dataesr/scientific-paragraphs-categorization huggingface dataset:
This characteristic potentially improves the performance of IQA models. During the past decade, several CNN models have been proposed for traditional 2D images, mainly patch-based ones  [4] , where the prediction is performed on individual portions of the input images rather than the entire content. Good performances were obtained on various 2D-IQA databases. The appeal of patch-based methods stems from their demonstrated effectiveness across many image-processing tasks, particularly image classification. Furthermore, by focusing on salient parts of the image, translated into patches, quality prediction tends to account for scene exploration. The main issue, however, is the lack of appropriate datasets with opinion scores per individual patch or a methodology to reliably derive them from global scores.
Moreover, patch-based training implies sampling patches prior to training. The latter are used as data augmentation to make the data fed to the model richer, diverse, and sufficient, enhancing both accuracy and robustness. This work introduces a novel patch-based CNN model for accurate blind 360-IQA. The model incorporates spatial attention, feature reuse, and a carefully designed structure to enhance its performance. The proposed framework includes an adaptive patch sampling, a patch-based training strategy, and patch-to-360-degree image quality aggregation. Extensive evaluations on three publicly available 360-IQA databases demonstrate the effectiveness of the proposed framework compared to state-of-the-art methods. The main contributions of this model can be summarized as follows: • Introducing a novel behavior-based patch sampling strategy on the sphere, which takes inspiration from how users explore 360-degree images.
Yang et al. [22]  proposed a patch-based model using the ResNet-34  [30]  backbone. The input patches are enhanced using a wavelet-based enhancement CNN and then used as references to compute error maps. The selection is made randomly on ERP images, not taking into account the geometric distortion nor the relevance of the patch content. Furthermore, a simple arithmetic mean pooling is performed on the obtained patch scores to compute the final quality of the 360-degree images, overlooking the important aspect of the non-uniform distribution of perceived quality. Differently from the use of traditional CNNs, some recent works  [27, 29]  opted for the use of ViT  [38]  based on the impressive results they achieved in different image processing tasks. As of today, 360-IQA lags behind 2D-IQA, mainly due to the lack of benchmark databases.
It is of paramount importance to train, test, and validate IQA models on large and representative datasets. Unfortunately, only a few 360-IQA of them exist, and they may not be deemed sufficiently representative of the field. This is holding back the development of accurate, precise, and general-purpose 360-IQA models, especially, those based on deep learning. To the best of our knowledge, only a limited number of 360-IQA databases are proposed in the literature, including Kim et al. [19] , MVAQD  [11] , CVIQ  [20] , OIQA  [39] , and IQA-ODI  [22]. Most of these databases are not publicly available, and diversity may be lacking in some of the available ones. By analyzing the content of CVIQ, OIQA, and MVAQD in terms of spatial information (SI) and colorfulness information (CFI), one can observe a lack of diversity on CVIQ as demonstrated by the SI vs.
It is acknowledged that it is more effective when the source and target domains are similar enough. The most common source domain used is image classification; hence, it is knowledge acquired by training on the ImageNet  [42]  dataset. The latter is composed of solely natural images with distortion occurring in the camera pipeline, which would not allow accurate predictions of perceived visual qualities for other distortions such as blur or noise. Subsequently, the design of reliable, accurate, and robust 360-IQA tools that solve the highlighted 360-IQA-related issues is in urgent need. In the following section, we describe a 360-IQA model built on the provided literature to robustly assess the quality of 360-degree images.
Datasets Training and evaluation of the proposed model are performed on three publicly available 360-IQA datasets, namely OIQA  [39] , CVIQ  [20] , and MVAQD  [11]. We conducted the performance comparison with state-of-the-art models on the OIQA and CVIQ databases. Performances of SOTA models are gathered from the corresponding papers or other studies, due to the unavailability of most training codes. As MVAQD is rarely used in the literature, we discarded it from the comparison, but it was included in the ablation study. Details regarding these databases are summarized in Table  1. The proposed model is implemented using TensorFlow and trained on a server equipped with Intel Xeon Silver 4208 2. 1 GHz, 192 G RAM, and a GPU Nvidia Tesla V100S 32 G. The batch size was set to 32 and the Adam optimizer  [59]  is used with β 1 = 0.
