dataesr/scientific-paragraphs-categorization huggingface dataset:
Instead of learning one-shot affine transform to reference views, we learn multiple affine transforms in a coarseto-fine manner to match the reference features for view synthesis. • Furthermore, to allow users to interactively manipulate views at any angle, we introduce the Conditional Latent space AutoEncoder (C-LAE). It consists of 1) patch based correlation coefficients estimation and 2) conditional angle encoding. The former enables finding global features for 3D scene coding and the latter introduces target angles as one-hot binary codes for view interpolation. • In addition, we provide two different types of datasets to train and test our model. One is the synthetic 360 • images collected from the virtual world, including UrbanCity360 and Archinterior360. Another is the real 360 • images collected from real-world indoor and outdoor scenes, including HungHom360 and Lab360.
D. Data Collection and Generation Our target is to render 360 • views for both indoor and outdoor environments. Most of the existing datasets are either collected as object-centred views from virtual machine  [32]  or indoor environment  [33] ,  [34]. To demonstrate the versatility and efficiency of the proposed See360 model, we propose two different types of datasets for training and evaluation: 1) virtual-world data, 2) real-world data. The basic process Fig. 6. Examples of our proposed UrbanCity360 (A), Archinterior360 (B), HungHom360 (C) and Lab360 (D) datasets. The UrbanCity360 and Archinterior360 are rendered from the virtual world. The HungHom360 and Lab360 are rendered from the real world. was to fix the camera at a given location. Then we horizontally rotated (along the y-axis) the camera to capture the images of 360 • views.
The angular step was set to 5 • , enabling us to collect 72 views at each location. For each scene, we also collected the segmentation map. Note also that the datasets were captured to avoid many moving objects so that the static 3D structure can be learned by the proposed model. For virtual-world data, we suggest using an open source project (unrealCV  [50] ) to render indoor and outdoor views from virtual worlds. Based on the available toolkit, we randomly placed the camera (distances range from 5m to 100m) and controlled it to capture images at different angles. For the indoor environment, we used the realistic virtual world "ArchinteriorsVol2Scene2" for data collection. It describes a scene of a house with 1 bedroom and 1 bathroom. We refer it to as "Archinterior360". For the outdoor environment, we used another realistic virtual world called "UrbanCity" for data collection, where the scene is a street block.
We refer it to as "UrbanCity360". For "UrbanCity360" and "Archinterior360". We randomly placed the camera at 100 different locations to capture the scenes. We used another 10 locations to capture the images for evaluation. In summary, each dataset includes 7200 training images and 720 testing images. We also collect semantic segmentation maps from UnrealCV  [50]  for training. For real-world data, as shown in Figure  1a  and b , we installed the camera on an electronic tripod head to collect different views. We also collected both indoor and outdoor datasets for estimation (with distances ranging from 5m to 30m). For the outdoor dataset, we placed the camera on a street of Hung Hom, Hong Kong. We randomly chose 14 locations to collect a total of 14 × 72 = 1008 training images and another 5 × 72 = 360 images for testing.
We refer this dataset to as "HungHom360". For the indoor dataset, we chose a laboratory in a university. We placed the camera at four corners of the lab to collect 4 × 72 = 288 training images and placed the camera at the center of a lab to collect 72 images for testing. We refer it to as "Lab360". We used pre-trained HRNet  [53]  to generate the corresponding segmentation maps for training (see equation 8). As shown in Figure  4 , the four datasets (UrbanCity360, Archinterior360, HungHom360 and Lab360) cover a large variety of indoor and outdoor environments with a large variety of contents. For example, UrbanCity360 contains views of a whole block of street. There are buildings, trees, street lamps and many other outdoor objects. More importantly, it has very strong lighting changes, such as shadows on the ground.
