dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation protocol All presented models are trained on the publicly released part of the CholecT50 dataset otherwise known as CholecT45. Evaluations are conducted on the hidden 5 video test set. At the evaluation, submitted Docker containers are executed on the challenge test set producing a JSON file per video containing the outputs for the three sub-tasks. Triplet recognition evaluation is based on the Y IVT probability scores for valid 94 out of 100 triplet classes ignoring the last 6 labels with at least one NULL component in the triplet composition, as done in the previous challenge  (Nwoye et al. , 2022a ). Model's direct outputs for any component of the triplet are not considered, instead, the predictions for instruments, verbs, and targets are filtered from Y IVT using Nwoye et al.
The AP IVT which evaluates the complete instrument-verb-target combination remains the main metric for this category. For the detection, the TP, FP, and FN are computed by measuring the level of overlap between the groundtruth and predicted bounding boxes. A detection is assigned a TP if the degree of overlap exceeds a certain threshold and the ID (i. e. 3 https://pypi. org/project/ivtmetrics instrument ID for category 2 or triplet ID for category 3) is correct. The corresponding AP is computed as a weighted mean of the precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight: AP = 1 0 p(r)dr. (3) We also utilize the triplet association scores (TAS) introduced in  Nwoye and Padoy (2022)  to assess the capacity of the models at associating the bounding boxes to the correct triplet IDs.
sharing multiple components). Unsurprisingly, we see that the Distilled-Swin-YOLO model also comes out on top with top 5 and top 10 accuracies of 73. 5 and 87. 9, respectively. The gains in the performance are for two reasons -first, the choice of model, a Swin-Transformer that generates meaningful hierarchical features, and second, the use of an ensemble that suppresses noise in the predictions. Interestingly, the ResNet-CAM-YOLOv5 model, which places fourth for triplet recognition comes in joint-highest in the top 5 and top {5:20} performances, respectively. The higher topK accuracy compared to the AP scores show that a reduced number of triplet classes might theoretically be easier for a deep learning model to train. This would imply super-classifying related classes, resulting in more data to train each class.
Instrument localization Fig. 9  shows the instrument localization performance for each competing method, evaluated using the AP metric at various IoU thresholds. Immediately, we observe a large contrast in performance between the top-performing ResNet-CAM-YOLOv5 and the rest of the models; this can be attributed to the fact that the ResNet-CAM-YOLOv5 includes components that are pretrained on the CholecSeg8k, HeiCo, M2CAI, and En-doVisSub datasets for instrument localization, in contrast to the other methods, which rely purely on weak supervision signals. This is further substantiated when studying the AP 0. 1 results, where a couple of other methods (MTTT, SurgNet), are competitive with the ResNet-CAM-YOLOv5; this indicates that these models are able to achieve a rough localization of the instruments, but lack precision and are therefore harshly penalized by increasing the IoU threshold for the AP computation.
Comparison with the CholecTriplet2021 challenge While the CholecTriplet2022 challenge is an evolution of the 2021 edition  (Nwoye et al. , 2022a) , we note here several differentiating factors in the organization, participation, and methodology. The primary distinction between the two editions is the scope of each challenge. The first edition aimed at benchmarking, analyzing, and improving methods for surgical triplet recognition. As a natural progression, the current edition extends these tasks to be able to localize the operating instrument corresponding to each detected triplet (i. e. from triplet recognition to triplet detection) without spatial labels for training. To evaluate these methods, the 5 test videos of the CholecT50 dataset were augmented to include bounding box labels around the instrument tips in addition to the binary triplet labels available for all 50 videos.
