dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION Identification of transposable elements, including insertion sequences, in metagenomic datasets is critical in our ability to accurately define the profile of mobile genetic elements. In turn, accurate and complete characterization of mobile genetic elements (i. e. the mobilome) of a community is central to understanding the spread and epidemiology of different genes in microbial communities, such as virulence genes and antimicrobial resistance genes. Here, we describe a tool and subsequent catalogue that enables this to proceed. Palidis is a tool that discovers novel ISs from mixed microbial communities by applying a fast maximal exact matching algorithm to identify ITRs. As a result, we have released the first version of ISC, a catalogue containing 879 ISs. Already, this is a valuable resource for researchers to search for ISs in isolated genomes.
However, since Palidis was only applied to metagenomes sequenced from the healthy human oral cavity and stool samples, it is recommended ISC v1. 0. 0 is used as a reference for annotating isolates sourced from human oral and stool samples. The main limitation of the current ISC is that it only contains common DDE types of ISs with ITRs, although these mobile genetic elements make up a large proportion of ISs  [3]. Palidis is currently only equipped with discovering ISs with ITRs. We are planning to include other databases into the catalogue, such as ISfinder, and we invite the research community to contribute and submit ISs to the catalogue. Another limitation is that the catalogue currently contains ISs with ITRs that are 25 or greater nucleotides in length as generated by Palidis, although ITRs can be as short as ten nucleotides in length.
In light of current times, disruptive sequencing technologies are advancing rapidly by becoming more accurate and generating longer reads. Very soon, Palidis could be applied to longer reads of isolates to identify novel ISs, as well as generating reference databases (like ISC) from mixed microbial genomes. However, the ISC could become a valuable resource for querying microbial genomes for ARGs that have been acquired through transposition. We will continue to enrich the ISC towards a comprehensive catalogue by applying Palidis with different parameters to more mixed microbial genomes from a diverse range of sources, and encouraging submission of ISs from the scientific community.
Funding information The project was supported by the  Centre for Host-Microbiome Interactions, King's College London , funded by the  Biotechnology and Biological Sciences Research Council (BBSRC)  grant  BB/M009513/1  awarded to D. L. M. S. S. was supported by  Engineering and Physical Sciences Research Council (EPSRC) ,  EP/S001301/1 ,  Biotechnology Biological Sciences Research Council (BBSRC )  BB/S016899/1  and  Science for Life Laboratory (SciLifeLab). S. P. P. is supported in part by the  PANGAIA  and  ALPACA  projects that have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreements No. 872539  and  956229 , respectively.
All supporting data, code and protocols have been provided within the article or through supplementary data files. Two supplementary data files are available with the online version of this article. 000917 https://github. com/blue-moon22/palidis. (2) The Insertion Sequence Catalogue is available to download here: https://github. com/blue-moon22/ISC. (3) The raw reads from the Human Microbiome Project can be retrieved using the download links provided in Data S1 (available in the online version of this article). (4) The analysis for this paper is available here: https://github. com/blue-moon22/palidis_paper_analysis. (5)  The output of Palidis that was run on these reads is available in Data S2.
I. INTRODUCTION Serveless model. Serverless can be understood as both a programming model, called Function as a Service (FaaS), and a deployment model for the cloud. In such a model, developers design their applications as a composition of stateless functions which execution is event-driven  [9]. Serverless services free tenants from complex resource reservation as they are designed to handle on-demand scaling requirements. In the FaaS model, providers only bill customers according to their actual resources usage  [10]. They are fully responsible This work was supported by the Institute of Research and Technology b<>com, dedicated to digital technologies, funded by the French government through the ANR Investment referenced ANR-A0-AIRT-07. for deploying intelligent resource management and multiplexing at a finer granularity to optimize Quality of Service (QoS) metrics such as response time, energy consumption, etc.
Cloud infrastructures are more and more heterogeneous to fit the needs of dataintensive applications such as machine learning model training or big data analytics  [12]. However, specialized processors and GPUs are yet to be made available to customers in serverless offerings  [13]. Hardware acceleration should be decided by the provider on a per-application or per-request basis. State-of-the-art work shows that using such hardware in a cloud setting provides substantial gains in execution time and energy consumption  [14] ,  [15]. However, reference orchestrators such as Kubernetes with Knative or OpenWhisk lack the support for dynamic allocation of such hardware. Performance challenge for serverless deployment. Due to the transient nature of unreserved FaaS resources, latency, throughput and continuity of service are hard to guarantee  [16] ,  [17].
