dataesr/scientific-paragraphs-categorization huggingface dataset:
There are several online resources that provide dermatological information. For example, the Dermatology Atlas (https:// play. google. com/ store/ apps/ detai ls? id= com. andro mo. dev70 6301. app78 2141) and VisualDx (https:// apps. apple. com/ us/ app/ visua ldx/ id348 177521) are applications that contain extensive collections of photographs of different conditions and are useful resources for both medical students and practicing doctors. Resources for patients are also available in different forms and typically also in the patient's language. An example is the Italian platform https:// lapel lesic ura. it that offers information about several dermatological conditions, including HS. In the BATMAN project, a smartphone application (https:// play. google. com/ store/ apps/ detai ls? id= si.
Aim 3: Methods Medical data is obtained in the form of a standard electronic health record (EHR) and is uploaded into the platform by the physician, using a unique patient identifier. Genetic data, likewise, comes in a standard format and is uploaded using the same identifier. Lifestyle data is collected in real time from the patients through a dedicated smartphone application, and is of two types. Self-reported data is obtained using questionnaires that the patient fills in when prompted. In the food preferences questionnaire, the patients report their dietary preferences, namely how often they eat certain types of food. This form is filled in by the patient only once in the study as it is expected that the preferences are unlikely to change during the duration of the pilot. Additional questions, that are asked only once, related to the alcohol consumption and smoking habits, whether they like to exercise in a company, and the daily hours of sleep.
Acknowledgements This work was supported by a  Biomolecular Analyses for Tailored Medicine in AcneiNversa (BATMAN)  project, funded by  ERA PerMed  ( JTC_2018 ) through the  Italian Ministry of Health , the " Fondazione Regionale per la Ricerca Biomedica" (FRRB) , the  Slovenian Ministry of Education, Science, and Sport (MIZŠ) , the  Austrian Science fund (I 4229) , the  Federal Ministry of Education and Research Germany (BMBF) , and  ANR  automate ( ANR-20-CE15-0018-01 ). This work was also supported by and by a grant from the  Institute for Maternal and Child Health IRCCS 'Burlo Garofolo/Italian Ministry of Health  ( RC16/2018 ) and by a  Starting Grant  ( SG-2019-12369421 ) founded by the  Italian Ministry of Health. Figures were created with BioRender. com.
While this data captures in principle all the physical content of the 1-loop amplitude, it is sometimes useful to write down an explicit Mellin space expression, which we do in the attached Mathematica file in terms of various ambiguities that are all in principle fixed by superconformal symmetry, but are difficult to compute in practice for R|R. 3 The main technical difficulty relative to previous 1-loop calculations such as the k = 2 theory and results in 4d  [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]  and 6d  [4] , is that the double discontinuity in the k = 1 case also receives contributions from tree level data of OPE coefficients with odd twists, 4 which are much harder to compute. We will also study the holographic correlator at finite c T using the numerical bootstrap combined with constraints from supersymmetric localization.
Here, P t i (V ) are polynomials in V divided by monomials in (1-V ), and K(1-V ) and E(1-V ) are elliptic integrals of the first and second kind, respectively, which occur in sums of odd twist data as discussed in  [28]. We can then easily expand in superblocks to get the average odd twist OPE coefficients (λ R p,t, ) 2 for odd p and t and any. Although we could not guess any simple closed form for this data, we computed enough to get the 1-loop expression in the next subsection. We give some sample values in the attached Mathematica file. For the R 4 amplitude, we need to fix the overall coefficient B R 4 (p), which depends on the specific holographic dual. For AdS 4 ×S 7 /Z k , it was shown in  [14]  that the flat space limit fixes B R 4 (p) = 32•2 1 3 (p-1)(p+1)(p+3)(p+5) 3•3 2 3 π 8/3 k 2/3 Γ p 2.