To support these additional organizational efforts, the committee was expanded from 4 members to 8. In terms of the outcome, the 2022 edition offers a wide range of methods: 6 out of 11 models have extremely distinctive methodologies. Interestingly, methods focusing on knowledge distillation are proposed to supplement weakly-supervised learning of spatial localization from different datasets. There is also a significant reduction in the gap between the top and bottom placed models in the leaderboard as illustrated by box plots in Fig 13 : the smaller standard deviation across all metrics illustrates an improved and better understanding of the task modeling compared to the 2021 edition. The final triplet recognition mAP is not outperformed in the latest edition of the challenge. This could be attributed to the increased complexity of the task and the challenge of optimizing more task heads at various levels of supervision (full and weak).
Both approaches are acceptable standards for weak supervision, however, their advantages differ thus affecting their comparability. The best we could do in terms of constraints is to withhold the spatial labels of the benchmark dataset. Looking at the obtained results, there is still room for improvement, especially on the localization task. Also, localization, which is explored on the instruments in this challenge, is yet to be extended to the surgical targets. The task of weakly supervised target anatomy localization remains an open problem as generating spatial annotation is quite expensive, and importantly, the target anatomy is not solely determined by its visibility, which further complicates the problem. Furthermore, there are fewer datasets in the community for differentiating the anatomy/organ structures causing a delay in research focusing on recognition of anatomical targets: there is also no known pre-training source.
This contributes to the imbalance and suggests why the target recognition is poor compared to the instruments. Finally, how the traditional AP metrics translates a model capacity at triplet recognition and detection is yet to be investigated in full. In the CholecT45 and CholecT50 datasets, there is a level of similarity between certain distinct triplets. As an example, the triplets grasper, grasp, gallbladder and grasper, retract, gallbladder are different but at certain stage of the procedure could be used interchangeably without misrepresentation. At some other point, an action, e. g. dissect, can happen at an unclear boundary such that the two adjacent anatomies, e. g. gallbladder-neck versus cystic-duct, can be interchangeably considered as the target without loss of generality. Taking everything into consideration, it may be fairer to design AP metrics that would consider the proximity of the predicted triplets to their interchangeable ground truth counterparts.
Challenge survey We conducted an anonymized survey on the organization of the challenge. The questionnaires were circulated online to the participants including those unable to proceed to the submission stage. The respondents were allowed to rate on a scale of 1-5 (later converted to percentile in the analysis) their satisfaction on each of the parameters of the challenge, and offer recommendations where they feel so. We collated the responses into four groups, presented in Fig. 14 , and summarized as follow: (a) Motivation for participation: The novelty of the task of triplet detection as well as the interest in the CholecT50 dataset top the reasons for many entrants into the contest. Participation is mildly influenced by the availability of computer infrastructure and the opportunity for a joint publication of the challenge findings.
(b) Challenge organization: The organization and coordination of the challenge receive a very high rating, especially on the clarity of the task instructions, responsiveness to participants' queries, and provision of helpful resources. Participants agree that the challenge started early enough with sufficient time allotted for its completion. (c) Usefulness of provided resources: The Docker template, self-validation system, and spatial-labeled dataset were rated to be the most useful. Providing snippets of code for easy starting, metrics library, reference papers, and GitHub repositories are also of great help. The creation of group and support mailing lists is rated lowest and this could be attributed to the alternative use of a dedicated Slack channel for daily communication. (d) Difficulties: The participants identify the low performance of their models as a major factor discouraging them from proceeding beyond the validation phase.
Conclusion With CholecTriplet2022, we presented a study in the form of an international contest focusing on the development of deep learning models for the detection of fine-grained surgical activities in laparoscopic videos. The task is subdivided into three categories of (1) recognizing surgical actions as triplets instrument, verb, target , (2) localizing the instruments performing the actions, and (3) pairing the localized instruments with their correct action triplets. The research and experiments are conducted on the CholecT50 dataset which has been annotated with triplet labels. The localization sub-task is handled by weak supervision. The challenge ran through a 5-month window recording a total of 11 teams' participation showcasing 11 new deep learning methods to solve the problems.
