dataesr/scientific-paragraphs-categorization huggingface dataset:
• Aerial videos in the VIRAT dataset were manually selected (for smooth camera motion and good weather conditions) from rushes of a total amount of 4 h in outdoor areas with broad coverage of realistic scenarios for real-world surveillance. Content included "single person", "person and vehicle", and "person and facility" events, with changes in viewpoints, illumination, and visibility. The dataset came with annotations of moving object tracks and event examples in sequences. The main advantage of VIRAT videos was its perfect fit for military applications. It covered fundamental environment contexts (events), conditions (rather poor quality and weather condition impairments), and imagery (RGB and IR). We decided to keep the original resolution of videos (720×480) to prevent the introduction of unrelated artifacts.
• The 70 videos (RGB, 1280×720 and 30 fps) from DTB70 dataset were manually annotated with bounding boxes for tracked objects. Sequences were shot with a DJI Phantom 2 Vision+ drone or were collected from YouTube to add diversity in environments and target types (mostly humans, animals, and rigid objects). There was also a variety of camera movements (both translation and rotation), short-and long-term occlusions, and target deformability. Table  1  reports for each database the number of sequences selected, their native resolution, duration and frame number. Table  2  presents basic statistics of the database in terms of number of frames and duration.
Content Diversity Figure  1  presents the diversity of selected UAV sequences by illustrating the first frame of every content. Visual stimuli cover a variety of visual scenes in different environments (e. g. , public and military environments, roads, buildings, sports, port areas, etc. ) and different moving or fixed objects (e. g. , people, groups of people, cars, boats, bikes, motorbikes, etc. ). Selected videos were captured from various flight heights and different angles between the UAV and the ground (allowing or not the presence of sky during their observation). Also, three sequences, extracted from the VIRAT dataset, were captured by IR cameras. Additionally, we considered various video duration as the length of the video may possibly impact the behavior of observers due to fatigue, resulting in a lack of attention and more blinking artifacts  [10, 65].
Eye-Tracking Apparatus A specific setup was designed to capture eye-tracking information on video stimuli. It included a rendering monitor, an eye-tracking system, a control operating system, and a controlled laboratory test room. Figure  2  illustrates the experimental setup used during the collection of gaze information. We can observe the arrangement of all the systems described below. To run the experiment and collect gaze information, we used the EyeLink R 1000 Plus eye-tracking system (https://www. sr-research. com/eyelink-1000-plus/), in the head free-to-move remote mode, taking advantage of its embedded 25 mm camera lens. The eye tracker principle is to detect and record the IR illuminator reflection rays on the observer's pupil  [65]. This system enables the collection of highly precise gaze data at a temporal frequency of 1000 Hz and a spatial accuracy between the visual angle range of 0.
Moreover, the eye tracker camera was placed 43 cm away from the experimental display, and thus about 67 cm from participants. Based on this setting, there were 64 pixels per degree of visual angle in each dimension, and the display resolution was about 30×17 visual degrees. Regarding software, the MPC-HC video player (https://mpc-hc. org/), considered as one of the most lightweight open-source video players, rendered the experimental video stimuli. Also, we took advantage of the Eyelink toolbox  [68]  as it is part of the third version of Psychophysics Toolbox Psychtoolbox-3 (PTB-3) (http://psychtoolbox. org/) and added in-house communication processes (LS2N, University of Nantes) for sync between control and display systems. The control system consists of an additional computer, used by the experimenter to configure and control the eye-tracking system with an Ethernet connection.
Stimuli Presentation The random presentation of stimuli in their native resolution centered on the screen prevents ordering, resizing, and locating biases. Knowing that the monitor resolution is higher than that of selected sequences, video stimuli were padded with mid-grey. Additionally, to avoid possible biases in gaze allocation, a 2-second sequence of mid-gray frames was presented before playing a test sequence. Please note that the amount of original information contained in a degree of visual angle was not the same for VIRAT sequences than for other database content, as specified in Table  1. Before starting the experiment, a training session was organized to get the subject familiar with the experiment design. It included a calibration procedure and its validation followed by the visualization of one video.
This UAV video was the sequence car4 from the DTB70 dataset. To avoid any memory bias, this sequence was not included in test stimuli. Once subjects completed the training session, they could ask questions to experimenters before taking part into test sessions. Regarding test sessions, they started with calibration and its validation. Then followed the visualization of nine videos during which subjects did or did not perform a task. To ensure the optimal quality of the collected gaze data, each participant took part in five test sessions. Splitting the experiment into sessions decreased the tiredness and lack of attention in observers. Also, this design enabled frequent calibration so that recordings did not suffer from the decrease of accuracy in gaze recordings with time  [65]. With regards to calibration, the eye-tracking system was calibrated for each participant, following a typical 13 fixed-point detection procedure  [65].
