id string | figure_id string | figure_type string | paper_title string | authors string | arxiv_id string | category string | year string | license string | citation string | original_caption string | small_caption string | medium_caption string | large_caption string | extracted_text string | text_score float64 | clip_score float64 | composite_score float64 | image_width int64 | image_height int64 | aspect_ratio float64 | image_hash string | image string | pdf_link string |
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arxiv_0000001 | Figure 1 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 1: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-BN+SH and the
softmax baseline trained on retrieval task for different model dimensions and sequence lengths. The reduction in
computation and memory when using our models improves with sequence length. When scaling up the model,
our me... | A diagram of a series of different. | A detailed layout showing the differences between sequence length and distance of a training course or testimore. | A comprehensive technical explanation of the difference between a training and sequence length of each exercise, using the same method as described by the figure below, and the data in the table below, 1 / 2 / 3 / 6 / 4. | Figure 1: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-BN+SH and the
softmax baseline trained on retrieval task for different model dimensions and sequence lengths. The reduction in
computation and memory when using our models improves with sequence length. When scaling up the model,
our me... | 0.81 | 0.3181 | 0.5641 | 1,920 | 960 | 2 | 74ca7c78a79ad2c717fd16b17f6ce8de | images/2024/arxiv_0000001.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000002 | Figure 2 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 1: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-BN+SH and the
softmax baseline trained on retrieval task for different model dimensions and sequence lengths. The reduction in
computation and memory when using our models improves with sequence length. When scaling up the model,
our me... | A diagram of the number of different. | A detailed layout showing the different stages of the cell phone ' s battery lifecycle from 1900 to 2000. | A comprehensive technical explanation of the different types of the cell phone ' s battery lifecycle from the first to the second generation of cellular phones, including the new and older models, 1970 - 2012 and present in the first half. | Figure 1: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-BN+SH and the
softmax baseline trained on retrieval task for different model dimensions and sequence lengths. The reduction in
computation and memory when using our models improves with sequence length. When scaling up the model,
our me... | 0.725 | 0.2113 | 0.4681 | 714 | 201 | 3.552 | 8edc6d8b507203cde72be8aca4cee9db | images/2024/arxiv_0000002.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000003 | Figure 3 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 2: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-SH and the softmax
attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths. | A diagram of the stages in testing and. | A detailed layout showing the differences between the sequence length and the training time for a dog, and the distance of the training. | A comprehensive technical explanation of the two basic training curves for the b - testing and sequence lengths of each one source image from the department of the university of applied materials of engineering, 2013 / eocnepto /. | Figure 2: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-SH and the softmax
attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths. | 0.81 | 0.3023 | 0.5562 | 1,920 | 960 | 2 | dd7dd292bbe0e10ea6db12d7aaf40bea | images/2024/arxiv_0000003.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000004 | Figure 4 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 3: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-BN+SH and
the Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence
lengths. When using our models, the reduction in computation and memory improves with sequence length.
When sca... | A diagram of the different stages of a. | A detailed layout showing the differences between sequence length and training time in the training system, with a single line graph. | A comprehensive technical explanation of the sequence length and training of a dog ' s teeth and teeth, with their corresponding corresponding lengths and the same length as shown below diagram in the following data graph above the chart below each. | Figure 3: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-BN+SH and
the Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence
lengths. When using our models, the reduction in computation and memory improves with sequence length.
When sca... | 0.87 | 0.3135 | 0.5917 | 1,920 | 960 | 2 | e9b8091a6b5ce7d36a569ad03198035a | images/2024/arxiv_0000004.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000005 | Figure 5 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 4: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-SH and the
Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths.
When using our models, the reduction in computation and memory improves with sequence length. When
scalin... | A diagram of the different stages of a. | A detailed layout showing the differences between a training and sequence length of the results in a single file. | A comprehensive technical explanation of the sequence length and distance of a training course for a specific course, and a specific training course, as described by the following the data file file from the data source to the data editor ' s. | Figure 4: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-SH and the
Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths.
When using our models, the reduction in computation and memory improves with sequence length. When
scalin... | 0.87 | 0.319 | 0.5945 | 1,920 | 960 | 2 | 30ed925affa7bab4353c8b3a900f5be4 | images/2024/arxiv_0000005.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000006 | Figure 6 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 3: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-BN+SH and
the Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence
lengths. When using our models, the reduction in computation and memory improves with sequence length.
When sca... | A diagram of the differences between. | A detailed layout showing the differences between the sequence length and the training time for each of the two teams. | A comprehensive technical explanation of the sequence length and technique used in this training course, including the following steps for each step, with the following one line graphed up at the same time of the following stages at the teste. | Figure 3: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-BN+SH and
the Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence
lengths. When using our models, the reduction in computation and memory improves with sequence length.
When sca... | 0.81 | 0.2885 | 0.5493 | 1,920 | 960 | 2 | 7d412f7765289b832b4c272efe6e722e | images/2024/arxiv_0000006.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000007 | Figure 7 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 4: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-SH and the
Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths.