Data availability The CholecT50 dataset and the validation data used in the challenge have been made available to the public and accessible via https://github. com/CAMMA-public/cholect50. The test set spatial labels will be released publicly. The baseline model code will be released as well. Participants can release their code on their own volition. All released code would be linked to the central GitHub repository for the challenge: https://github. com/ CAMMA-public/cholectriplet2022.
and merged Load docker Mount Drives Execute Docker Compute Results Docker pull Map dataset Run model output NO exists ? NO container exists ? YES NO Map output read only access ? YES YES *GPU runtime error ? NO YES YES correct ? format NO NO internet restricted ? YES NO write access ? YES YES *Memory *Time limit exceeded ? NO Compute result write result Log Fig. 3. Flowchart of the self-validation system used in the challenge with the CholecT45 binary labels. The statistics of the entire dataset as used in the challenge are provided in Table.
Deployed from 2009, the purpose of the Design Safety approach is, "to put in place the technical and/or organisational resources to reduce the risks incurred by those involved during the construction phase" (Mbaye and Saliou 2014). We seek to understand the role of spatial visualisation and workplace situations in risk prevention. A field study was conducted over a three-year period, suggesting that Design Safety involves strategic decision-making during upstream project phases, combined with operational decision-making during downstream phases, each with an impact on safety. In this context, the work of designers should be supported by specific artefacts (databases of photographs, technical documentation, 3D visualisation, etc. ) to give them a better understanding of the workplace situation and visibility in terms of the risks incurred by the workers.
Safety Integration and Digital Simulations While a number of standards describe how to provide for safety integration and workstation ergonomics, they must be supplemented by digital simulation tools. These tools help visualise the design of workspaces so as to rapidly integrate good safety practices. Within modelled work spaces, the use of digital "dummies", integrated into work environments represented via CAD software (Computer Aided Design), improves the visualisation of the users' workplace situations in order to improve risk prevention. Designers can therefore be instrumental in preventing risks to workers (new structures and, by extension, revamping of existing structures), as long as they are trained and have a thorough understanding of the manufacturers' requirements  [3]. In addition, designers argue that they rarely benefit from appropriate initial training as well as tools allowing them to take personnel safety into account  [16].
The job of those working on the ground is precisely to adapt a generic design file to the local context. Therefore, an important operational avenue would be to increase the amount of information available in engineering centres. In concrete terms, designers cannot always avail themselves of technical plans and sufficiently reliable and exhaustive photographic databases to fully project into the local context of the actual work site. A possible improvement would therefore be to increase the knowledge available in engineering centres on the "life of work sites" and the actual working conditions of those involved in factories (e. g. , time to access the site, joint activity, rescheduling, etc. ).
This means that rock slabs with only one main texture were analyzed by selecting only 1cm 2 area, whereas those with multiple textures were analyzed by selecting multiple 1cm 2 areas. In all cases, cm-sized clasts/grains were avoided. The digital images were processed by using both Gimp and ImageJ software applications. Employing the former software, the selected 1cm 2 areas were analyzed in order to identify all visible clasts (hos rocks)/survivor grains (fault rocks). The individual clasts/survivor grains were then manually drawn in order to carefully trace their individual shapes. As a result, 135 a bitmap image was obtained for the single 1cm 2 areas. There, the clast/survivor grains were reported with a black color, while both carbonate matrix and cement with a white color (Figure  2a ).
Pores were not considered at this stage of the work. Afterwards, by using the command "Analyze Particles" of ImageJ software (Figure  2a ), all clasts/survivor grains included in the 1 cm 2 bitmap images were automatically processed to measure: (i) particle count, (ii) total particle area, (iii) average 140 particle size, (iv) area fraction representative of the percentage of clasts/survivor grains (pixels highlighted in black using the Threshold command), (v) percentage of matrix, and (vi) perimeter of the clast/survivor grains. A particular attention was paid to the computation of the Box-counting Dimension D0(grains), which was obtained by using the ImageJ software. This software automatically superimposed a square grid on each binary image, and then counted the boxes occupied by, at least, one clast/survivor grain (filling frequency).