In this paper, we propose a full framework to deploy a deepfake detection application on a serverless cloud. This framework comprises an offline and an online phase. The offline phase is used to characterize the performance and energy behavior of the deployed heterogeneous hardware platforms. The online phase consists of an autoscaling platform and a scheduling strategy that make efficient use of (characterized) heterogeneous hardware resources to achieve per-request SLAs while reducing the energy consumption of the platform. For this case study, we devised a simulation environment 2  that models the infrastructure for a deepfake detection application, run by the provider as a Software as a Service using a serverless infrastructure. Some performance figures. With our allocation and scheduling policy, we were able to handle 50000 tasks in the same makespan as Knative with less than 36% QoS penalties.
C. Performance measurements As the FPGA acceleration card is intended to be more efficient than a CPU or a GPU  [25] , making a comparison of the inference time with these three technologies is a first requirement to enable the comparison of the energy cost per image. The performance evaluation in terms of execution time was realized with the same 10,000 images for the three different models. We built a two classes deepfake dataset, the real ones from the CelebA dataset  [26] , and the fake ones generated using a Generative Adversarial Network (GAN)  [27]. The quantization and compilation of the graph was performed with Vitis-AI in order to run it on the FPGA. Only considering the inference time, it turned out that on the three tested models (ResNet50, VGG16 and VGG19), the FPGA is 13. 08 to 13.
We consider tasks that can indiscriminately be executed on heterogeneous execution platforms. In the context of our specific case study, implementation of the different functions has been done by hand for each platform; however, work exists to allow automatic cross-compilation to heterogeneous architectures  [36] ,  [37]. The following metadata have been measured for each function, on each execution platform: • Memory requirements -the quantity of memory (in GB) allocated for the task; • Cold start duration -the duration of sandbox initialization when running the task on a platform that does not have the function in cache; • Execution time -the expected duration of effective execution of the task, excluding its initialization phase; • Energy consumption -the difference between idle and active energy incurred by the execution platform when it runs the task.
D. Scheduling strategy Workload characterization is instrumental to performance prediction, as it can guide scheduling decisions that lead to the fulfillment of QoS requirements  [40]. Our scheduling strategy relies on tasks metadata as described in section IV-B. Building knowledge about serverless tasks is achieved during an offline phase on our platform, as code is pushed to the provider's registries ahead of actual execution  [41]. In Knative, the scheduler handles incoming tasks in a FIFO fashion. To manage the different levels of QoS requirements, we propose that our scheduler pops tasks from the gateway queue by earliest deadline first. We compute the task deadline by using its worst execution time on the platform using equation 2, and multiplying it by the allowed duration deviation set by the QoS level.
A. Experimental setup We used measurements from the evaluation of three different machine learning models (see table  III ). These models have been implemented on three different execution platforms (see table II) explained in section III. These data served as input for a simulator we built using SimPy  [42]. The simulator follows the system model described in sections II-A1, II-A2, IV-B. We measured cold start delays for our case study applications, see table III. It appears that execution times are dominated by cold start delays, making adequate resource allocation a stringent requirement to comply with SLAs. In the performance evaluation part, we compare two autoscalers: • HeROfake (HRO) -Our heterogeneity-aware, metadatabased resource allocator; • Knative (KN) -We modeled the Knative autoscaler behavior to the best of our knowledge.
We set the size of the input images to 512 × 512 pixels, which is approximately 10 × 10 km in a 20-meter resolution image. The collection of training data consisted of digitizing polygons in the coconut-producing regions that were identified during the bioclimatic analysis. The polygons were drawn in 146 training images (Fig. 1b ) using the sub-meter resolution images that are displayed as the base layer in Google Earth to discriminate coconut from other land covers. The U-Net consisted of a binary classification of coconut (digitized polygons) and the rest of land covers (image background) and, thus, the resulting layer was a binary raster, in which each pixel presented values of 0 (coconut is not present) and 1 (coconut is present). The U-net model was trained and deployed using the PyTorch framework in the Microsoft Planetary Computer hub.
Area estimates in small tropical islands The global coconut layer relies on the availability of Sentinel-1 and Sentinel-2 data. These two satellites provide images for the larger land bodies across the globe, but the data is missing in parts of the Pacific and other small tropical islands. On small islands with no Sentinel-1 or Sentinel-2 data, coconut mapping was not possible using our classification model. To overcome this issue, we used a sampling-based method to estimate the coconut area in small tropical islands, owing to the availability of sub-meter resolution satellite images in most of these islands. The sampling-based approach comprised randomly sampling 5,000 points within the small tropical island extents (Fig. 1d ). Small tropical islands included islands with an area between 1 to 200 ha in the tropics (latitudes within latitudes 30°S and 30°N) in a reference dataset  (Sayre et al.
