that, while the median slope was relatively high, for most of the oceanic data set ES-area relationships the best model was in fact the intercept-only model. This could partly be because most of these data sets have relatively few data points (a characteristic of many oceanic archipelagos), reducing the power of the test and increasing the effect of noise in the data (e. g. due to anthropogenic impacts). For habitat islands, there was a larger proportion of cases where the intercept-only model provided the best fit. This likely reflects the fact that they are often relatively noisy systems  (Matthews, 2021)  and can vary substantially in terms of various properties.
Eye Tracking The eye-tracker used was the Pupil Labs' Core model, which offers binocular tracking at a 200 Hz framerate. This model was chosen for its performance (0. 6 • angular accuracy), light weight (~23 g), affordability, and above all its open source software that allows for controlling the eye tracker through simple Python instructions. Basically, in video-based eye trackers such as the Pupil Labs', one or multiple camera(s) take an image of the eyes, which are often illuminated by infrared light sources. Gaze direction is deducted from analysis of pupil and corneal reflections positions  [15].
Basically, the Pupil Labs returns the x,y in the world image frame in normalized coordinates. Since the angular siz is known, calculation of the angular gaze position is straightforward. The e then calibrated using Pupil Labs software. The good agreement between and fixation target was assessed during this calibration. This approach can, to a bias of a couple of degrees, but this will have a limited impact, especia of a wide-field camera. The small size and the weight of the components make it possible to consider the integration of two motorizations, one for the camera, the other for the pointer. However, since the aim of our study was to demonstrate a proof of concept, we chose, for simplicity, to place both the camera and laser pointer on the same motorization.
System Integration The headband system integrating the various components was designed using the SolidWorks software to be manufactured by additive fabrication. The advantage of this solution is to allow rapid prototyping in order to be able to adapt to the expectations of end users (e. g. , surgeons). The specific headband layout and the different integrated elements are represented in Figures  2  and 3. The headband weights 111 g and the material used is Phrozen Water Washable Rapid Black Resin. A specific design was chosen to place the cameras near the eyes for a "first-person" viewpoint and to allow users to wear spectacles or binoculars. Illustration of the headband on a face. The headband was designed so that it could be used by spectacle wearers. We can distinguish the two cameras of the eye tracking module which are under the eyes, slightly in front of the glasses.
System Operation The camera output is both recorded locally and transmitted wirelessly. The control and communication channels of the various modules making up the system are depicted in Figure  4. In this case, the eye tracker is driven by software installed on a laptop computer, connected to it by a USB-C cable for the transfer of the scenes captured by the two cameras before real time analysis. A Python program retrieves the eye tracker data, calculates the correct angular command (pan and tilt pulse-width modulation (PWM) values) for the servos and sends it via Wi-Fi channel to the ESP32 that controls the servos. At the same time, the ESP32 retrieves the video from the camera and connects to an existing local  Illustration of the headband on a face. The headband was designed so that it could be used by spectacle wearers.
System Operation The camera output is both recorded locally and transmitted wirelessly. The contro and communication channels of the various modules making up the system are depicted in Figure  4. In this case, the eye tracker is driven by software installed on a laptop com puter, connected to it by a USB-C cable for the transfer of the scenes captured by the two cameras before real time analysis. A Python program retrieves the eye tracker data, calcu lates the correct angular command (pan and tilt pulse-width modulation (PWM) values for the servos and sends it via Wi-Fi channel to the ESP32 that controls the servos. At th same time, the ESP32 retrieves the video from the camera and connects to an existing loca Figure  3. Illustration of the headband on a face. The headband was designed so that it could be used by spectacle wearers.
System Operation The camera output is both recorded locally and transmitted wirelessly. The control and communication channels of the various modules making up the system are depicted in Figure  4. In this case, the eye tracker is driven by software installed on a laptop computer, connected to it by a USB-C cable for the transfer of the scenes captured by the two cameras before real time analysis. A Python program retrieves the eye tracker data, calculates the correct angular command (pan and tilt pulse-width modulation (PWM) values) for the servos and sends it via Wi-Fi channel to the ESP32 that controls the servos. At the same time, the ESP32 retrieves the video from the camera and connects to an existing local Wi-Fi network from which it gets an IP address. Using this information, it also sets up a server that another Python script can use to download and display the video.
Sensors 2023, 23, x FOR PEER REVIEW 6 of 11 Wi-Fi network from which it gets an IP address. Using this information, it also sets up a server that another Python script can use to download and display the video. In the next version, we shall integrate a fully embedded system for which these operations are entrusted to an autonomous micro-computer (e. g. , Raspberry Pi), which could also manage the servo-motors and the piloting of the laser-pointing sources.