, 1987). The sieving technique hence provides suitable results for near-rounded shapes of grains, but digital image analysis is 160 preferred in the case of cohesive rocks  (Ferraro et al. , 2018). From the 44 study rock slabs, 4 x 6. 5 cm-wide thin sections impregnated with a blue-dyed epoxy resin were then obtained in order to carry out a pore space digital image analysis. Images of 6. 17 x 4. 55 mm-wide areas were taken by means of a 170 Nikon Eclipse E600 optical microscope equipped with a Nikon E4500 camera. Afterward, two representative images were selected from each thin section, and then analyzed by using the Gimp and ImageJ software applications. As a result, applying the "select color" command of the Gimp software, the blue portions filled with the epoxy resin were selected. The obtained bitmap images highlighted the pores, shown with a black color, with respect to the clasts/survivor grains/matrix and cement shown with a white color.
Then, by using the command "Analyze Particles" of ImageJ software 175 (cf. Figure  3 ), all black objects (pores) included in the 6. 17 x 4. 55 mm-wide binary images were automatically counted to compute: (i) 2D porosity (percentage of pixels highlighted in black by using Threshold), (ii) circularity (shape factor, with 1. 0 indicating a perfect circle and values close to zero indicating a very elongated shapes), and (iii) aspect ratio (ratio between longest and shortest axes of ellipses inscribed within each pore). The Box-counting dimension, D0(pores) was https://doi. org/10. 5194/egusphere-2023-2247 Preprint. Discussion started: 13 November 2023 c Author(s) 2023. CC BY 4. 0 License. computed d by superimposing a square grid on the binary image. The automatic counting of the boxes occupied by, at 180 least, one pore was then repeated for increasing box sizes, from 2 to 128 pixels, and finally plotted in a ln-ln space to compute the angular coefficient of the power-law best-fit line.
Methodology In this study, an in-depth bibliographic search was carried out in order to scan all the studies involving the use of metal-based nanomaterials stimulated by PTT in the treatment of bacterial and fungal infections. The literature search was stopped on 28 July 2023 using the Medline and PubMed databases, and the following keywords: 'bacteria', 'photothermal therapy', and 'nanoparticles'. Out of a total of 403 publications, 40 reviews were excluded. Then, 88 articles using non-metallic nanomaterials and 80 treating viral or tumor cells were excluded as well as 1 article in which authors did not use PTT. In addition, 82 articles that did not use gold and silver nanoparticles (Fe:20; Cu:28; Ti:9; Zn:4; Te:2; Pt:3; Rh:2; Se:3; PB:1; Pd:1; Re:1; and BP:3) were excluded. In the end, 113 articles were included in the study (Figure  1 ).
Moreover, an additional bibliographic study was carried out to show the use of metalbased nanomaterials in PA imaging. Using the same databases and the keywords 'bacteria', 'photoacoustic', and 'nanoparticles', a total of 49 articles were identified. A total of 4 reviews were excluded, as well as 27 articles that did not use metal-based nanomaterials or gold or silver NPs, and in which treatments were performed on tumor cells. In the end, 11 studies were included (Figure  2 ).
Regulating artificial intelligence Overall, AI is considered a driver of the digital transformation of society and economy, as well as a source of innovation and growth potential, with broad fields of application in the energy industry  (dena, 2019; WEF, 2021) , some of which are mentioned above. In addition, it is widely believed that AI can also contribute to a majority of the United Nations "Sustainable Development Goals"  (Vinuesa et al. , 2020). Such benefits lead to an increased number of businesses adopting AI technology to their operations (dena, 2019). In the energy sector, the use of AI goes beyond "traditional" energy-centric use cases and enables business-centric functions such as customer relationship management  (Künzel and Lohse, 2022) , big databases knowledge extraction and summary with knowledge graphs  (Wang et al.
