dataesr/scientific-paragraphs-categorization huggingface dataset:
• The set of messages that process 2 expects to receive R= {comm A}. The set of messages that it will send is S= {comm B}. The above required property that the collection of sends {comm A, comm B} matches the collection of receives {comm A, comm B} is verified. However, this code will fall into a deadlock. Semantic equivalence between two parallel programs requires not only that collected sends match collected receives but also that there is no deadlock. If, conversely: Assumption 2. the resulting checkpointed code is deadlock free, then, the semantics of the checkpointed code is the same as that of its non-checkpointed version. To sum up, a checkpointing adjoint method adapted to MPI programs is correct if it respects these two assumptions: Assumption 1. The duplicated recvs in the checkpointed part will collect the same values as their original calls.
Correctness G ⃗ U G C G ⃗ D G D G C G U G ⃗ C ⃗ U ⃗ D ⃗ C U D C ⃗ ⃗ ⃗ C ⃗ ⃗ ⃗ Therefore, the complete graph on the bottom of figure 9 is acyclic. 5 Discussion And Further Work We studied checkpointing in the case of MPI parallel programs with point-to-point communications. We proved that any technique that adapts the checkpointing mechanism to MPI parallel programs and that respects some sufficient conditions, is a correct MPI checkpointing technique, in the sense that, the checkpointed code resulting from the application of this MPI checkpointing technique preserves the semantics of the non-checkpointed adjoint code. We introduced, a general MPI checkpointing technique that respects the sufficient conditions for any choice of the checkpointed part. This technique is based on logging the received messages , so that the duplicated communications need not take place.
Our aim in the EU funded JERICO project was to develop a flexible and scalable imaging platform that could be used in the widest possible set of ecological situations. Depending on research objectives, both image acquisition and analysis procedures may indeed differ. Up to now the attempts for automating image analysis procedures have consisted of the development of pieces of software specifically designed for a given objective. This led to the conception of a new software: AVIExplore. Its general architecture and its three constitutive modules: AVIExplore -Mobile, AVIExplore -Fixed and AVIExplore -ScriptEdit are presented. AVIExplore provides a unique environment for video analysis. Its main features include: (1) image selection tools allowing for the division of videos in homogeneous sections, (2) automatic extraction of targeted information, (3) solutions for long-term time-series as well as large scale image acquisition, (4) real time acquisition and in some cases real time analysis, (5) a large range of customized image-analysis possibilities through a script editor.
The flexibility of use of AVIExplore is illustrated and validated by three case studies: (1) Coral identification and mapping, (2) Identification and quantification of different types of behaviors in a mud shrimp, and (3) Quantification of filtering activity in a passive suspension-feeder. The accuracy of the software is measured comparing with visual assessment. It is: 90. 2 %, 82. 7%, and 98. 3% for the three case studies, respectively. Some of the advantages and current limitations of the software as well as some of its foreseen advancements are then briefly discussed.
In this context, we report here on the AVIExplore software that provides a unique flexible environment for automated video analysis. AVIExplore proposes different interfaces to analyze videos originating from imaging devices set on mobile and/or fixed platforms. The principal features of AVIEplore are: (1) to provide image selection tools allowing for the division of videos in homogeneous subsections, (2) to allow for the automatic extraction of targeted information, (3) to propose solutions for long-term time-series as well as large scale image acquisition, (4) to allow for real time acquisition and in some cases real time analysis, (5) to provide a large range of customized image-analysis possibilities through a script editor. This paper presents the principles and the structure of the AVIExplore software.
Generalities and common features AVIExplore is a software that allows acquisition and analysis of standardised AVI videos. It presents a graphical user interface (Fig. 1 ) that allows access to three different modules: (1) AVIExplore -Mobile. This module allows the extraction of information from videos taken with moving sensors and cameras. It has two working modes: real time and recorded video. (2) AVIExplore -Fixed. This module allows for the survey of activity on surfaces by using videos taken with a fixed camera. It has two working modes: real time and recorded video. (3) ScriptEdit. This module can be seen as a tool that allows writing and testing scripts in view of their use in the other two AVIExplore modules. It is however a standalone module that can be used to compute data from videos or images.
The three modules of AVIExplore use a similar internal structure (Fig. 2 ). Images are extracted from video sources; each incoming image being called 'Origin Image'. The result of applying an operation (for example contrast enhancement) on an 'Origin Image' is saved as a 'Work Image'. If the user needs to apply a second operation, he can either save it as 'Secondary Work Image' or rewrite on the initial 'Work Image'. In order to speed up operations, it is possible to work with greyscale images, which are then saved as a 'Grey Image'. Operations can be bidirectional as a 'Work image' can become an 'Origin Image'. However, in this case, this transformation is not reversible as the return to the initial 'Origin image' is impossible. A mask is a tool used to select a subset of pixels in an image.