These 1-loop inversion formulae were already computed in  [14] , the only difference now is that the DD of the even and odd twist contributions will give different factors. For instance, we can compute the 1-loop λ 2 (A,+), as λ 2 (A,+), = 12(2 +5)Γ( +3) 4 Γ + 5 2 2 Γ + 7 2 2 1 0 dz z g +4, +2 (z)dDisc[G [0040] (z z, 1-z)| z ] , (2. 26) where we set U = z z , V = (1-z)(1-z), and the DD of the even and odd twists are computed as dDisc [f (z, z) √ 1-z] = 2 √ 1-zf (z, z) , dDisc [f (z, z) log 2 1-z] = 4π 2 f (z, z) , (2. 27) where we assume here that f (z, z) is analytic as z → 1. Similar formulae are given for the other CFT data in  [14]. The resummed 1-loop DD's in the relevant irreps are given in the attached Mathematica file. We can then apply the inversion formulae to these DDs to get the R|R CFT data: (λ R|R (A,+) 0 ) 2 = 513.
JHEP11(2022)091 for the full k = 1 theory using the Mathematica code attached to  [34] , where one must carefully subtract the free theory contribution. For instance, for λ 2 (B,+) 0040 = 32 3 δ p,2 + 256 -π 2 (p+2)p 2 +2(p+2)p 2 ψ (1) p 2 +4(p-1)(3p+4) 3π 2 pc T + 8192•2 1 3 p 3 +3p 2 -p-3 3•3 2 3 π 8/3 c 5/3 T + g(p) c 2 T - 32768•2 2 3 (p-1)(p+1)(p+3)(p(p+2)-4) 9•3 1 3 π 10/3 c 7/3 T +. , (3. 3) where ψ (1) (x) denotes the degree 1 Polygamma function, the 1/c T contribution was given already in  [48] , and the 1/c 5/3 T term can be matched to (2. 18) for k = 1, which was fixed from the flat space limit. For the 1/c 2 T term we could not find a simple pattern, but the p = 2 values are f (2) = g(2)/5 = 16384(2π 2 -25) 45π 4. ( 3. 4) Note that for p = 2 we have the linear relation (3. 2), but for p > 2 these OPE coefficients are independent.
Acknowledgments We thank  Ofer Aharony ,  Davide Gaiotto ,  Silvu Pufu , and  Xinan Zhou  for useful conversations. The work of LFA is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  787185 ). SMC is supported by the  Weizmann Senior Postdoctoral Fellowship. The work of LFA is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  787185 ). LFA is also supported in part by the  STFC  grant  ST/T000864/1. The authors would like to acknowledge the use of the  University of Oxford Advanced Research Computing (ARC)  facility in carrying out this work. (http://dx. doi. org/10. 5281/zenodo. 22558) JHEP11(2022 ) 091.
Setup and treatment duration of DIBH To determine the feasibility and clinical compatibility of DIBH, comprehensive data collection and analysis was performed. Firstly, session duration and CBCT shifts were retrospectively collected using Aria® oncology information system (Varian Medical Systems, Palo Alto, USA). Secondly, setup time, CBCT analysis time, number and duration of BH required for treatment delivery were manually recorded by the radiation therapists at the treatment console. Additionally, treatment reports automatically generated by AlignRT InBore™ in ". csv" format were retrospectively analyzed using a Python script  [27]  to compute monitoring time including setup, CBCT imaging and analysis, treatment duration as well as the number and duration of each treatment BH.
Online and offline review of CBCT images Daily online soft tissue matching of CBCT acquisitions to planning CT images was performed by one of the therapists (all with >10 years' experience) operating the Halcyon™ machine. CBCT shifts applied by the therapist were retrospectively collected to determine the reproducibility of DIBH. Additionally, a retrospective offline review of CBCT images was undertaken by the senior physician leading this study to individually verify the compliance of breast, heart, spine, sternum and diaphragmatic domes positions. For each of the 22 patients enrolled in this study, a minimum of one set of CBCT images per week was randomly selected and analyzed. CBCT images were registered to planning CT data and vertical, longitudinal and lateral shifts were manually reported into a dedicated registry.
Only patients capable of repeating and maintaining the DIBH position in the 10 consecutive BH are thereafter considered eligible to DIBH. Day to day repeatability of DIBH and patient's ability to comply with previously established references are verified during the simulation session prior to CT acquisitions. Lastly, patients are now treated with an abdominal breathing maneuver (A-DIBH) which was found to be easier to reproduce as opposed to the previously used thoracic breathing maneuver (T-DIBH)  [28]. To ensure appropriate and reproducible lung filling (T-DIBH) during A-DIBH we add a part of the abdomen in the monitoring ROI  [29]  and we use the deformation module (3D deviations with colors) to ensure that the patients inhale by inflating the belly as much as possible. As a single-centre retrospective study on a limited data sample for the first 22 patients treated on Halcyon™ in DIBH with AlignRT InBore™ guidance, the present work involves several limitations.