A. Datasets Our goal being to achieve 360 • neural view rendering without any prior about the 3D environment (e. g. no depth map, point cloud or other information), the left and right views we provide as input need to overlap slightly. In our experiments, we set the angle between them to 60 • (which is also the FOV limit for most of the common cameras). This ensures limited overlap, enabling the images to show quite different scenes. To complete a 360 • view, we can use 6 such references, to interpolate a maximum of 72-6 = 66 intermediate views. As introduced in Section III-A, we set the code length δ = 12 so that every two references, we have 12 intermediate views, that is, the minimum angle change is 60 • /12 = 5 •. All intermediate view images are the ones we used as ground truth for comparison.
B. Implementation Details 1) Network Architecture: As shown in Figure  3 , the generator of the proposed See360 method firstly downsamples the feature maps 3 times by a factor of 2. Each downsampling unit consists of 1 convolution layer (with kernel size 4 × 4, stride 2) followed by Instance Normalization and Leaky ReLU. The Conditional Latent space AutoEncoder consists of a few fully connected layers followed by ReLU activation functions. The Multi-Scale Affine Transformer upsamples the feature maps 3 times by a factor of 2 using simple bilinear interpolation. The structure of the discriminator is designed as 3 layers of convolution followed by Instance Normalization and Leaky ReLU. We trained our network using Adam optimizer with a learning rate of 10 -4 and a batch size of 8. The training process for virtual-world datasets took about 8 hours on a single NVIDIA GTX1080Ti GPU.
In contrast, the training on real-world datasets took about 10 mins at most because 1) we only have a few numbers of real-world images, so the training is fast and 2) we used the model pre-trained on virtual-world datasets as a starting point for fine-tuning, so we did not need too much training time. We conducted our experiments on a Pytorch platform. The source code and results will be made available upon publication of this work. 2) Methods to Compare With: This work being the first one, to our best knowledge, on camera-centered 360 • neural view rendering, there is no existing approach tackling exactly the same problem that we can compare with. However, as shown in Section II, there are a few works that explored novel view rendering by proposing different network architectures. For comparison, we choose three state-of-the-art methods to implement their structures to resolve our problem: Conditional GAN  [23] , HoloGAN  [35] , Pix2pixHD  [45]  as shown in Figure  2.
D. Comparisons With Prior Works Let us compare our approach and other methods on 4 different datasets, using the 5 different evaluation metrics just discussed. Results are shown in Table  I. As illustrated in Section III-B, we sparsely selected 6 references (0 • , 60 • , 120 • , 180 • , 240 • and 300 • ) to cover the whole 360 • view. For each two adjacent references, every 5 • angle, we predicted one view leading to the estimation of 6 × 12. 72 novel views, totally. We used PSNR and SSIM to evaluate data fidelity compared to ground truth. We found that our proposed See360 achieves the best performance compared to others by about 0. 6 dB and 0. 3, respectively. For LPIPS, we used the pre-trained AlexNet  [54]  to measure feature distance, which describes the semantic differences on the deep feature representation.
e. , HoloGAN, C-GAN and Pix2pixHD are proposed for camera-centered view synthesis so that they do not fit the target of 360 • view rendering, and 3) other methods focus on synthetic/simple objects rather than real-world scenes, hence they do no perform well. For visual comparison, Figure  7  shows two examples from virtual-world datasets and two examples from realworld datasets (where C-GAN stands for Conditional GAN). We visualize both the generated images as well as the segmentation maps. In Figure  7A , we can see that the proposed See360 can reconstruct the details of the buildings, while others distorted the textures. For example, though the 30 • views predicted by C-GAN and Pix2pixHD look alike real scenes, they fail to predict the correct angles of the scene. In Figure  7B , we can see clear differences among the different methods.
On the contrary, our model explicitly ports the camera pose into the network as 2D affine transformation on the feature space. For HoloGAN, it does not work on different datasets because it uses 3D convolution to explicitly discover the 3D representation. However, the real scene is not object centered. Since views from different angles describe different 3D structures, 3D convolution cannot uniformly project different scenes to a single 3D space for estimation. In contrast, our proposed See360 method learns to use equivalent 2D affine transform to estimate the 3D model for novel views of different angles.
