dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and Future Work Processing large-scale spatio-temporal data is experiencing an increasing importance and is required by many fields among which is included precision agriculture. Analyzing these data, for instance, weather, soil humidity, etc. , allows for extracting more valuable, crucial and accurate information required to analyze environmental phenomena. The main requirement for spatial data-intensive applications is the processing time and scalability. Unfortunately, most of the existing methods are based on traditional approaches and are not appropriate for querying massive spatial data efficiently. In this paper, we addressed the problem of speeding up the spatio-temporal rasters data-based query consisting of selecting only disjoint raster subsequences of a fixed size, such that the average of the cells over these subsequences is less than a user defined threshold.
In recent years, several big raster spatio-temporal data processing infrastructures have been developed for information mining. For instance, XArray  [34] , which is an efficient tool for processing scientific datasets and widely used by the GIS community for processing raster spatial data. Fully integrated in DASK  [35] , it allows the processing of big raster data. Despite their simplicity and their efficiency, most of these solutions are based on high level languages, e. g. , Python, and provide high level pre-built functions which make them slower and without the high flexibility to implement very bespoke algorithms. In this paper, we are working on a very specific query which is not yet implemented in the existing raster data processing systems. Our goal is to propose a generic and very fast solution and this can be achieved only by using low level programming languages (e.
g. , C++). Moreover, their flexibility allows us to include a heuristic based on sorting in order to save computations, remembering that our problem belongs to massive data computations which are very suitable for a GPU parallel, which justifies our solution. Thus, in our work, first we have shown that we can improve the processing of the said query by using the power of recent GPU devices. Furthermore, using parallel primitives based on CUDA allowed us to further optimize our implementation and reduce the coding complexity. Second, we have designed and implemented a new method based on a sort process to avoid useless computations and hence further accelerated the processing time of our query. The proposed approaches have been tested on the real dataset provided by the INRAE experiment farm using three measures: temperature, humidity and watermark 2, that are essential for agriculture.
The experimental results on temperature show that our method without sorting based on the GPU performs better than the CPU over all the datasets; however, the methods (CPU and GPU) based on sorting show poor results (The time of execution is worse compared to the methods without sorting). As cited above, this is due to the nature of the temperature data (mean of rasters standard deviation = 2. 73). Concerning the air humidity and watermark 2 measures, the experimental results have shown that GPU-based methods are always better than the CPU methods over all the datasets. Furthermore, our methods using the sorting step enhance the results, especially, for the parallel version. Thus, a significant speed up is obtained  (4, 8)  for the dataset 4 for watermark 2 and humidity. As mentioned above, our optimized method based on sorting is sensitive to data distribution; it works only in the case of high variations in data (e.
g. , in our case humidity and watermark 2) and it does not work in the case of data with low variations such as temperature. We have to highlight that our method based on sorting is not limited to spatial data but it can be used in other applications which use the same form and characteristics of data. Furthermore, the query can be extended to other operations besides the average, for example, min. or max. and the user can also change the constraint. As future work, we would like to predict mathematically the optimal size of the time window and the size of the tiles, as this will help to avoid the empirical method which is a time consuming and imprecise method for choosing the best values for tile and time window sizes. Furthermore, we want to provide more precise statical parameters for the dataset description in order to provide a decision-making support for either using the sorting-based method or remaining content with the GPU-based method without sorting.
Additionally, the investigation of the use of our methods (for instance, the rejection process based on sorting) on other fields will be very useful in order to study the behavior of our methods on other types of data from other fields and applications. The proposed GPGPUbased sorting method to improve raster selection performance is currently not implemented in traditional libraries such as spatial XArray  [34]. The proposed improvement is generic and can be integrated in such libraries. A future work can include implementing the proposed method (with sorting) in this manuscript as an XArray module and compared with an XArray-based method (without sorting). Finally, we would like to expand the use of the power of the GPU for more massive computations and complex queries, and running our methods on different heterogeneous computing solutions such as OpenCL, which will be very useful for making a complete comparison between all the solutions.