EyeTrackUAV2 in Brief We have created a dataset containing binocular gaze information collected during two viewing conditions (free viewing and task) over 43 UAV videos (30 fps, 1280×720 and 720×480-42,241 frames, 1,408 s) observed by 30 participants per condition, leading to 1,239,157 fixations in free-viewing and 1,269,433 in task-viewing for dominant eyes positions. Notably, selected UAV videos show diversity in rendered environments, movement and size of objects, aircraft flight heights and angles to the ground, duration, size, and quality. This dataset overcomes the limitations of EyeTrackUAV1 in that it enables investigations of salience in more test sequences, on larger population samples, and for both free-viewing and task-based attention. Additionally, and even though they are still too few, three IR videos are part of visual stimuli.
Analyses In this section, we characterize the proposed EyeTrackUAV2 database. On one hand, we compare salience between six ground truth generation scenarios. This study can be beneficial to the community to know what is the potential error made when selecting a specific ground truth scenario over another. On the other hand, UAV videos induce new visual experiences. Consequently, observers exhibit different behaviors towards this type of stimuli. Therefore, we investigate whether the center bias, one of the main viewing tendencies  [28] , still applies to EyeTrackUAV2 content.
We further explore the other metrics, namely CC, SIM, and IG, through the Tukey's multiple comparison post-hoc test  [85]  (α = 0. 05). Results are presented in Figure  6. On the charts, we can see where stands group means and comparison intervals for each scenario over the entire dataset. Scenarios having non-overlapping intervals are statistically different. In details, our results show three statistically distinct categories for all metrics.
Qualitative Evaluation of Biases in UAV Videos We evaluated the viewing tendencies of observers thanks to the average gaze density map, computed over the entire sequence. It was representative of the average position of gaze throughout the video. It was used to observe potential overall biases, as it could be the case with the center bias. Figures  7  and 8  show the average gaze density map for all sequences of EyeTrackUAV2 dataset, generated from D scenario, for both free-viewing and task-viewing conditions. Several observations can be made. Content-dependent center bias. We verify here the content-dependence of the center bias in UAV videos. For both attention conditions, the scene environment and movements exacerbates or not UAV biases. For instance, in sequences car 2-9 (fourth row), the aircraft is following cars on a road.
Associated average gaze density maps display the shape of the road and its direction, i. e. , vertical route for all and roundabout for car7. Car 14 (third row), a semantically similar content except that it displays only one object on the road with a constant reframing (camera movement) which keeps the car at the same location, presents an average gaze density map centered on the tracked object. Original database-specific center bias. We can observe that a center bias was present in VIRAT sequences, while videos from other datasets, namely UAV123 and DTB70, did not present this bias systematically. The original resolution of content and the experimental setup are possibly the sources of this result. Indeed, the proportion of content seen at once was not the same for all sequences: 1. 19% of a VIRAT content was seen per degree of visual angle, whereas it was 0.
44% for the two other original databases. VIRAT gaze density maps were thus smoother, which resulted in higher chances to present a center bias. To verify this assumption based on qualitative assessment, we computed the overall gaze density maps for sequences coming from each original dataset, namely DTB70, UAV123, and VIRAT. These maps are shown in Figure  9. VIRAT gaze density maps are much more concentrated and centered. This corroborates that biases can be original-database-specific. Task-related gaze density maps seemed more spread out. Task-based gaze density maps covered more content when compared to free-viewing condition for most sequences (e. g. , in about 58% of videos such as basketball, car11, car2, and wakeboard). This behavior is also illustrated in Figure  9. We correlate this response with the object detection task.
Conclusions UAV imaging modifies the perceptual clues of typical scenes due to its bird point of view, the presence of camera movements and the high distance and angle to the scene. For instance, low-level visual features, and size of objects change and depth information is flattened or disappears (e. g. , presence of sky). To understand observers' behaviors toward these new features, especially in terms of visual attention and deployment, there is a need for large-scale eye-tracking databases for saliency in UAV videos. This dataset is also a key factor in the field of computational models of visual attention, in which large scale datasets are required to train the latest generation of deep-based models. This need is even stronger with the fast expansion of applications related to UAV, for leisure and professional civilian activities and a wide range of military services.
Combining UAV imagery with one of the most dynamic research fields in vision, namely salience, is highly promising, especially for videos that are gaining more and more attention these last years. This work addresses the need for such a dedicated dataset. An experimental process has been designed in order to build a new dataset, EyeTrackUAV2. Gaze data were collected during the observation of UAV videos under controlled laboratory conditions for both free viewing and object-detection surveillance task conditions. Gaze positions have been collected on 30 participants for each attention condition, on 43 UAV videos in 30 fps, 1280×720 or 720×480, consisting of 42,241 frames and 1408 s. Overall, 1,239,157 fixations in free-viewing and 1,269,433 in task-viewing were extracted from the dominant eye positions.