E. Ablation Study In the ablation study, we consider the effect of three key components: 1) Cross patch correlation (CPC), 2) Multi-Scale Affine Transformer (MSAT) and 3) multi-scale style discriminator with or without semantic segmentation (SS). To make a comparison, we refer to these three situations to as CPC, MSAT and SS. In Table  II , we compute the evaluation metrics PSNR, SSIM and LPIPS on each dataset, to quantitatively measure the importance of their effects. In Table  II , "Full Pipeline" is the our complete See360 model. To test the three key components, we successively removed the key components from the full pipeline to train the model. We can find that the most important component is the use of Multi-Scale Affine Transformer (MSAT). The reason is that we can reconstruct the scene from coarse to fine to fill the details at different scales.
See360-indicates models using different combinations of losses and See360 indicates the best model that makes use of all three losses. We measured PSNR, SSIM and LPIPS on UrbanCity360 dataset. It can be found that using lap loss improves the SSIM because it computes structural similarity of images using the differences of Gaussians. Using pd loss and VGG loss improves the LPIPS but affects PSNR and SSIM scores because of the trade-off effect between pixel distortion and visual quality  [66]. Combining all three loss terms, See360 achieves the best LPIPS as well as the second best PSNR and SSIM, which indicates that our proposed See360 can well synthesize photo-realistic novel views.
F. Robust and Flexible 3D Awareness As discussed in Section II, our proposed See360 method can recognize different camera poses to synthesize unique   It inspires us that it has the potential to be used for 360 • scenery video. We provide a video demo at the link https://youtu. be/P1JHx7ViSpI. 2  Please note the smooth motion transition and view changes. There could be some blurry effects or distortions due to sudden big structural changes or additional noises on the reference images. Visual quality can be further improved by using larger datasets and image preprocessing. To test the limit of the See360 model, we also investigated whether it can use references with a larger angle distance, while still predicting the novel view correctly. We used UrbanCity360 as an example to show the results of using references with different τ in Figure  9 , rather than always using τ = 60 •.
That is, we directly use our pre-trained model on new scenes without any finetuning. We collected real-world datasets to evaluate such versatility for the proposed See360 model. Since real-world datasets contain much fewer images for training, we firstly pretrained the model on virtual-world datasets and then fine-tuned them on the targeted real-world datasets for better prediction. As shown in Table  I , we only fine-tuned our models for 100 epochs to adjust to real-world datasets, which required about 10 mins on a computer with one GPU. Figure  10  illustrates the robustness of See360 to different datasets. To further illustrate the robustness, we show the intermediate results on fine-tuning See360, using the HungHom360 dataset. With the training epochs increase, we can see that the visual quality is getting better, e.
G. Novel View Synthesis in the Wild So far, we presented evaluations on the few datasets on which the model was trained. In addition, the proposed model works well in the wild. For example, given two reference views of an unknown scene, we do not even need to know the angle between these left and right references: We represent each intermediate view as a unique one-hot code, so all we need is the one-hot code of the target camera pose. To illustrate this, we collected a few views inside a university by randomly rotating the camera and we used any two views as references to interpolate the middle view. E. g. , we input the one-hot camera pose code as "0000001000000" for prediction. Note again that we do not train on unknown scenes. We directly use the pre-trained UrbanCity360 and Arch-interior360 models for prediction.
As shown in Figure  11 , though UrbanCity360 and Archinterior360 are virtual-world datasets, we can find that both models can render novels view with similar structures. Another interesting finding is that using UrbanCity360 model can achieve better results than Archinterior360. The possible reason is that UrbanCity360 is also an outdoor dataset with diverse contents including buildings and trees. Therefore, it is a better match for unknown outdoor scenes. However, when we have photos with views that highly differ from our training data, like photos taken from other countries, the proposed See360 model may not work properly. Since we cannot actually go around different places to test Visualization of view synthesis on unknown scenes captured inside a campus. We do not train the model but directly use two pre-trained models for estimation: 1) pre-trained UrbanCity360 model and 2) pre-trained Archinterior360 model.