These masks can be directly drawn by the user or computed using a script. Binary hexadecimal masks are used as internal structures to store intermediate information. They can be saved and loaded using an external file (*. msk). Other Regions Of Interests (ROIs) can also be drawn by the user and saved in a different external file (*. roi). ROIs can be converted in internal binary regions for computations. External files are similar within the three modules. They gather different types of information (Fig. 3):  (1) the AVI input file (main input), (2) script files to process images (processing), (3) ROI files to store working areas (service), (4) mask files to get fast mask loading (service), (5) data and companion files to store intermediate results (information), (6) time and position files which can be of interest for image tracking collected with mobile carriers (information), and (7) export information in text files.
The graphic user interfaces of each of the three modules share a similar structure with: (1) a displayed window, (2) a strip menu, and (3) a scrollbar for video timing. However, depending on the module, the functionalities present on the left side of the window may differ (Fig. 4 ). AVIExplore can be used for different purposes namely: (1) image acquisition, (2) testing of image-analysis procedures and image analysis per se (Fig. 5 ). The user may consider AVIExplore real time acquisition mode (which is present within both the AVIExplore -Fixed and the AVIExplore -Mobile modules) for acquiring a video to be analyzed with another module. During testing, different functions can be tried out in order to optimize and automatize the image-analysis procedure, which will then be applied to a whole acquisition sequence or a batch of acquisition sequences.
The AVIExplore Real Time acquisition mode Real time acquisition can be achieved for both mobile and fixed platforms through AVIExplore -Mobile and AVIExplore -Fixed modules. The main advantages of monitoring real time acquisition using AVIExplore are: (1) an absolute control of image frequency acquisition, (2) the possibility for real time video treatments, which includes the possibility of applying different filters to incoming images as well as the use of masks and the selection of objects. The user may then choose to directly save results and no longer original images, which can prove valuable when using acquisition memory limited devices. There is however a clear limitation for real time analyses: the flow of incoming images cannot be interrupted. Processing time must therefore remain less than the time lag between consecutive images.
The AVIExplore -Mobile module AVIExplore -Mobile handles both the video to be analyzed and a geolocalization file, which is used to locate each image and associated identified objects in space. Sophisticated Remotely Operated Vehicles (ROVs) usually produce this file when acquiring images but other mobile acquisition carriers do not. If the information concerning the location of the images is available through another way (e. g. a Log book), the user can generate a geolocalization file by himself. If not, the user can still proceed with image-analysis but in this case, identified objects will not be geographically located. The corresponding format for the geolocalization file is specified on Table  1. The first step when analyzing a video is to extract its images (Fig. 6A ). When using AVIExplore, it is recommended to first proceed with a global scene analysis.
This procedure extracts features (Table  2 ) from the images and generates a companion file containing relevant metadata, which are used for a fast comparison between images. This process allows for the identification of common: (1) properties, and (2) objects between images. It can be used to define sections of the original video where the presence of the same specific object is suspected and which therefore can be processed using the same image analysis procedure (script). The values of the extracted features can be exported on a text file for post-treatment. Object detections can be done either on this module or on the Script Editor module. The section on AVIExplore -ScriptEdit module describes the type of extracted information that can be exported.
The first file includes for each image the position (barycenter) and size (number of pixels) of each isolated region where the subtraction result is bigger than the specified threshold. The second file provides the mean position (barycenter and standard deviation) and the total size (total number of pixels) of all the regions where the subtraction result is bigger than the specified threshold found on an image. The section describing the AviExplore -ScriptEdit module provides more examples of the extracted information that could be exported.
The AVIExplore -ScriptEdit module The AVIExplore -ScriptEdit module allows writing and testing scripts, which can later be used in another AVIExplore module. However the AVIExplore -ScriptEdit module is also a standalone program that can proceed with the complete analysis of a video and produce a result file. Available functions and commands that can be used within this module can be divided into: (1) Video management and visualization functions. (2) Image processing operations. Some of these operations allows for: color filtering, image segmentation, edge detection, image and histogram equalization, morphological processing and noise removal. (3) Looping. Set of commands that allow applying one operation to a set of images. (4) Export and log. Functions that allow either to have a log or a result text file.
(5) Masks and ROIs. Set of functions allowing the use of masks and ROIs within a script. Those masks and ROIs can either be the result of an image processing operation or designed by the user. The detailed description of the high number of functions available is provided in the AVIExplore manual, which is embedded in the software and available online (http://www. epoc. u-bordeaux. fr/index. php?lang=fr&page=eq_ecobioc_aviexplore). Depending on the scope of the analysis, dedicated searches must be carried out to identify/classify objects. The user can choose which information to export, some examples are: the number of objects on an image, the surface and the perimeter of each object, the width and the length of the minimum bounding rectangle of an object, the position of each object, the mean and standard deviation of all the positions and surfaces of all the objects on an image.