9 and β 2 = 0. 999, following the recommendation in  [59]  so as to prevent adverse effects on optimization. The learning rate is set initially to 1e -3 with a learning decay of 1e-4 /EPOCHS to help the optimization of the model. A five-fold cross-validation is adopted for a complete assessment on the selected datasets. Each fold was trained for 100 epochs. During training, the datasets are randomly separated into 80% for training (including 20% dedicated for validation) and 20% for testing. To ensure a complete separation of the training and testing sets, the distorted images linked to the same pristine source are allocated to the same set. This helps to test the model on unseen content, avoiding a very common mistake that is frequently overlooked, in which datasets are split based on distorted images.
Hence, the ability to classify images according to their perceived quality can be highlighted. Basically, it compares the capacity to distinguish different/similar and better/worse among image pairs and provides a percentage of correct classification denoted as C 0. Unfortunately, such a metric necessitates additional data, such as the standard deviation of MOS. For this study, we could only collect the required data for the OIQA dataset. Thus, we only perform this analysis on that specific dataset. As one of the main motivations behind the development of the proposed model is to achieve better accuracy while having less complexity compared to state-of-the-art models in general and multichannel ones in particular, we performed an inference analysis. Hence, a complexity analysis is provided in terms of the model's parameters and the number of floating-point operations (FLOPs).
2%), and AHGCN by approximately 0. 7% (specifically, 0. 4%). Similar behavior can be observed with the prediction error in terms of RMSE. This illustrates the accuracy and monotonicity of our model and its ability to evaluate the quality of 360-degree images in a close manner to human judgment. Regarding the performances across different types of distortions, the SOTA models still lag behind the performances of the proposed model for JPEG and GB. For JP2K and WGN, the proposed model achieves competitive performances compared to VGCN, AHGCN, and Yun et al. in terms of PLCC and SRCC. A possible explanation could be the LCN normalization. In terms of RMSE, however, the proposed model achieved the lowest error for individual distortions as for the overall database. The superiority of the proposed model is highlighted once more.
When looking at the performance of 2D-IQA models, it is clear that DB-CNN outperforms 2D traditional models, including FR models such as SSIM and MS-SSIM. This demonstrates the advantages of a deep learning-based model for quality assessment over traditional approaches. Among the performances of the proposed model, the best is achieved when using outlier rejection (OR) and saliency together as the aggregation strategy. It appears that discarding predicted local qualities that are outside the agreement range ±λ * σ(LQs) before averaging the scores improves the correlation performances. This is demonstrated by the acquired performances on the overall database and across distortions compared to considering all local qualities, except for GB. Simple averaging resulted in slightly better correlation performances for GB.
Considering all content of CVIQ, the proposed model achieved slightly worse in terms of accuracy compared to VGCN while outperforming MC360IQA, Zhou et al. , and AHGCN. In terms of monotonicity, our model achieved the best correlation. As for the prediction errors, the MC360IQA scored the best, with a slight difference. With regard to the performances obtained on OIQA (Table  2 ), those on CVIQ seem to be slightly worse when compared to SOTA models. Here, the diversity of the training dataset is affecting the accuracy of the model. It is worth noting that CVIQ is less diverse compared to OIQA in terms of (i) distortion types and (ii) content, which is paramount to better training the model. Regarding the performances across the different distortion types, the proposed model reached competitive performances compared to multichannel ones.