Acknowledgments: We acknowledge the support received from the  Agence Nationale de la Recherche  of the  French government through the program  " Investissements d'Avenir "  16-IDEX-0001 CAP 20-25  on the general topic of this paper. Also, we would like to thank all the members of  LIMOS (UCA)  and the  COPAIN team (INRAE)  involved in the deployment of the LiveNode network in INRAE Montoldre and in the collection and documentation of data.
Funding: This work was funded by grants from the  French program  " investissement d'Avenir " managed by the  Agence Nationale de la Recherche of the French government (ANR) , the  European Commission (Auvergne FEDER  funds) and"  Région Auvergne" in the framework of LabEx IMobS 3  ( ANR-10-LABX-16-01 ).
Discussion AZP2006 is a human safe small synthetic polycationic amphiphilic molecule (free base MW = 428. 67 g/mol) orally administered and currently under clinical development (Phase 2a clinical trial # NCT04008355). The molecule was proven to have a good absorption, a large exposure and a long half-life (> 15 days in human) with a fast BBB crossing and brain penetration in mice (15 min after an oral administration, AZP2006 was found in different brain areas in particular in the hippocampus). The present study reports for the first time the neuroprotective effects of AZP2006 against the injuries caused by Aβ oligomers and neurodegeneration linked to aging. Neuroprotective activity of AZP2006 was firstly investigated in an in vitro coculture of neurons and microglia. In this model, application of Aβ 1-42 displayed a significant reduction of neuron number, dendritic network and synapses parallel to a severe induction of microglia activity and interleukin release.
These antibodies were labeled with Alexa Fluor secondary antibodies (Thermo Fisher Scientific) at the dilution of 1/400 in PBS containing 1% FCS, 0. 1% saponin, for 1 h at room temperature. For each condition, between 30 and 40 pictures per well were taken using ImageXpress (Molecular Devices, San Jose, USA) with 20× magnification. All images were generated using the same acquisition parameters and analyses were directly and automatically performed by using Custom Module Editor (Molecular Devices) (Supplementary Fig. 7 ).
115 pico device at 25 °C (NanoTemper Technologies, Munich, Germany). Data was analyzed using NT. Analysis Analysis software (version 1. 5. 41, NanoTemper Technologies) at the standard MST-on time of 10 s (respectively Thermophoresis + TJump). No aggregation effects or unspecific adhesion of the protein to the glass surfaces of the capillaries were detected. Two technical runs were performed. Data did not possess amplitudes > 5 units combined with Signal to Noise levels > 5 units and was hence defined as non-binder. PSAP protein binding experiments were carried out with 3 nM NT647-labeled PSAP in binding buffer with a range of concentrations of AZP2006 (0. 9155-30,000 nM) or PGRN (0. 0604-1978 nM). Effect on hTLR-9 receptor. Samples and controls were tested in triplicate on recombinant HEK-293 cell line (InvivoGene; HEK-Blue™ hTLR9 cells).
From D08 to D010, all mice performed novel object recognition test to measure recognition memory. In this test, the activity was analyzed using the nose tracking protocol, in terms of number of contacts with objects (frequency) and duration of contacts (time). Novel object recognition (NOR) procedure was recorded by Videotrack software (Viewpoint) and filmed. The task procedure consists of three sessions. Session 1: mice were placed individually in a squared open-field (50 cm × 50 cm × 50 cm high) made of white plexiglass and a floor equipped with infrared light emitting diodes. Mice were habituated to the open-field during a 10-min duration session and their locomotor activity captured through an IR-sensitive camera and analyzed using the Ethovision® software (Noldus). The activity was analyzed in terms of locomotor speed (cm/s), percentage of presence in the central area (25 × 25 cm) defined by the software and stereotypes (rearing and grooming).