When using our models, the reduction in computation and memory improves with sequence length. When
scalin... | A diagram of the sequence length and. | A detailed layout showing the difference of a training and sequence length for each course, and the same number of training. | A comprehensive technical explanation of the sequence length and training of a dog using a computer screen, and a graphed line graph on a white background with data from the same time and the same height as shown in each line,. | Figure 4: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-SH and the
Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths.
When using our models, the reduction in computation and memory improves with sequence length. When
scalin... | 0.81 | 0.2759 | 0.543 | 1,920 | 960 | 2 | ffb613e48d3e31fd0453e2efcf94c8cc | images/2024/arxiv_0000007.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000008 | Figure 8 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 2: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-SH and the softmax
attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths. | A diagram of different types of the. | A detailed layout showing the different levels of the light intensity of the leds and their corresponding brightness. | A comprehensive technical explanation of the different types of the data processing system for each type of machine, from 1 to 8 years old and up to 7 years old, in the same stages of development, with the same time,. | Figure 2: (Left) FLOPS ratios and (Right) memory usage ratios between the Attention-SH and the softmax
attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths. | 0.75 | 0.2171 | 0.4835 | 754 | 212 | 3.557 | 478ea0df619f7cc792cd3d761ef4e25e | images/2024/arxiv_0000008.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000009 | Figure 9 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 3: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-BN+SH and
the Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence
lengths. When using our models, the reduction in computation and memory improves with sequence length.
When sca... | A diagram of the different stages of. | A detailed layout showing the number of training steps for each type of trainer at different stages of training. | A comprehensive technical explanation of the different levels of training and training for each individual person in a sports team, including a running coach, a squaturic, a training, a squashing, and a - kayaking,. | Figure 3: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-BN+SH and
the Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence
lengths. When using our models, the reduction in computation and memory improves with sequence length.
When sca... | 0.81 | 0.221 | 0.5155 | 754 | 200 | 3.77 | 37bc31f53271d3253df5cd92bd7f870b | images/2024/arxiv_0000009.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000010 | Figure 10 | scientific_figure | A Primal-Dual Framework for Transformers and Neural Networks | arXiv Research Authors | 2406.13781v1 | cs | 2024 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2024). A Primal-Dual Framework for Transformers and Neural Networks. arXiv:2406.13781v1 | Figure 4: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-SH and the
Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths.
When using our models, the reduction in computation and memory improves with sequence length. When
scalin... | A diagram of the stages of a cell. | A detailed layout showing the different stages of a proteining process, including the different levels of protein. | A comprehensive technical explanation of the different levels of heat in the world ' s most radioactive - like substances for the first time, and how much is that it actually changed??? - - - based on - page - page. | Figure 4: (Left) FLOPS ratios and (Right) memory usage ratios between the Sparse Attention-SH and the
Sparse Attention baseline trained on the LRA retrieval task for different model dimensions and sequence lengths.
When using our models, the reduction in computation and memory improves with sequence length. When
scalin... | 0.75 | 0.2323 | 0.4911 | 754 | 204 | 3.696 | 581e4eef55dcfa64e765158cfacc090e | images/2024/arxiv_0000010.png | https://arxiv.org/pdf/2406.13781v1.pdf |
arxiv_0000011 | Figure 11 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figures and captions | A diagram of the process for a camera. | A detailed layout showing the various components of a laser machine and its equipment, including the main parts. | A comprehensive technical explanation of a micro - laser system for the use of a microscope and other instruments, including a small camera, a microscope, and a computer and a large screen monitor and a microscope are shown in the same. | Figures and captions | 0.75 | 0.2861 | 0.5181 | 1,349 | 1,461 | 0.923 | 3b4f4e6cc81e93c3d0010f11f68b04d5 | images/2025/arxiv_0000011.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000012 | Figure 12 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 1. (a) Schematic layout: Overview of the snapshot multi-spectral imaging system,
illustrating the arrangement of the circular LED array, Digital Micromirror Device (DMD),
optical components (collimator, diffuser, lenses), and the monochrome imaging camera. (b)
Experimental set-up. | A diagram of a camera and a computer. | A detailed layout showing the components of a robotic arm and equipment for the project, including a camera. | A comprehensive technical explanation of a large scale laser machine, with its components labeled and labelled in english and english, for the use of engineers and technicianss to study the area of their needs on the market and customers. | Figure 1. (a) Schematic layout: Overview of the snapshot multi-spectral imaging system,
illustrating the arrangement of the circular LED array, Digital Micromirror Device (DMD),
optical components (collimator, diffuser, lenses), and the monochrome imaging camera. (b)
Experimental set-up. | 0.7357 | 0.3655 | 0.5506 | 1,224 | 1,155 | 1.06 | 4fef3b79b0d4553799783caf67825617 | images/2025/arxiv_0000012.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000013 | Figure 13 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 2. (a-b) Visualizations of image reconstruction results of two test image patterns