Test stimuli were carefully selected from three original datasets, i. e. , UAV123, VIRAT, and DTB70, to be representative as much as possible of the UAV ecosystem. Accordingly, they present variations in terms of environments, camera movement, size of objects, aircraft flight heights and angles to the ground, video duration, resolution, and quality. Also, three sequences were recorded in infra-red. The collected gaze data were analyzed and transformed into fixation and saccade eye movements using an I-DT-based identification algorithm. Moreover, the eye-tracking system high frequency of acquisition enabled the production of gaze density maps for each experimental frame of the examined video stimuli directly from raw data. The dataset is publicly available and includes, for instance, raw binocular eye positions, fixation, and gaze density maps generated from the dominant eye and both eyes information.
We further characterized the dataset considering two different aspects. On one hand, six scenarios, namely binocular, both eyes, dominant eye, non-dominant eye, left, and right can be envisioned to generate gaze density maps. We wondered whether a scenario should be favored over another or not. Comparisons of scenarios have been conducted on six typical saliency metrics for gaze density maps. Overall, all scenarios are pretty similar: over the six evaluated metrics, three could not make a distinction between scenarios. The three last metrics present mild but statistically significant differences. Accordingly, the information of both eyes may be favored to study saliency. If not possible, choosing information from the dominant eye is encouraged. This advice is not a strict recommendation. On the other hand, we notice that conventional biases in saliency do not necessarily apply to UAV content.
Indeed, the center bias is not systematic in UAV sequences. This bias is content-dependent as well as and task-condition-dependent. We observed new prior patterns that must be examined in the future. In conclusion, the EyeTrackUAV2 dataset enables in-depth studies of visual attention through the exploration of new salience biases and prior patterns. It establishes in addition a solid basis on which dynamic salience for UAV imaging can build upon, in particular for the development of deep-learning saliency models.
Acknowledgments: The presented work is funded by the ongoing research project  ANR ASTRID DISSOCIE (Automated Detection of SaliencieS from Operators' Point of View and Intelligent Compression of DronE videos)  referenced as  ANR-17-ASTR-0009. Specifically, the LS2N team ran the experiment, created and made available the EyeTrackUAV2 dataset. The Univ Rennes team added binocular and both-eyed scenarios information to the dataset, conducted analyses, and reported it.
With the growing number of cores and fast network like Infiniband, one of the keys to performance improvement in MPI applications is the ability to overlap CPU-bound computation with network communications. While this can be done manually, this is often a complex and error prone procedure. We propose an approach that allows MPI blocking communication to act as nonblocking communication until data are needed, increasing the potential for communication and computation overlapping. Our approach, COMMMAMA, uses a separate communication thread to which communications are offloaded and a memory protection mechanism to track memory accesses in communication buffers. This guarantees both progress for these communications and the largest window during which communication and computation can be processed in parallel.
I. INTRODUCTION Nowadays, MPI remains one of the standard runtimes for distributed computing. While the exact proportion varies between applications, in a lot of these, completion time is shared between computation (CPU intensive) and communications between nodes (network intensive). These two components can mostly operate independently, but designing application using the MPI runtime to take advantage of the potential parallelism is hard. Though nonblocking communications exist in the MPI runtime, these primitives need a corresponding waiting primitive before the communication buffer can be used in order for the application to be semantically correct. The position of these waiting primitives in the application code is the limit before which overlapping computation and communication can happen and is currently at the programmer initiative.
B. Blocking and nonblocking are only part of the answer Currently, overlapping communication and computation is mostly done using nonblocking communications. The code presented in Listing 1 demonstrates a basic usage of nonblocking communication that would allow overlapping to be done. However, communication can only run in parallel with computation if there is some kind of progress engine in the runtime implementation. While some approaches to communication and computation overlapping such as  [2]  propose methods to ensure progress, to the best of our knowledge, there is no production ready progress engine included in wellknown MPI implementations for general use cases. Moreover, in Listing 1 MPI_Wait is called before the write_function because this function does write in the buffer but only after a long independent or read only computation which could also have been overlapped with communication.
III. OUR OVERLAPPING INFRASTRUCTURE: COMMMAMA In this section, we explain how standard calls performed by the application are transformed and treated as we detail the different parts composing COMMMAMA, our system. As presented in Figure  1 , we consider a software stack composed of three major elements, the application, COMMMAMA and the MPI runtime. COMMMAMA is composed of three different layers. The interception layer which intercepts and transforms standard blocking primitives into multiple calls to other layers. The protection layer which manages memory protection for communication buffers and forces the application to wait when an otherwise semantically incorrect memory access occurs. The progress layer which is responsible for communications and offloads communications to a standalone thread.