The immunofluorescent slides were digitized at 20 × magnification (resolution of 0. 6484 µm/pxl) using a Path-Scan® scanner (Excilone, Élancourt, France) in immunofluorescence. The 3 sections of each slide were entirely scanned into 3 separated regions of interest (ROI). The imaging and quantification were performed with the Halo® Imaging Analysis software (IndicaLabs; Corrales, NM, USA). Halo® measures and reports individual cell data maintaining an interactive link between cell metrics and cell imagery. The number of NeuN positive neurons, the Iba1 positive microglia and its body-cell area in a section of the cortex and in the whole hippocampus were automatically counted with the HALO software (Supplementary Fig. 6A ). Halo® platform is designed to be used through modules which are pre-built algorithms designed for a particular image analysis.
Lipid peroxidation measurement (modified FOX assay). For LPO measurement hippocampi from each group were used as described by  75. After thawing, tissues were homogenized in cold methanol (1/10 w/v), centrifuged at 1,000 g during 5 min and the supernatant placed in Eppendorf tube. The reaction volume of each homogenate was added to FeSO 4 1 mM, H 2 SO 4 0. 25 M, xylenol orange 1 mM and incubated for 30 min at room temperature. After reading the absorbance at 580 nm (A5801), 10 µl of cumene hydroperoxyde (CHP) 1 mM were added to the sample and incubated for 30 min at room temperature to determine the maximal oxidation level. The absorbance was measured at 580 nm (A5802). The level of lipid peroxidation was determined as CHP equivalents according to: CHPE = A5801/A5802 x [CHP (nmol)] and expressed as CHP equivalents per wet weight of tissue and as percentage of control group data (Vehicle-treated mice).
Data analysis and statistics. Results are expressed as mean ± SEM (standard error of the mean). In vitro studies statistical analyses on the different conditions were performed using ANOVA followed by Dunnett's or Fisher's t test. In vivo statistical analyses were performed on the different conditions using two-way ANOVA (F value), followed by the Dunnett's post-hoc multiple comparison test. Passive avoidance latencies do not follow a Gaussian distribution, since upper cut-off times are set. They were therefore analyzed using a non-parametric method the Kruskal-Wallis H test (two-way ANOVA on ranks), followed by a Dunn's multiple comparison test. p < 0. 05 was considered as statistically significant. All test were performed by using GraphPad Prism software version 9. 0. 2.
Hand movements and handles were recorded to understand the manipulation of objects, potentially facilitating research in Artificial Intelligence (AI), which can be used to train virtual AI agents for robot gripping tasks. Developing a sensory glove can be used in medical training to improve the user's ability. One of the most significant features is VR haptic feedback to stimulate muscles and nerves, allowing users to train through muscle memory. Medical students can learn and comprehend essential techniques faster than traditional learning methods  [25]. There were studies compared the use of Vive Controller, Leap Motion, and data glove. Using controllers has average ease of use and greater satisfaction than other devices  [26]. However, in terms of usefulness, they were about the same. The research also suggested that having haptic feedback increases user satisfaction.
At the beginning of the experiment, all participants of both groups studied endotracheal intubation from a video to understand the basic training procedures for approximately 10 min. Then, each group was tested with the same procedures in VR intubation training but using different interactions with controllers and hand tracking. After finishing all procedures, interaction time measurements were automatically recorded to a database. All participants had to complete the evaluations by answering the SUS and USEQ questionnaires. Finally, the interviewing process was conducted individually by our experiment crew and took about 10 min per student. It consisted of 19 questions concerning emotional, instrumental, and motivational experiences. The questions asked about feelings and opinions on the experiment as well as suggestions for development of VR medical trainings in general.