5 The Technical University of Delft has developed a software EXCALIBUR (acronym for EXpert CALIBRation). It is based on a method introduced in  [12]  which combines parametric and quantile inputs for continuous uncertain quantities from weighted experts. It then outputs a probability distribution (or equivalently a CDF). 6  Aggregating experts' uncertain opinions or probabilistic predictions is a well-studied mathematical/social choice problem  [1, 31, 33, 12] , sometimes referred to as belief aggregation, or opinion pooling. The formal model 3 We are grateful to Thomas Boyer-Kassem, Roger Cooke, Aris Filos-Ratsikas, Hervé Moulin, Clemens Puppe and some anonymous EC2021 referees for their helpful comments and suggestions. 4 "In round 1, statements were provided by EAN scientific panels (SPs).
In round 2, these statements were circulated to SP members not involved in writing them, asking for agreement/disagreement. Items with agreement > 70% were retained for round 3, in which SP co-chairs rated importance on a five-point Likert scale. Results were graded by importance and reported as consensus statements. " 5 We refer the interested reader to Roger Cooke web page: http://rogermcooke. net which contains useful references, real data, software for expert aggregation, and a wide range of applications. 6 ANDURYL  [23]  the successor to EXCALIBUR is open source and scriptable with a graphical user interface. arXiv:2108. 04705v2 [econ. TH] 19 Aug 2021 is the following. Each expert i ∈ N is asked to provide his prior probability distribution p i ∈ P = ∆(Λ) over a set of outcomes Λ (usually finite or a real interval).
This U-net network architecture is computational extremely more efficient than CNN architecture, while it couples pixels in large lateral spatial regions, which is an important feature in this application. The evaluation in  [2]  showed that this deep optimization prior approach finds a better minima than classical optimizers (Table  1 ), estimates model parameters in low SNR levels (Fig. 3 ) and robustly reconstructs parameters in shot noise situations. More details of the experiments can be found in  [2]. Table  1. Comparison of average ℓ 2 -squared loss in (2) using measurement dataset by classical optimizers to the Deep Optimization Prior (DOP) approach. The full details of experiments can be found in  [2]. Average Normalized Loss (×10 3 Deblurring on the THz model parameter image In this section, we compare the resolution enhancement by the deep optimization prior (DOP) approach to the per-pixel curve fitting approach using the THz image enhancement methodology in  [1].
Experimental Setup We evaluate the deblurring result on the MetalPCB+AWGN datasets from  [2] , which are based on a measured FMCW THz image datasets using a resolution target. The datasets are synthetically added with an Additive White Gaussian Noise (AWGN) at PSNR noise level from -20dB to 10dB by simulation respectively. For the comparison of the per-pixel curve fitting optimizer, we choose the AdamW  [8]  op- Comparison of RMSE of model parameter ê using synthetic datasets at AWGN noise level from -20 to 10dB. The full details of the experiments can be found in  [2]. timizer adopted in  [2]  instead of the Trust-Region Algorithm  [9]  in  [1]  to be able to properly compare with the DOP approach in  [2]  by taking the same optimizer and hyperparameters. For the deconvolution method, we apply one of the modern blind deblurring method by Xu et al.