Sub-meter resolution images could be used in future research to accurately map sparse coconut and small tropical islands where Sentinel-1 and -2 data are unavailable. Very-high-spatial resolution images (<1 meter), such as those obtained by DigitalGlobe satellites or Planet's SkySat, can be useful to detect coconut that did not reach the full canopy closure and coconut that is sparsely present over the land. Object detection models using sub-meter satellite data or drone images have previously been used for detecting individual oil palm trees  (Li et al. , 2018)  and delineating the tree canopy  (Chemura et al. , 2015). Similar efforts have been made to map coconut using sub-meter resolution images  (De Souza and Falcão, 2020; Vermote et al. , 2020; Freudenberg et al. , 2019). Deep learning using sub-meter images could complement our closed-canopy coconut and can also be useful for mapping different palm trees, including coconut, oil palm, and sago.
, 2020a) , especially because coconut can be invasive in tropical islands  (Young et al. , 2017). More work needs to be done to map coconut areas on these islands, ideally using submeter resolution data where Sentinel-1 and -2 data are currently unavailable. Once such maps become available, they can provide better insight into the extent to which coconut has displaced natural ecosystems, relative coconut productivity (in areas with detailed harvest information), and potential for coconut expansion, conversion to other forms of agriculture, or restoration of natural ecosystems. Detailed and accurate spatial information is a key component in any land-use optimization planning, for coconut as well as other crops.
Data availability The dataset presented in this study is freely available for download at https://doi. org/10. 5281/zenodo. 7453178  (Descals, 2022). The file 'GlobalCoconutLayer_2020_v1-1. zip' contains 886 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes: [0] Other land covers that are not coconut. [1] Coconut. The file 'GlobalCoconutLayer_2020_densityMap_1km_v1-1. zip' contains the 20-meter coconut classification aggregated to 1 km. The value of each pixel represents the coconut area (in squared meters) within the 1-km pixel. The file 'Validation_points_GlobalCoconutLayer_2020_v1-1.
, 2020 ) is available at https://doi. org/10. 7910/DVN/PRFF8V. The country-wide harvested area of coconut was extracted from the FAOSTAT database at http://www. fao. org/faostat/en/ (accessed on 10 March 2022) under the item 'Coconuts in shell -Crops and livestock products (Production)'  (FAO, 2022).
Code availability The original code of the U-Net model can be found at: https://github. com/qubvel/segmentation_models. pytorch  (Iakubovskii, 2019)  https://doi. org/10. 5194/essd-2022-463 Preprint. Discussion started: 14 February 2023 c Author(s) 2023. CC BY 4. 0 License.
Conclusions We focused on developing the second global high-resolution map of a vegetable oil crop. We mapped the global distribution of coconut using a deep learning model that classified satellite data (SAR, Sentinel-1, and optical, Sentinel-2) into a 20meter land cover map depicting the extent of closed-canopy coconut. The model achieved a high accuracy for closed-canopy coconut, and the resulting coconut layer accurately depicts the regions with the highest density of coconut. The presented dataset can be integrated into the recently published Essential Agricultural Variables' "Perennial Cropland Mask" as well as fits the Food and Agricultural Organization's LCCS classification as "Cultivated and Managed Terrestrial Areas" -"Tree Crops" (Di  Gregorio, 2005). Our global coconut layer is of considerable interest to researchers and policy makers that inform the highly polarized debate about the use of vegetable oil crops (especially oil palm) to meet future demands for food, feed, biofuel, surfactants, and other oil uses.
Author contributions The conceptualization for this work originated from SW, ZS, MS, and EM. AD designed the study. AD, RD, TA, and NU collected the training data and RD, ZH, TA, NU collected the reference points. AD implemented the data processing workflow and generated the figures and tables. AD, SW, ZS, and EM wrote the draft and AD, SW, ZS, MS, RD, ZH, TA, NU, DLAG, and EM were involved in the revision of the manuscript.
VRESS The VR Enhancement of Social Skills (VRESS) was developed in line with the guidelines for developing VR software for research and clinical applications in the field of psychology  [66] , which they have been found to produce VR software that meets the criteria of the American Academy of Clinical Neuropsychology (AACN) and the National Academy of Neuropsychology (NAN)  [67]. The VRESS incorporates social scenarios that are exemplary of adult activities and common in daily life, such as renewing your subscription at the gym, selecting a movie and buying a ticket at the cinema, browsing the available options and purchasing a smartphone at the phone store, attending a seminar class and interacting with the instructor and the co-students, and attending a job interview and responding to the interviewers' questions.
Research Aims This study thus aims to evaluate the usability (i. e. , the capacity of a system to provide a condition for its users to perform the tasks safely, effectively, and efficiently while enjoying the experience) and user experience (i. e. , how a user interacts with and experiences a product, system or service) of an immersive VR training software of social skills (i. e. , VRESS) in adults with ASD. Moreover, this study strives to examine acceptability (i. e. , the quality of being satisfactory and able to be agreed to or approved of) of the VR training software of social skills as a social service (i. e. , by a service user's point of view) that may be prescribed and/or offered by clinicians, educators, and social workers to adults with ASD for training and improving their everyday social skills.