V. CONCLUSION In this paper, we open up a new research direction of using neural rendering to generate views at different viewing angles from only two overlapping input images, a key contribution helping people understand their surroundings. Our novel view synthesis method, called See360, builds on two carefully designed components: 1) Multi-Scale Affine Transformer (MSAT) and 2) Conditional Latent space AutoEncoder (C-LAE). The key insight is that we transfer 3D view rendering as an equivalent 2D affine transformation. We contribute further by providing two types of datasets, respectively consisting of synthetic and real images, for training and evaluation. Our See360 model has the potential to be used in 360 • video processing for virtual reality. In future work, we plan to explore the extension of See360 to combine both 2D and 3D information for high-resolution images/videos rendering.
COMPARISON OF THE PROPOSED SEE360 AND OTHER NOVEL VIEW RENDERING NETWORKS ON 4 DATASETS, INCLUDING PSNR (dB), SSIM, LPIPS, PIXEL ACCURATE(%) AND RUNNING TIME (S/IMG). FOR PSNR, SSIM AND PIXEL ACCURACY, HIGHER VALUES MEAN BETTER RESULTS. FOR LPIPS AND RUNNING TIME, LOWER VALUES MEAN BETTER RESULTS Fig. 7. Visual comparison among different methods on virtual-world and real-world datasets.
École Polytechnique ,  The Hong Kong Polytechnic University , and the  Caritas Institute of Higer Education, Hong Kong, SAR, under University Grants Committee (UGC ), under Grant  UGC/IDS(C)11/E01/20  (manpower) and Grant IDS(R) 11/19  (high-performance computing facility). The associate editor coordinating the review of this manuscript and approving it for.
We present Monte Carlo simulations of radiative transfer within the absorbers of X-ray microcalorimeters, utilizing a numerical model for the photon propagation and photon absorption process within the absorber structure. In our model, we include effects of Compton scattering off bound electrons and fluorescence. Scattered or fluorescence photons as well as Auger and photoelectrons escaping the absorber can result in partial energy depositions. By implementing a simplified description of the physical processes compared to existing comprehensive particle transport software frameworks, our model aims to provide representative results at a small computational effort. This approach makes it possible to use our model for quick assessments, parametric studies, and application in other Monte Carlo-based instrument simulators like SIXTE, a software package for X-ray astronomical instrumentation.
The model is developed within the SIXTE (SImulation of X-ray TElescopes) software package  1   [3] , a Monte Carlo simulation toolkit for X-ray astronomical instrumentation, and xifusim  [4] , a dedicated simulator for the X-IFU microcalorimeter instrument  [5]  of the future Athena X-ray observatory  [6]. Our model will be made available in a future release version of the SIXTE package. Compared to existing general purpose, but computationally expensive, particle transport codes, as the Geant4 toolkit  [7] , the goal of our model is to provide representative distributions with minimal overhead and fast computation times. The model emerged from the need for a more realistic description of the involved physical processes in the SIXTE and xifusim simulators while keeping the impact on run time minimal.
The histograms are binned using one of the latest Athena X-IFU ancillary response files. The bottom panels compare the cumulative sums of the relative frequencies obtained with the Geant4 package and our model. Generally, the resulting escape peaks and continuum match well. The electron-loss continuum is slightly stronger in the Geant4 simulation. Additional emission features in the Geant4 data, emerging from escaping photons detected in neighboring pixels, are yet missing in our calculation (Color figure online) Carlo-based simulators like SIXTE and xifusim with only a small overhead. As one of the following steps, we will compare our results with measured data. We also want to further investigate the slight remaining difference to the Geant4 simulation and refine our model accordingly. We also plan to include a more accurate treatment of the thermalization mechanism if possible without negatively impacting the run time.