Evaluation Fig. 4  shows the intensity images by the original datasets (first row), the AdamW estimation (second row), the DOP estimation (third row), and respective deblurring images (last two rows) using MetalPCB datasets at AWGN noise level from -20dB to 10dB. Note, that the original datasets intensity image is extracted by the same methodology in  [1,  reference intensity in Section 7. AdamW optimizer approach (second row) and the DOP approach (third row) using MetalPCB datasets at AWGN noise level from -20dB to 10dB. Images by AdamW and DOP (last two rows) approaches are deblurred by blind deconvolution method from Xu  [5] ,  [6]. data at the center of sampling window. By visual comparison of the intensity images of AdamW and DOP estimation, we observe that DOP approach obtains less outliers for very low SNR level, i.
e. , -20dB and -10dB. These outliers are more significantly observable after the deblurring procedure, which makes the deblurred DOP intensity image having a better quality than the deblurred AdamW intensity image because of the robustness of DOP estimation. Fig. 5  plots the vertical and horizontal intensity difference for each resolution target group from 4000µm to 280. 6µm, using the MetalPCB datasets at 0dB AWGN noise level as an example. As a higher intensity difference represents a better ability to resolve a resolution target, both deblurred AdamW and DOP intensity images out-perform the original dataset image, while the deblurred DOP intensity image obtains a more stable intensity difference than the deblurred AdamW image. By comparing the 3dB crossing dimension obtained by both deblurred images, the DOP apporach enhances the performance of deblurring method because of the robustness of model parameter estimation.
The purpose of this paper is to introduce a network architecture for monocular depth estimation which balances network size and training time without sacrificing too much accuracy or overfitting to specific training data. To this end, we propose an encoder-decoder network for monocular depth estimation using inception style layers  [5]. Our architecture performs on-par with state-of-the-art algorithms while being comparably light-weight, i. e. having less trainable parameters, and is generalizable to different datasets and resolutions. Contributions. We propose a novel encoder-decoder architecture for depth from a single RGB image using inception blocks. Our architecture optimizes the trade-off between having few parameters for efficient training and inference but still resulting in good accuracy.
NYU v2 Dataset In our experiments, we used the publicly available NYU Depth v2 RGB-D dataset  [6]. This dataset contains 464 video sequences of 26 different indoor scenes recorded with a Microsoft Kinect RGB-D camera with over 400k frames in total. It contains two sets of 480 × 640 images, which are labeled (pre-processed) and raw sets. The labeled dataset accounts for 1449 pairs of RGB-D images which were randomly selected from the raw dataset. The second set is the raw dataset, which contains raw images (RGB and depth) and accelerometer dumps as produced by the Kinect camera. This set is unprocessed and with many missing depth pixels. Hence, we pre-processed and aligned the raw depth image using the acceleration data and tools provided with the dataset. The test images were randomly selected from the labeled images.
The rest of the labeled images were added to training set. Then, the validation set was randomly selected from the whole training set. We synchronized the RGB and depth images based on the timestamps of the captured frames. To increase the size and diversity in the training set, we augmented the training set with horizontally flipped versions which are meaningful for indoor scenes. The augmentation was done off-line as preprocessing. In total, we used 194K images (after augmentation) for training, 654 images for validation, and 654 images for testing. In our experiments comparing to existing works (Table  1 ), we have used the train-test split proposed by the dataset. Cropped Training Data Due to the slight offset of depth and RGB frames and hardware, the training data contains varying amounts of missing boundaries.
iBims-1 Dataset Additionally, we evaluate on the iBims-1 dataset  [7]. This dataset contains 100 RGB-depth pairs from 10 different indoors scenes, and contains more in-depth ground-truth labeling and evaluation metrics than NYU. It is meant to further evaluate models trained on different datasets, especially NYU, showing generalization capability and providing more detailed metrics. All images are 480 × 640 pixels, with ground-truth depth, masks annotating invalid and missing pixels, as well as some semantic segmentation masks and plane estimations that are used for evaluation.
iBims Metrics As an enrichment to the the standard metrics, the iBims dataset offers a collection of additional metrics based on the additional ground-truth annotation in the dataset. We will give a short overview of the measures here, please consult  [7]  for the exact formulas. [7]. All of the methods were trained on NYU. Although the winner in the standard metrics is clear, the newly introduced metrics for iBims show varying results. Planarity and Orientation: Based on ground-truth annotated major planes in the depth data, the values ϵ plan PE and ϵ orie PE measure how planar the result is in comparison to the ground-truth and how close the orientations are to each other, respectively. Location Accuracy of Depth Boundaries: The next metric measures how well boundaries in the depth maps are preserved by comparing detected edges.