VRESS Scenarios and Interface The VRESS software runs on SteamVR and is compatible with every VR headset that runs on the SteamVR platform (e. g. , HTC Vive series, HTC Vive Pro series, Oculus Rift series, and Varjo VR series; see here for an exhaustive list). VRESS encompasses five social scenarios: 1) being at the gym; 2) buying a smartphone at the phone store; 3) going to the cinema to watch a movie; 4) attending a seminar class; 5) attending a job interview. Each scenario has three different difficulty levels: 1) easy; 2) moderate; and 3) difficult. Thus, the five scenarios have three diverse versions (i. e. , per difficulty level), which means that there are a total of fifteen diverse scenarios in VRESS. The difficulty level is determined by the complexity of the scenario in terms of how many social tasks the users have to perform and how many 3D characters they need to interact with (e.
The R language  [86]  on R Studio  [87]  was used for performing the analyses. The best-Normalize R package  [88]  was used to transform and centralize the data since the continuous variables violated the normality assumption. The distribution of the continuous data was then normal. For performing the respective analyses, the psych (correlational analyses)  [89] , the ggplot2 (plots)  [90] , and the stats (regression analyses)  [86]  R packages were used.
Discussion The present study aimed firstly to assess the usability, user experience, and acceptability of an immersive VR social skills training software (i. e. , VRESS) in adults with ASD. Results showed that in terms of the system's ratings, the VRESS software exhibited a relatively high performance with positive evaluations, as average scores were close to the high edge of the possible scores of questionnaires. Secondly, the examination of the associations between mental state/emotion recognition, EF, functionality level of individuals with ASD, performance in VR social scenarios, and the self-reported ratings revealed several statistically significant relations. Furthermore, the regression models' (single predictor) analyses revealed significant predictors of several aspects. The performance in VR social scenarios (i.
VR Training of Social Skills in Adults with ASD Based on the authors of the SUTAQ, UXQ, and SUS recommendations for interpreting their scores for technological interventions' acceptability  [91] , quality of the user experience facilitated by the software  [79] ,  [92] , and system's usability  [81] , the VRESS showed very high acceptability, user experience, and usability, as rated by the participants with ASD. High acceptability suggests that this software  [91] , which also facilitates remote intervention and training of social skills, will probably be preferred by adults with ASD. Likewise, the very high usability indicates that the VR software requires a small amount of effort from the user/trainee  [81]. In VRESS, the user had only to speak to the 3D characters by using the microphone of the headset and navigating in the virtual environment by pressing a single large button on the controller (either left or right).
Hence, from the trainees' part, only a single button was required to be used, while the rest were controlled and operated by the researcher (see subsection 2. 1. for details). Finally, the high user experience postulates that the VR software offered a highly pleasant and immersive experience to the trainee  [79] ,  [92]. Given that providing a therapeutic process, which is perceived as pleasant and positive by the patients, enhances the engagement and commitment to therapy, as well as the effect size of the therapeutic outcomes  [93] , the high user experience of VRESS suggests that it may achieve comparable positive outcomes. Furthermore, given that there is a scarcity of robust evidence supporting the feasibility and acceptability of implementing immersive VR interventions in populations with ASD  [54] ,  [55] ,  [57] , the results of this study provide substantial evidence that the implementation of immersive VR social skills training in ASD is feasible and acceptable by adults with ASD.
However, it should be noted that the VRESS was developed in line with the guidelines for developing VR software for psychological sciences  [66] , which also lead to VR software which meets the criteria of AACN and NAN  [67]. For this reason, beyond the high ratings in terms of acceptability, user experience, and usability, the participants experienced minimal to absent symptoms of cybersickness, which indicates that VRESS is a VR software that meets the health and safety criteria. Finally, since the VRESS was designed specifically for individuals with ASD, the observed high ratings of acceptability, user experience, and usability, highlight the necessity of developing VR software which considers the highly prevalent cognitive and behavioural symptoms in ASD. However, a downside was that the usability and acceptability of VRESS were significantly predicted by the performance in social scenarios.
Conclusions The VRESS appears an effective VR social skills training system, which facilitates a high acceptability, usability, and user experience in individuals with ASD, without inducing adverse symptoms. These positive outcomes pertaining to VRESS also support the effectiveness and feasibility of implementing VR social skills training in individuals with ASD. Furthermore, executive functions were found to be implicated in the social skills of adults with ASD. Finally, social skills were seen as the best indicator of the severity/functionality level of adults with ASD. Author Contributions: Conceptualization, K. P. , V. M. , C. S. , and A. P methodology, P. K. , E. K. , C. S. , and A. P; software, P. K. and C. S. ; validation, P. K. , E. K. , P. R. , V. M. , K. P. , C. S. , and A. P; formal analysis, P.