Likewise, a data protection impact assessment has to be prepared. b. If the network company has its own AI systems or makes them available to other network operators (e. g. , DSO forecasts to TSO), further efforts are required as he then acts as a manufacturer of AI systems. For example, manufacturers of AI systems should create a quality management system, keep updated technical documentation, and ensure that the AI system receives a conformity assessment. If it is an AI system that is applied in a high-risk sector, manufacturers should register the AI system in an EU database and inform the responsible national authority of the EU member state where the AI system is used. In addition, the network company would be responsible for ensuring that the AI system complies with the requirements of the AI Act.
3) European or third-country AI developers with business activity in the EU or third countries: If the AI system is applied within the electricity sector, it could be considered a high-risk system. Hence, manufacturers need to register the AI system in an EU database and inform the responsible national authority of the EU member state where the AI system is used. Furthermore, the responsibility that the AI system complies with the requirements of the AI Act lies with the manufacturer of the AI system. Constraining our analysis to the electricity sector only, we discard any AI developers for example, being located within the IT sector), as their compliance costs eventually will become part of the balance sheet and cost structure of the analyzed electricity systems, e. g. , such as through trickling down into electricity costs  (Renda et al.
Acknowledgments The work of  Konstantinos Parginos  was financed by the  Ai4theSciences  project (Grant No. 945304 ) hosted by  PSL University  and funded under the  Marie Skłodowska-Curie H2020 research and innovation Program. The work of  Ricardo J. Bessa  was financed by  National Funds  through the  Portuguese  funding agency,  FCT -Fundação para a Ciência e a Tecnologia , within project  LA/P/0063/2020.
Regarding aging, data on upper-lower asymmetries are scarce and remain largely inconclusive. Although some research has reported that the VMA persists in late adulthood  32, 33  , other psychophysical and visual search studies have revealed that vertical performance asymmetries are modified in older age  [34] [35] [36]. Here, we sought to determine whether changes in visual encoding in one vertical hemifield could modulate the mnemonic performance of young and older adults. The aim of the present study was threefold. First, it strove to characterize the influence of the vertical position of information on item and spatial memory; second, it attempted to compare such visual preferences between young and healthy older adult populations; third, it verified whether the area of participants' upper and lower visual fields could explain performance differences.
Apparatus and setting. We used PsychoPy v2020. 2. 10 42 on a Dell monitor with a 1280 × 1024-pixel resolution. The screen subtended 29°of visual angle in height and 36°of visual angle in width. Participants sat 57 cm from the monitor with their heads positioned on the chinrest of a head-mounted monocular eyetracker (EyeLink 1000 Tower Mount, SR Research Ltd. , Canada). We collected responses with a mini numeric keypad (KKmoon) positioned flat on a table in front of the participants. Young and older adults with visual corrections removed their glasses to perform the task because multifocal glasses could have biased performance in the upper or lower visual fields. Finally, the experiment took place in a quiet, dim-lit room at the participants' preferred time of day. Stimuli. Stimuli consisted of 327 photographs of objects selected from the datasets of the mnemonic similarity task  43  (available at https://github.
One-half of the participants saw object 1 in the upper part of the screen, and the other half saw object 1 in the lower part of the screen. Visual testing. Trained orthoptists performed all visual testing. We assessed all participants' visual acuity and contrast sensitivity using the LogMAR scale and the Pelli-Robson score, respectively. We also performed kinetic perimetry on the Octopus 900 (Haag-Streit, Switzerland). We obtained participants' visual fields from both eyes using stimulus sizes of V1e, III1e, II1e, and I1e at 4  degrees per second for isopter charting. We adjusted participants' responses to stimuli presentation for reaction times. We then used custom MATLAB code to process the visual field data for each participant's left and right eyes. The latter extracted upper and lower visual areas along the horizontal meridian for the isopters corresponding to stimulus sizes of V1e, III1e, and II1e.