Our use of a ceiling-vision camera aims to limit such obstructions. Approaches using ceiling-mounted cameras or artificial landmarks require the environment to be modified, and the use of ceiling lights is only feasible in environments where the lights are constantly switched on. The use of manually-defined high-level features makes it harder to adopt the algorithm for various types of ceilings composed of different types of panels or lamps. There are a few fundamental differences between the applications of our approach and the applications considered for camera relocalization  (Kendall et al. , 2015; Kendall et al. , 2017; Clark et al. , 2017; Brahmbatt et al. , 2018; Kendall et al. , 2016). These works obtained high 6-DoF pose accuracy (0. 18-0. 48m, 0. 11-0. 17rad) using the 7-Scenes dataset  (Zhang et al.
, 2016)  which was acquired in small indoor environments (2-12m², 1-1. 5m height). They also evaluate their methods in larger outdoor environments (875-50000m²), but the accuracy obtained is insufficient for indoor localization of a mobile robot (1. 46-3. 67m, 0. 13-0. 25rad). In contrast, we aim to provide accurate localization at any point of rooms of around 100m². Furthermore, images of ceilings generally contain less visual features than images from either outdoor or 7-scenes datasets. Finally, these works do not evaluate the performance of their networks on more constrained embedded hardware typical for indoor mobile robots. A more recent work  (Xiao et al. 2019 ) explores how to ignore dynamic objects when performing visual SLAM using deep CNNs.
Motivation CNNs learn which features are relevant to a given dataset and task, so applying our method to a new environment requires only a new dataset and network training, as opposed to manual tweaking of predefined features. Inspired by works in the field of place recognition and 6-DOF camera relocalization, we use CNNs to estimate the 3-DoF pose of the robot from an image. CNNs for complex tasks have a high memory and computational cost. We split the task into the estimation of the 2-DoF position, and estimation of the orientation, each of which is treated as a regression problem solved by a separate CNN. This was more accurate than using a single CNN with our architecture to regress the full 3-DoF pose. Using only a ceiling image as input allows our approach to provide immediate global localization, without the need for an external initial pose estimation as input or environment exploration phase.
CNN Architectures In order to design our CNN, we experimented with various network architectures. We decided to use the same base CNN architecture to construct our position and orientation CNNs, with the only difference being the number of outputs. We base our final architecture on only a sub-part of the GoogLeNet architecture  (Szegedy et al. , 2015)  because our task is different, and we target a lower computational cost. Starting with the full GoogLeNet architecture, the process to build our CNN can be summarized as follows: (1) we took the first part of GoogLeNet (from Input to Softmax0 layer in Fig. 3  of  (Szegedy et al. , 2015) ); (2) we adapted existing layers to suit our dataset and task by modifying the input layer to suit our images, and by adapting the output layer for regression; (3) we added Batch Normalization layers after the first two Maxpool layers; (4) we adjusted network hyperparameters.
We adapted the input layer to our data by changing the input shape to 160x120x1(width, height, channels) in order to suit our images, which are smaller than those used in GoogLeNet, and use only one color channel. Consequently, the sizes of our feature maps are different, and can be seen in Fig. 3. We adapted the output layer by replacing the softmax0 with a dense layer with one or two units for orientation or position regression respectively. Batch normalization (Ioffe and Szegedy, 2015) consists in normalizing the inputs of a layer for each batch of images. This allows us to limit overfitting by improving regularization, which is especially needed when using small datasets such as ours. We introduced Batch normalization layers after each of the first two MaxPooling layers, as seen in Fig.
3. Hyperparameters are tuned to maximize a network's performance on a given task and dataset. We experimented with various parameters considering what worked best for our task, datasets, and hardware. We replaced the stochastic gradient descent optimizer with the more recent Amsgrad (learning rate=0. 001, beta1=0. 9, beta2=0. 999, epsilon=10⁻⁷, decay=0)  (Reddi et al. , 2018). We reduced the batch size for training from 256 to 16 to account for our dataset size, which is smaller than the one GoogLeNet was designed for. This also reduced the memory requirements when training the network, allowing us to target mid-range hardware. For the task of position estimation, we augmented the dataset by applying random rotations to the images. Given that our camera was centered on the robot, this was equivalent to capturing images from the same position at different orientations.