A. The interception layer We chose to avoid modifying directly any MPI runtime to be compatible with any implementation and to avoid maintaining a forked version. Therefore, COMMMAMA uses a shared library which replaces needed MPI functions versions. Our interception library first stores all the existing MPI library functions into dedicated function pointers and then declares functions with their original names. This library is then loaded before the real MPI library using LD PRELOAD, effectively renaming MPI library symbols and replacing them with ours. To ease the use of this shared library, we provide an executable managing this configuration and using the interception library becomes as simple as mpi_interceptor mpirun  [args]. On top of being easy to use, this method has another upside which is the possibility to use it on already compiled or written software, without any need to modify the code to obtain its full efficiency.
V. RELATED WORK The field of asynchronous progress for MPI primitives has been extensively studied. Some approaches use compile time techniques to evaluate the best position for nonblocking primitives and specifically MPI_Wait calls. This is the case of  [5]  which uses source to source translation to remodel code using compiler pragmas to divide the code into three types of regions: send, receive and compute. While this allows some communication to be overlapped with computation by running communication and compute region in parallel on different cores, the compute region still need to start with a waiting primitive (in most cases MPI_Waitall) which in turns seems too coarse grained to attain a satisfying overlap ratio in complex cases. L. Fishgold et al. [6]  propose a finer grained, automated technique which detects patterns presenting an opportunity for tranformation by parsing the source code and then modify it.
This approach focuses on finding compute loops filling parts of communication buffers at each iteration and modifying them to send these parts when ready. While this technique covers a large portion of MPI applications, especially High Performance Computing related ones, our approach offers a broader field of application. Moreover, they focus on MPI_Alltoall collective while our work does not target a specific communication primitive. A similar approach from Jichi Guo et al. [7]  uses static code analysis to replace blocking communications by nonblocking communications in loop based computations thus overlapping communication step i with computation step i + 1. While this approach is close to our proposal, it still uses compile time analysis, compute loops and does the communication progression using MPI_Test primitives inserted in the loop body.
This is similar to  [10]  which uses clang to perform modifications. The method presented in  [11]  uses code instrumentation and offline analysis to determine code fragments where overlapping capabilities are not fully exploited. After the instrumented run has terminated, their tool uses the trace files to perform optimizations. Other approaches use runtime techniques. This is the case of  [12]  and  [13]  that use threads to increase asynchronous progress by running communications in separate tasks. Karthikeyan Vaidyanathan et al. [2] , probably the closest approach to ours, delegates MPI nonblocking calls to a standalone thread. Our approach builds upon and extends this approach by using the memory protection mechanism to transform blocking MPI calls into nonblocking equivalents. While we agree on the potential gain provided by such a method, such a gain can only be obtained if waiting primitives are placed correctly.
Content analysis We generated a list of individual words from the interview transcripts using the Word-Stat text analysis module of QDA Miner  (Péladeau 2004 ), a process that resulted in a large number of words. In line with prior studies that have analyzed vocabularies, we then identified words that were frequent enough  (Jones and Livne-Tarandach 2008; Nag et al. 2007)  in the interview transcripts to meaningfully constitute the distinctive lexicon of a topic-design in our case. Following Merleau-Ponty's (2012) "Phenomenology of Perception", we found that a cutoff frequency of five occurrences of a word (in total across the 14 informants) offered a level of granularity and parsimony that generated a gestalt understating of the meaning of design as accorded by the informants. Using this cutoff, we generated an initial list of words for each informant.
Acknowledgments PD acknowledges support by the  Engineering and Physical Sciences Research Council (EP-SRC)  under grants no. EP/M006883/1  and  EP/P013651/1 , by the  Royal Society  and the  Wolfson Foundation  through a  Royal Society Wolfson Research Merit Award  no. WM130048  and by the  National Science Foundation (NSF)  under grant no. RNMS11-07444  ( KI-Net ). PD is on leave from  CNRS ,  Institut de Mathématiques de Toulouse, France. A. D. acknowledges the hospitality of  CEREMADE, Université Paris-Dauphine  and the  University of Sussex  where part of this research was carried out. A. D. was supported for this work by the  École Normale Supérieure de Rennes  through an  Erasmus+ grant. This work was initiated at the  École Normale Supérieure de Lyon  as part of a Master degree delivered to A.
D. A. F. acknowledges support from the  EFI  project  ANR-17-CE40-0030  and the  Kibord  project  ANR-13-BS01-0004  of the  French National Research Agency (ANR) , from the project  Défi S2C3 POSBIO of the interdisciplinary mission of CNRS , and the project SMS co-funded by  CNRS  and the  Royal Society.