The polymerization and crystallization kinetic models parameters follow the identification conducted by Vicard et al. [21]  on their experimental DSC curves obtained with the calorimetric method. [5] The Hillier coupling method is then compared with a new coupling approach. Afterward, few key parameters have been readjusted to account for the different modeling methods and more importantly, the variability of the experimental measurements. Finally, the model is integrated in OpenFOAM ® , an open-source software, to compute an injection simulation where the influence of the reaction on the overall crystallinity is assessed.
| Optimization method Determination of the model parameters has been conducted using MATLAB ® Optimization Toolbox™ and MATLAB ® Global Optimization Toolbox. It was observed that iterative methods such as the quasi-Newton method or the least square method tend to get trapped in local minima when working with the crystallization peak, presumably because of nonlinearities and Heaviside function usage. Consequently, when these local minima needed to be avoided, the pattern search method was preferentially used to minimize constrained objective functions. When possible, B 0 was determined using the polymerization dominant spike by comparing the maximum heat flow at the relevant time using the half-time of the synthesis t 50% (Q t 50% ð Þ¼0:5 Ã Q tot Þ. Then t i and a i were obtained by minimizing the difference between the experimental and the simulated enthalpy at each point of the reaction.
Surface rupturing data from the historical earthquakes is used for obtaining empirical regression parameters for fault displacement hazard assessment. This paper represents an additional compilation and analysis effort, extending the first version of the SUrface Ruptures due to Earthquake (SURE) database. This new release contains slip measurements and mapped surface rupture traces of 50 surface rupturing earthquakes of reverse, normal, and strike-slip kinematics occurred all over the world between 1872 and 2019. As a novelty, a ranking scheme of the rupture features is applied to all the traces and slip measurements in the database. Fault ranking introduces geology as a primary analysis tool and allows the end user to obtain regression parameters suitable for the specific geological conditions at the site of interest.
The first release of the SUrface Ruptures due to Earthquakes (SURE, hereafter SURE 2020) database was published in 2020  2. This was the first attempt to gather the surface rupturing data of historical and modern earthquakes into one unified and homogenous dataset. This kind of data is essential for analyses aimed at estimating the fault displacement related hazard in areas and for infrastructure located near and/or on active fault zones. Fault displacement hazard analysis is used for estimating the occurrence of co-seismic fault displacement at the ground surface, and the approach is based on regression parameters describing the spatial distribution of amount and occurrence of surface rupturing. The strength of such a database is in the wealth of good quality data organized homogenously, and more the events are included, the more accurately the eventual hazard calculations and the corresponding uncertainties can be estimated.
SURE database is also an initiative for establishing a standardized format for reporting the surface-faulting data, as the slip components of historical earthquakes have been communicated in miscellaneous ways. Thus, we have put effort into converting the reported data from various sources into a new, updated SURE 2. 0 format. The database with slip components has been designed to be applicable to any geological setting, and one of the purposes of forming such a structure has been its usability in future field working so that the slip components would be measured and reported directly in a format that is compatible with other such datasets. This new release of the database, SURE 2. 0  3  , is a revised version both by its structure and the events included. Compared to the previous release, several updates and enhancements have been done.
Fault mapping accuracy has increased in most cases, and as a major novelty, fault ranking has been applied to all the events. Fault ranking introduces geological knowledge to the database by distinguishing surface ruptures as a function of the causative geological structure, more of which in the following chapter. This enables obtaining regression parameters based on categorization of surface faulting. We have also added several new parameters into the database, and the slip observation points have been linked to the corresponding ruptures in the shapefile with their identification parameter. The overall homogeneity of the initial dataset (SURE 2020) has been re-evaluated and some cases have been left out from this release based on quality selection criteria. In order to ensure regular updating, data can be found on Zenodo, and the scientific community is invited to interact and implement the database by suggesting new data using the form available in the Zenodo repository  3.