Integration The different elements presented in Section 2 were successfully housed into a specifically designed ergonomic headband that overall weighs 396 g (incl. eye tracker, batteries, etc. ). The device worn by a human operator is depicted in Figure  5. Despite its slight lack of flexibility regarding the possible on-demand switching of sources, the laser diode option was chosen over the fibered laser because the small volume available for integration imposes on the fiber strong radii of curvature, which weakens it. Wi-Fi network from which it gets an IP address. Using this information, it also sets up a server that another Python script can use to download and display the video. In the next version, we shall integrate a fully embedded system for which these operations are entrusted to an autonomous micro-computer (e.
Pointing Precision The laser-pointing module was used to test the precision of the angular control allowed by the pan/tilt motorization. The system was set on an optical test bench and pointed at a piece of graph paper 85 cm away, as depicted in Figure  6. The first angular actuator tested was the one using the nano D1302 servo-motor. be changed on this type of miniature component. Moving to the HS-40, the tracking pre-cision was improved to 0. 15 degrees. The angular repeatability was, however, limited to 0. 35°, again due to the non-zero value of the servo deadband (4 μs), resulting in a maximum spot positioning error of 5 mm at 80 cm. Note that the impact of this value on the overall accuracy of the system is minimal, as the Pupil Labs eye-tracker accuracy is 0. 6°. Examples of spatial light distribution patterns obtained with different DOEs are also depicted in Figure  7 , in the projection area located 80 cm from the headband.
Note that the impact of this value on the overall accuracy of the system is minimal, as the Pupil Labs eye-tracker accuracy is 0. 6°. Examples of spatial light distribution patterns obtained with different DOEs are also depicted in Figure  7 , in the projection area located 80 cm from the headband. When tested with the eye tracker, the latency with which the motorized camera followed the gaze direction was assessed by recording a video in which the subject wearing the device directed his gaze at different targets while pointing at them. Despite the high servo-motor speed, the latency was approximately 0. 5 s due to data processing and data transmission. A simple video illustrating the use of the device for remote assistance is available as Supplementary Material.
As well as being dependent on the angular performance of the optical beam-steering module, our system is mainly limited by two issues: the latency with which the camera follows the gaze direction and the quality of the live streaming. The ESP32 has difficulty managing the different information streams simultaneously. As a result, the system cannot effectively stream high-resolution images (i. e. , 1080p) and the motorized camera movement can be delayed by half a second. These two points are likely to limit the interest of the eye tracker to control the camera or pointer. Embedding some computing power into the headband (e. g. , the Pupil Labs software can run on a Raspberry Pi) would allow for a fully wearable device but would not significantly reduce the ESP load. For instance, a Raspberry 4 (ARM quad-core, 1.
We present CertiCAN, a tool produced using the Coq proof assistant for the formal certification of CAN analysis results. Result certification is a process that is lightweight and flexible compared to tool certification. Indeed, the certification of an industrial analyzer needs access to the source code, requires the proof of many optimizations or implementation tricks and new proof effort at each software update. In contrast, CertiCAN only relies on the result provided by such a tool and remains independent of the tool itself or its updates. Furthermore, it is usually more time efficient to check a result than to produce it. All these reasons make CertiCAN a practical choice for industrial purposes. CertiCAN is based on the certification and combined use of two well-known CAN analysis techniques completed with additional optimizations.
Résumé : Nous présentons CertiCAN, un outil produit à l'aide de l'assistant de preuves Coq et permettant de certifier les résultats d'analyses de bus CAN. La certification de résultats d'analyses est un procédé moins coûteux et plus flexible que la certification des analyses ellesmêmes. En effet, la certification d'un analyseur industriel demande l'accès au code source, la preuve d'un code souvent complexe avec de nombreuses optimisations ou astuces de mise en oeuvre sans compter le besoin de nouvelles preuves à chaque mise à jour. CertiCAN, de son côté, n'a besoin que du résultat de l'analyse ; il reste indépendant de l'analyseur, de son implantation et de ses mises à jour. De plus, il est toujours plus efficace de vérifier un résultat que de le produire. Ces caractéristiques font de CertiCAN un choix pragmatique pour une utilisation industrielle.
Acknowledgment This work has been partially supported by the  LabEx PERSYVAL-Lab  (grant  ANR-11-LABX-0025-01 ) through the  CASERM  project and the  French national research organization ANR  (grants  ANR-15-CE25-0008  and  ANR-17-CE25-0016 ) through the  VOCAL  and  RT-PROOFS  projects. We would like to thank RealTime-at-Work for granting us an academic license and  Jiajie Wang  for providing an XML parser.
Data generated from sources such as wearable sensors, medical imaging, personal health records, and public health organizations have resulted in a massive information increase in the medical sciences over the last decade. Advances in computational hardware, such as cloud computing, graphical processing units (GPUs), Field-programmable gate arrays (FPGAs) and tensor processing units (TPUs), provide the means to utilize these data. Consequently, an array of sophisticated Artificial Intelligence (AI) techniques have been devised to extract valuable insights from the extensive datasets in the healthcare industry. Here, we present an overview of recent progress in AI and biosensors in medical and life sciences. We discuss the role of machine learning in medical imaging, precision medicine, and biosensors for the Internet of Things (IoT).