NYU v2 We evaluate on both the original resolution of the NYU dataset as well our training resolution (see Section 4. 1), using the same pretrained model for both. On the training resolution, our method performs extraordinarily well, outperforming the competitors in all measures (which are all normalized wrt. resolution). On the original resolution, our results are still close to state-of-the-art methods although this resolution was never seen during training. Notice that the NYU test set does not suffer from the same boundary issues the training set has. We are convinced that our architecture would perform even better on the original with a slightly adapted training procedure overcoming the missing boundary problems. The exact results are reported in Table  1. Qualitative results can be seen in Figure  4.
iBims-1 On the iBims, dataset we compare to three methods in the benchmark of  [7]. Since this dataset is rather new, not that many methods have been evaluated on it. All methods were pretrained on NYU and not finetuned to iBims. While the winner in the standard metrics is clear on this dataset, the results for the more detailed metrics of the dataset are mixed. Again, our method is not far off the state-of-the-art although our network is a lot smaller and was trained on a different resolution. The exact results are reported in Table  2. Qualitative results can be seen in Figure  5.
Conclusion We presented a novel encoder-decoder architecture with modified inception blocks for the challenging task of monocular depth estimation. The inception blocks focus on finding cross-  - - - - - - Up Pool3 2 128 - - - - - - - Basic Conv2 3 32 - - - - - - - Up Pool4 2 32 - - - - - - - Basic Conv3 3 16 - - - - - - - Basic Conv4 3 1 - - - - - - - channel relationships instead of relying on the spatial information of convolutions only or including geometric priors in the architecture as state-of-the-art methods do. We are able to reach close to state-of-the-art results on the NYU v2 dataset while using a considerably lower amount of trainable network parameters. We also showed how our network is generalizable to different resolutions and datasets by training on a smaller resolution on NYU, and evaluated the pretrained NYU model on the iBims dataset.
Discrete computational models The discrete computational models offer a more precise analysis of the deformation behaviour of the metamaterials but at higher computational costs than the homogenised models. The strut based cellular structure, such as open-cell foams and auxetic structures, are mostly modelled with the beam finite elements  [8] ,  [36] ,  [37]. The computational models of TPMS lattices are generated using the shell finite elements using the MSLattice code  [38]  to generate the fundamental lattice geometry of TMPS samples  [40]. Meshing was performed by the PrePoMax software  [45] , and the boundary conditions were defined in the LS-PrePost software. The manufacturing imperfections resulting in plate thickness variation were also analysed  [41]  and were considered indirectly through the used material parameters determined by inverse parametric computational simulations.
Solution architecture As such, the architecture implemented for the developing serious games can be accessed in Fig. 2 , through a representation of the system/framework components diagram that will be further described. The architecture proposes three main components: (1) component referring to the game interface, in which the user can access the serious game either through a PC or mobile device with Android system; (2) the component that relates the four main implementations that are constantly interconnected with the business logic of the serious game; and (3) component related to the game server that includes the multiplayer implementation through sockets and a REST API together with a database in order to make the serious game endure.
As for the subcomponents of text-to-speech and speech-to-text, these allow the implementation and use of Portuguese language using the synthesized voice of Cortana in case the game runs on Windows. On Android mobile device, the text-to-speech engine also allows for that in case the device can provide the European Portuguese. Another subcomponent used is the Avatar of Portuguese Sign Language, an avatar developed by GILT (Games, Interaction and Learning Technologies), that aims to support deaf users and those who understand Portuguese Sign Language. The user can click or select the text presented in the game to obtain a real-time translation operated by this avatar. The server component is divided into two main components, a virtual machine (Amazon EC2) hosted on Amazon's cloud platform, called Amazon Web Services, and the Docker container which provides an image-based deployment model.
It makes it easy to share an application or set of services, including all their dependencies across multiple environments. Docker also automates the deployment of the application (or set of processes that constitute an application) within the container environment  [15]. In this project two images are deployed, one with NodeJS that not only enables the integration of the online component of the game through socket communication, but also the creation of a REST API to obtain data regarding the ratings on the game server. The second image that makes up the Docker container is the database, which through NodeJS will receive the data that must be endured between the players and the server. To define the essential functional requirements to the serious game, 10 essential requirements were determined for the development of the game, in particular the focus on the player.