Cross-Dataset Evaluation With the intent to demonstrate the generalization ability of the proposed model to new content built using different conditions, we carried out a cross-dataset evaluation. Hence, we trained our model on OIQA and tested its performance on CVIQ and vice versa. Table  4  reports the obtained performances in terms of accuracy (PLCC) and monotonicity (SRCC). As highlighted, our model outperforms the others while showing the lowest computational complexity, demonstrating its ability to generalize new content and distortions. For instance, by training on OIQA and testing on CVIQ, our model achieved a PLCC (respecting SRCC) score of 0. 9145 (specifically, 0. 9020), outperforming MC360IQA by approximately 10% PLCC and 7% SRCC, VGCN by approximately 3% PLCC and 4% SRCC, and Zhou et al.
by approximately 8% PLCC and 9% SRCC. By training on CVIQ and testing on OIQA, similar behavior can be observed. Comparing the training datasets, we observe that training on OIQA resulted in significantly better performances. Here, the diversity in terms of content, as well as distortion types, appears to significantly contribute to the generalization capability. Whereas, with less diversity as in the case of CVIQ, relatively poor performances are attained. Moreover, OIQA comprises GB and WGN, which are not available on CVIQ. This affects the ability of the models to adapt to new distortions. Nevertheless, the proposed model attained satisfactory performances compared to SOTA. In terms of complexity, the proposed model with 6. 19 million trainable parameters and 3. 38 G of FLOPs has drastically fewer parameters and requires the least operations compared to the other models.
In particular, VGCN (26. 7 M, 220 G) requires much more computational complexity. This is due to its architecture, involving twenty ResNet-18 in parallel with a graph CNN and a sub-network composed of the DB-CNN. In the case of MC360IQA (22. 4 M, 22. 7 G) and Zhou et al. (29. 3 M, 6. 45 G), a higher complexity is also shown. However, for the latter, it is significantly lower in terms of the number of FLOPs compared to the other multichannel models. The reason lies in the weight sharing among the CNN channels. We further monitored the evolution of PLCC and RMSE performances of the proposed model during training on the whole OIQA and CVIQ databases to analyze the behavior. From Figure  11 , we first observe that training on OIQA (336 epochs) converges faster than CVIQ (407 epochs). This is due to the size of the training sets composed of 528 images for CVIQ and 320 for OIQA (40% difference).
Ablation Study We conduct an ablation study to analyze the impact of various components composing our model, including (i) patch sampling, (ii) the use of the SPA module, and short and long skip-connections, (iii) loss functions by comparing the use of the Huber loss to MSE and MAE, and (iv) quality score aggregation. As mentioned previously, we use the MVAQD database in addition to OIQA and CVIQ.
Without the SPA module, the activation maps appear more spread-out. For example, in the second row of the visualization, patches (b) and (g) show a high refinement of the salient regions, specifically focusing on the white button and tags on the suitcase. This refined attention to salient details reflects human perception. However, for patch (e), the SPA module failed to focus on the vacuum, which is a less prominent object in the patch. It is important to note that Grad-CAM provides local explanations, offering insights into the model's decision at the pixel and patch levels.
PLCC is strongly affected by the value of λ. This is particularly true on OIQA, where PLCC shows a relative inflection after λ = 2. 5. The same can be observed with SRCC. As for CVIQ, PLCC appears to be stable and less affected by λ. This supports the observation drawn in Section 4. 2, where the predicted local qualities seemed to be within an agreement. However, SRCC expresses an increase at λ = 1. 5, whereas it is stable elsewhere. For MVAQD, both PLCC and SRCC slightly improve at different λ values before decreasing. The obtained results highlighted an important aspect related to the dataset dependency.
Conclusions In this paper, an attention-aware patch-based CNN model for blind 360-IQA was presented. Spatial attention is used to help the model focus on spatially meaningful features. Skip-connections within the spatial attention module were also integrated to align the preserved features using spatial attention. The exploration behavior, as well as a latitude-based selection, were used to sample patches appropriately on the sphere for training the model. The latter has shown a significant improvement over standard sampling, not accounting for 360-IQA peculiarities. Patch quality aggregation was accomplished adaptively using saliency and outlier rejection, which resulted in an improved correlation with the ground truth. The proposed model demonstrated good performances across different databases and distortions.
In comparison to SOTA models in general, and multichannel ones in particular, our model reached competitive or much better performances while offering the lowest complexity. This demonstrates the value of the adopted (i) appropriate patch sampling, (ii) data representation, (iii) model architecture, and (iv) aggregation strategy. Furthermore, the proposed model's generalization capacity demonstrated its superiority in adapting to new content and distortion-based cross-database evaluations. While the proposed strategy led to some improvements, several questions are required to be addressed, especially the adaptive patch labeling when using a patch-based model. The lack of mean opinion scores per individual patch is an important problem to tackle in our future work.