The ScriptEdit module proposes a functionality to help the user write scripts. This functionality provides the users with the parameters required by all specific functions. The current version of the software includes examples of object detection and identification routines (needed for the mobile module) to help the user building their own scripts and to provide insights on which kind of operations are relevant to obtain specific information. Scripts can be saved within a Microsoft access database and then become easily accessible for subsequent analyses. The AVIExplore -ScriptEdit module has a complementary utility for video edition, including: (1) cropping, (2) reducing duration, (3) reducing resolution, (4) changing frame frequency acquisition, (4) extracting images, and (5) building a video from extracted images.
, 2005). In this context, video imagery acquired with ROVs has been used to detect macrohabitat and determine main seafloor features that includes the percentage of recognizable dead and/or live coral colonies  (Vertino et al. , 2010). This last analysis was achieved but at a low spatial resolution and apparently by direct visual video observation  (Vertino et al. , 2010). We report here on the use of the AVIExplore software to analyze another video collected in the same area. Our specific aims consisted in automatically: (1) detecting corals, and (2) differentiating between apparently dead and living coral patches.
Materials and methods The analyzed video was recorded with a remotely operated vehicle (ROV) Victor-6000 during the MEDECO cruise, which took place aboard the French R/V Pourquoi Pas? This ROV was equipped with a SONY DXC990 3 CCD camcorder (resolution of 720x576 pixels, frame rate of 25fps, duration 2 hours and 5 seconds). The study area covers 0. 022 km². The video contained a total of 180,133 images. Because of the deployment of an automatic flash device, overexposed images were removed every 298 images. A total of 179,529 images were thus considered for the analysis. The video was processed using the AVIExplore -Mobile module. A region of Interest (ROI) was created to exclude imbedded text from the analysis (Fig. 7 ). Two different scripts were executed to assess the surface of: (1) apparently dead and living corals within each image (Table  4A ), and (2) only apparently living corals within each image (Table  4B ).
Results The dissimilarity in the Grey-Level Co-occurrence Matrix (GLCM) computed from each image on the video was computed during the global scene analysis (Table  2 ). This allowed us to identify (Fig. 7 ) a continuous sequence of images featuring high dissimilarity of their GLCM. The direct visualization of those images showed that they were recording the water column and no longer the water-sediment interface. They were thus discarded and only 161,000 images were considered for the identification of corals. An example of the results of the two executed scripts is shown in Fig. 8. These results were combined with the geolocalization file allowing for the mapping of apparently dead and apparently living coral colonies (Fig. 9 ). Agreement between images classified by the software and visually was achieved for 90.
g. to abdomen contractions and pleopod beating). During 'Burrowing', the mud shrimp moves within its burrow but only over a limited distance and it occasionally displaces sediment. During 'Walking', the mud shrimp moves within its burrow over large distances. During the present study, we used the AVIExplore -Fixed module to automatically analyze the video and to quantify these three behaviors. A ROI corresponding to the visible part of the burrow was first drawn (Fig. 10A ). Each image was then subtracted from the preceding one and pixels featuring a positive differences higher than 30 (threshold value on an overall scale between 0 and 255 set after preliminary trials) were identified. For each pair of consecutive images, the AVIExplore -Fixed module generated: (1) the number of pixels within the ROI differing between the two images, and (2) the barycenter of those pixels.
Materials and methods The experimental setup is described in  (Jordana et al. , 2000). Briefly, worms were collected by dredging and kept in the laboratory in tanks filled with a thin layer of well-sorted fine sand supplied with running ambient sea water. During the experiment per se, 15 worms were geometrically positioned upward, with tube openings, 1 cm above the sediment surface (Fig. 12A ) in a 60. 3 x 40. 3 x 20 cm aquarium filled with a 10cm thick layer of well sorted fine sand and 30 l of filtered seawater. Polychaete worm filtering activity was monitored during 9 h using a CCD-ICS059 AI video sensor (VLSI Vision Ltd®) with an image frequency acquisition of 0. 017 Hz. The video was analyzed using the AVIExplore -Fixed module. Fifteen ROIs were drawn close to the shadow of the extremity of each tube, where the openings of gills crowns were most visible (Fig.