Dataset Construction The dataset required to train the CNNs is built by teleoperating the robot in the environment in which it will be deployed, while acquiring images of the ceiling along with the corresponding ground-truth poses. The images are pre-processed as described in section 3. 3. The quality of the CNN's predictions depends on the dataset, which is affected by the conditions in which the images are acquired, as well as the method by which the ground-truth pose is acquired. Artificial and natural lighting conditions were varied during data acquisition to improve the CNN's robustness. For artificial lighting, data was acquired with all lights on, or all lights off. The ground-truth pose is provided by an alternative existing localization method. We used laser-based Adaptive Monte-Carlo Localization (AMCL) (Fox et al, 1999) coupled with an existing map of the environment to provide ground-truth measurements.
We acquired data in a quasi-static environment in order to maximize the accuracy of the AMCL. We discarded samples for which the ground-truth localization had reported a high uncertainty, which we defined as samples where any one of the pose covariances were higher than a threshold: σ(x,x) > 0. 15m² or σ(y,y) > 0. 15m² or σ(θ,θ) > 0. 1rad². This does not eliminate the problem of the pose estimation drifting out of accuracy as the robot is teleoperated. To mitigate this effect, data acquisition was performed by combining data from several shorter acquisitions. Fig. 4  summarizes the data acquisition.
Environments and Datasets We considered two environments (denoted as A and B -see Fig. 5 ) with different ceiling features. Their areas were 83 m² and 72 m², and ceiling heights of 4m and 2. 5m for A and B respectively. A dataset was built for each environment by teleoperating the robot at a speed of 0. 5 m/s. Images were acquired at 5fps and associated with their ground truth pose. For environment A, 30105 samples were acquired (train, validation data split: 89. 7%, 10. 3%). For environment B, 27237 samples were acquired (train, validation split: 80. 5%, 19. 5%). For testing, we deployed the robot in three different scenarios: (1) an uncrowded environment; (2) an uncrowded environment with a new artificial lighting configuration; (3) an environment where several participants partially obstructed the camera's view.
In order to evaluate the performance of our approach, we teleoperated the robot while recording samples composed of the ceiling image, ground-truth pose, and our method's pose prediction. New samples were taken when the ground-truth pose changed by more than 0. 001m or 0. 001rad in order to discard moments when the robot was stationary. We present statistics on position and orientation errors for the different scenarios in Table  1 , computed using the testing datasets containing 400 to 600 samples, acquired over 320 to 609 seconds of operation.
Implementation Details The camera we used was a low-resolution (640x480 pixels) USB camera, equipped with a fisheye lens. It was mounted on top of the RobAIR, at a height of 120cm. The robot was equipped with an i5-6200U processor. The full pose estimation step, from acquiring the image to outputting a pose took 100ms to run on the robot's processor. When performing localization, the camera recorded at 5fps, hence the pose update rate was 5Hz. We implemented the localization module as a ROS node. CNNs were implemented in Python using Keras with Tensorflow backend. Training was performed on a mid-range laptop GPU (Nvidia GTX 960m, 2GB of VRAM) using CUDA. Networks were trained for 30 epochs (about 1 hour each network). The laser for groundtruth pose acquisition was a Hokuyo URG-04LX-UG01 rangefinder, mounted on the base of RobAIR.
Experimental Results and Analysis In Fig. 6  and Fig. 7 , we show the histograms of pose error values for scenario 1, environment B. The supplementary video contains a visualization of the pose predictions in our evaluation scenarios, as well as the graphs of errors over the whole duration of each scenario, which give an idea of how the pose prediction behaves over time. For both position and orientation, the general trend in error values varies according to which part of the environment the robot is in. Error values fluctuate continuously during the evaluation, with occasional large error spikes. In all scenarios, the error behaves in a similar fashion. Scenario 1 consisted in sampling the data from the testing datasets used to evaluate the CNNs. These datasets were acquired in environments with lighting conditions that the network had trained on (all lights on or all off).