The village has to its east the Hasbani river, originating from the northwestern slopes of Mount Hermon in Hasbaya (36 km away from Kawkaba and located at 750 m a. s. l. ;  Badr et al. , 2014; Jurdi et al. , 2002). On the other hand, the Litani River (170 km long and located at 800 to 1000 m a. s. l. ; Fig. 1 ) rising in the south of the Bekaa valley is about 29 km away from Kawkaba  (Abou Habib et al. , 2015; Khatib et al. , 2018). These two rivers are polluted, and for this reason, they are not used for irrigating crops in Kawkaba and the surrounding areas, while the olive trees are growing rainfed, as per what is indicated by the municipality of Kawkaba. Here as well, we did not find indication of direct Hg pollution. Climatic data in both Bchaaleh and Kawkaba were collected from the meteorological station and manual rain gauge installed in the villages by LARI (Lebanese Agricultural Research Institute).
Acknowledgements. The  Institute of Evolutionary Science of Montpellier (ISEM) at Montpellier University  and the  Research Platform for Environment and Science -Doctoral School of Science and Technology (PRASE-EDST)  at the  Lebanese University  are acknowledged for their support of the laboratory work. Credits go to the  Lebanese Agriculture Research Institute (LARI)  for assuring automated weather stations and manual rain gauges per site. The authors are grateful to the  Municipality of Bchaaleh (Rachid Geagea ) and the  Municipality of Kawkaba (Mira Khoury ). Acknowledgments are extended to  Amira Yousef  at  LARI  for her kind support and to  Akram Tabaja  for helping during fieldwork. We are very grateful to  Hakan Pleijel , who reviewed this article, improving significantly our paper.
We deeply acknowledge  Andrew Johnston  for the English revision. Financial support. This work was supported by the  National Council for Scientific Research of Lebanon (CNRS-L) ,  Montpellier University doctoral  fellowship grant ( CNRS-L/UM ), and the  Franco-Lebanese Hubert Curien Partnership (PHC-CEDRE ; project no. 44559PL ). Review statement. This paper was edited by  Jens-Arne Subke  and reviewed by  Håkan Pleijel  and three anonymous referees.
Purpose The GODDESS © tool was developed to assess Desmoid Tumor/Aggressive Fibromatosis (DT/AF) symptom severity and impact on patients' lives. This study evaluated GODDESS © 's cross-sectional and longitudinal measurement properties. Methods The Phase 3, randomized placebo-controlled, DeFi study (NCT03785964) of nirogacestat in DT/AF was used to assess GODDESS © 's reliability, construct validity, responsiveness, and estimate of meaningful change thresholds (MCTs). Other patient-reported outcome (PRO) measures included Patient Global Impression of Severity (PGIS) in DT/AF symptoms, EORTC QLQ-C30, Brief Pain Inventory Short Form, and PROMIS Physical Function short-form 10a v2. 0 plus 3 items. Results DeFi participants (N = 142) had a median age of 34 years (range: 18-76) and were mostly female (64.
Data source Analyses were conducted using blinded data from the Phase 3, double-blind, randomized, placebo-controlled study of nirogacestat in adults with desmoid tumor/aggressive fibromatosis (DT/AF) (DeFi; NCT03785964). Patients were administered nirogacestat or placebo tablets twice daily continuously in 28-day cycles. Eligible patients were ≥ 18 years old, with histologically confirmed DT/AF that progressed by ≥ 20% per Response Evaluation Criteria In Solid Tumors (RECIST) v1. 1 within 12 months of screening, and an Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) score ≤ 2. A 1:1 randomization allocation was stratified based on tumor location: intra-abdominal (including mesentery/pelvis) and extra-abdominal (including head/neck, para-spinal, extremities, abdominal wall, chest wall, and other).