12B ). A fixed reference image with all gill crowns fully retracted was subtracted from each current images. 'Dark' pixels featuring negative differences lower than -30 (threshold fixed after preliminary trials) were identified and considered as indicative of gill crown deployment. They were summed for each ROI and the corresponding data were exported as text files. Filtering activity was defined as the ratio of the current number of 'dark' pixels divided by the maximum number of 'dark' pixels recorded at any given time within the corresponding ROI. Assuming, that maximal extension of the gill crown was achieved for all 15 worms during the observation period, this index allows for direct comparisons between worms irrespective of their size and position within the recorded field. Validation was achieved on one of the 15 specimens and accuracy was assessed as the proportion of images for which there was an agreement between visual and AviExplore assessment.
Along this line, and within JERICO, our own research group has recently produced the SPIArcBase software, which is specifically dedicated to the processing of sediment profile images  (Romero-Ramirez et al. , 2013). The aim of the development of the AVIExplore is much more ambitious. The goal is to provide a single platform flexible enough to automate the image analysis procedures requested by the largest possible sets of image acquisition platforms and ecological implications. In order to do so, we have followed a three-pronged approach: (1) building two modules specifically designed to process the video acquired with mobile and fixed image acquisition platforms and to tackle associated ecological questions, and (2) 4. 1. The AVIExplore -Mobile module  Edgington et al. (2006)  emphasized the fact that the potential of ROVs and underwater observatories for collecting long-term observations is constrained by the time and effort to view and quantify the data.
They thereby underlined the need for new software developments for processing videos obtained from mobile platforms. A first difficulty when processing those videos is that they often cover different habitats, which may correspond to very different images, which may therefore require different image analysis procedures. Applying all of those procedures to the whole video may rapidly become time consuming and could possibly lead to significant errors as well. AVIExplore therefore offers the possibility of defining sections of videos that are composed of similar images, and therefore can be processed using the same script. This is based on image comparison achieved on the Grey-Level Co-occurrence Matrix. Moreover, this procedure may prove useful even when a single habitat is monitored. This was the case during the coral mapping case study, where it facilitated the reduction of the number of analyzed images from 179,529 to 161,000 (i.
e. , by 10%) of the images to analyze. The reason for that was that the video recorder was no longer recording the sediment water interface but the water column by the end of the video. We strongly believe that in the future, this procedure will also prove useful in defining section of videos characterized by dissimilarities due to various characteristics including: light conditions, image quality levels, angle of view, contrast, clarity, and presence of objects. The AVIExplore -Mobile module also presents a high potential for assessment of benthic (mega)fauna. The visual assessment of benthic epibenthos is indeed often restricted to presence/absence data or to operator-dependent semi-quantitative data, which led  Cuvelier et al. (2012)  to plea for the development of automated image analysis procedures.
The AVI -Explore -Fixed module The AVIExplore -Fixed module gathers the experience gained by our research group through the developments of pieces of software specifically designed to process videos in view of assessing movements and activities of several benthic invertebrates and communities  (Bernard et al. , 2012; Duchêne et al. , 2000; Duchêne and Queiroga, 2001; Duchêne and Rosenberg, 2001; Grémare et al. , 2004; Hollertz and Duchêne, 2001; Maire et al. , 2007a; Maire et al. , 2007c). In its present form the AVIExplore -Fixed module mainly involves image subtraction procedures, which can first be achieved between the current image and a fixed reference image. This procedure is especially appropriate for the quantitative assessment of activity as was for example the case in the 'Quantification of filtering activity in the passive suspension-feeding polychaete Ditrupa arietina' case study.
, 2007; Turk and Pentland, 1991). In its present forms the AVIExplore -Fixed module allows for the assessment of the trajectory of isolated objects/organisms through the plots of the position of consecutive barycenters of modified pixels identified by image subtraction. Such a capacity has proven useful in the assessment of swimming capacities in pelagic larvae of benthic organisms  (Duchêne and Nozais, 1994; Nozais et al. , 1997). Benthic ecologists are nevertheless more and more interested in the study of individual movements occurring within groups of objects/organisms. Bernard et al. (2012)  for example developed a specific application to monitor the individual displacement of fluorescent sediment particles (luminophores). This requires the elaboration of a complex research algorithm (involving both the analysis of distances and directions of movements between consecutive pairs of images), which are currently not included in the AVIExplore -Fixed module.
The AVI -Explore -ScriptEdit module The Avi-Explore -ScriptEdit module allows for the analysis, processing and visualization of videos and images. In its current version, it includes a large set of functions for analyzing individual images. As demonstrated during the present study, it already constitutes a powerful tool that can be used for different purposes, e. g. ,: detection and quantification of organisms (see the Deep corals case study) and the detection/quantification of different kinds of activities. Other applications can be already foreseen such as: (1) the analysis of swimming (fishes and benthic invertebrate larvae) patterns, and (2) the study of specific processes such as the dynamics of benthic settlement and early recruitment. As it stands, the Avi-Explore -ScriptEdit module nevertheless does not allow for the quantitative assessment of some specific processes.