0  [19] , which assesses cancer patients' HRQoL; the Patient Global Impression of Severity (PGIS) and the Patient Global Impression of Change (PGIC) referring specifically to desmoid symptoms severity and overall status  [20] , respectively. All PRO measures were completed by the patients using home electronic PRO (ePRO) devices at screening, baseline (i. e. , Cycle 1), and in 28-day cycles thereafter. For full details of the PRO measure assessment schedule, see Appendix 4 of the supplementary material. Measurement properties of the PRO tools were evaluated by using measurements at baseline, cycle 4, and cycle 7 from the DeFi study. The blinded data were pooled across treatment arms for these validation analyses. Baseline demographic and clinical variables were described using descriptive statistics (e.
Engagement: This was determined by looking at the number of people who accessed, completed or viewed different components of the MOOC including: Pre-and post-MOOC Knowledge Assessments and the post-MOOC Evaluation: The number of participants who completed the pre-and post MOOC Knowledge Assessments and the post-MOOC Evaluation were counted (data sourced from the Content Management System of the www. SCIMOOC. org website). Facebook or Weibo Group: The number of participants who joined each FB or Weibo Group, and posted comments or reacted to posts were counted (data sourced from FB or Weibo). Emails: The number of instructional emails that were sent and opened were counted (data sourced from MailChimp). Website hits on www. SCIMOOC. org: The number of page views of www. SCIMOOC. org were recorded (data sourced from Google analytics).
This website contained the weekly instructions. Website hits on www. elearnSCI. org: The number of page views of www. elearnSCI. org were recorded (data sourced from by Google analytics). This website contained most of the learning content. Daily views of www. physiotherapyexercises. com: The number of page views of www. physiotherapyexercises. com were recorded over the duration of the MOOC although participants were only asked to use this website during the 4th week of the course to create exercise booklets for a patient (data sourced from Google analytics). Attrition rate: This was estimated on the basis of the number of participants who completed the post-MOOC Knowledge assessment. It was also based on the change in the number of page views of www. SCIMOOC. org and the www. elearnSCI. org websites from the beginning to the end of the MOOC, and the drop in the number of comments posted to the threads each week on FB.
BEHAVIOUR: Will the participants change their clinical practice or teach others differently in response to the MOOC? Text analysis: The two notable clinical skills participants wrote about were (i) using what they had learnt to improve the way they taught patients motor skills (ii) using www. physiotherapyexercises. com to There was a 33% and 7% drop off in the number of page views from the weeks with the highest number of page views per day to the weeks with the lowest number of page views per day for the www. SCIMOOC. org and www. elearnSCI. org websites, respectively. Capture of the Google Analytics data for www. MOOC. org commenced 5 days after the start of the MOOC (and may therefore be an underestimation of page views). FB, Wk 5 I really learn alot this course even change my thinking approach and treatment strategies of my pts with SCI.
DISCUSSION This study uses qualitative and quantitative data to determine the impact of our MOOC designed to train physiotherapists in the management of people with SCI. It also explores the learners' experiences. Specifically, we sought answers to four key questions categorised under the headings: reach, effectiveness, reaction and behaviour. The results of this study are important because they will help organisations like ISCoS make decisions about using their scarce teaching resources, money and effort to run these MOOCs in the future. They will also help improve the conduct of future MOOCs (both on this specific topic and other topics). One of the great strengths of our MOOC was its reach. Nearly 26,000 people from 169 different countries registered for the MOOC. Many were from countries where there may be few opportunities for physiotherapists to learn about SCI.
ACKNOWLEDGEMENTS The 2022 MOOC was run in conjunction with the  International Spinal Cord Society. The website that housed the MOOC was initially funded by the  University of Sydney. The upgrading of the website to incorporate the different languages was funded by the  International Spinal Cord Society. The MOOC was based on the physiotherapy-specific module of www. elearnSCI. org: an initiative of the  International Spinal Cord Society  with contributions from over 40 physiotherapists around the world and funding from many different sources. The physiotherapy contents of www. elearnSCI. org was translated into the four languages by volunteers. The content of the MOOC (excluding content on www. elearnSCI. org) was primarily translated by JI, YX, GG and SR with assistance for the French version from EW,  Christophe Lombard  and  Juliette Medeleien  (from the  International Committee of the Red Cross ).