illuminated under different wavelengths. Each column refers to an image with the monochrome
defocused sensor input, network output and multi-spectral target (ground truth). Pseudo colors
represent the corresponding illumination... | A diagram of various colors of. | A detailed layout showing different color schemes of the same pattern in various colors, including red, green, blue, and yellow. | A comprehensive technical explanation of the different color combinations of the multi - colored samples in the image below, from the first to the next two years of this time of the year, and then the same period, the same time. | Figure 2. (a-b) Visualizations of image reconstruction results of two test image patterns
illuminated under different wavelengths. Each column refers to an image with the monochrome
defocused sensor input, network output and multi-spectral target (ground truth). Pseudo colors
represent the corresponding illumination... | 0.75 | 0.2312 | 0.4906 | 1,371 | 1,106 | 1.24 | eaed630072830ba41f5d086ac64eaf9a | images/2025/arxiv_0000013.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000014 | Figure 14 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 2. (a-b) Visualizations of image reconstruction results of two test image patterns
illuminated under different wavelengths. Each column refers to an image with the monochrome
defocused sensor input, network output and multi-spectral target (ground truth). Pseudo colors
represent the corresponding illumination... | A diagram of the different color. | A detailed layout showing the different color variations of each cell phone, in different colors and sizes,. | A comprehensive technical explanation of the multicolored structure of the cell membranes of the human and verteopia of the cellular cell membrane - based on this sample, weltronomic model is shown below the image. | Figure 2. (a-b) Visualizations of image reconstruction results of two test image patterns
illuminated under different wavelengths. Each column refers to an image with the monochrome
defocused sensor input, network output and multi-spectral target (ground truth). Pseudo colors
represent the corresponding illumination... | 0.7357 | 0.242 | 0.4889 | 1,018 | 823 | 1.237 | bbfe75c8b96da20c04731d6389948e97 | images/2025/arxiv_0000014.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000015 | Figure 15 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 3. Visualization of image reconstruction results under various illumination
configurations. Each column displays the predicted image alongside the corresponding target
(ground truth) image for different combinations of activated LEDs that were on. | A diagram of the different types of. | A detailed layout showing the different properties of the structure of the cell phone, including the signal and the signal. | A comprehensive technical explanation of the different types of light in a photograph of a multicolored background, with the following colors and the same number of light that is visible in the image, and red, black, red,. | Figure 3. Visualization of image reconstruction results under various illumination
configurations. Each column displays the predicted image alongside the corresponding target
(ground truth) image for different combinations of activated LEDs that were on. | 0.75 | 0.269 | 0.5095 | 1,440 | 941 | 1.53 | 88c02e349f7ae008c22f13324ce7dbaf | images/2025/arxiv_0000015.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000016 | Figure 16 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 3. Visualization of image reconstruction results under various illumination
configurations. Each column displays the predicted image alongside the corresponding target
(ground truth) image for different combinations of activated LEDs that were on. | A diagram of the different colors of a. | A detailed layout showing the different effects of different colors in the image and how they are used to create them. | A comprehensive technical explanation of the different effects of color in photoshopped images, from a series of images with varying color schemes, to a series with the same image and texture and size, each color scheme, each. | Figure 3. Visualization of image reconstruction results under various illumination
configurations. Each column displays the predicted image alongside the corresponding target
(ground truth) image for different combinations of activated LEDs that were on. | 0.75 | 0.3012 | 0.5256 | 937 | 620 | 1.511 | aca53fa72a09f79a2c151051556dc5c4 | images/2025/arxiv_0000016.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000017 | Figure 17 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 4. Image reconstruction performance analysis. (a) Confusion matrix of spectral channel
accuracy. (b) Classification metrics against the number of concurrent LED illuminations: Plots of
sensitivity, specificity, and F1 score against the number of concurrent LED illuminations. (c)
Energy difference values as a ... | A diagram of the different types of. | A detailed layout showing the effects of the different types of energy in the environment and how it affects. | A comprehensive technical explanation of the different effects of different types of energy in a system that is not very efficient, and the results are very attractive to us $ 3, $ 5, 000, 000 / $ 1, 000. | Figure 4. Image reconstruction performance analysis. (a) Confusion matrix of spectral channel
accuracy. (b) Classification metrics against the number of concurrent LED illuminations: Plots of
sensitivity, specificity, and F1 score against the number of concurrent LED illuminations. (c)
Energy difference values as a ... | 0.7393 | 0.2537 | 0.4965 | 1,440 | 1,235 | 1.166 | 359fed75205db9382f5268b5bfd69771 | images/2025/arxiv_0000017.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000018 | Figure 18 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 4. Image reconstruction performance analysis. (a) Confusion matrix of spectral channel
accuracy. (b) Classification metrics against the number of concurrent LED illuminations: Plots of
sensitivity, specificity, and F1 score against the number of concurrent LED illuminations. (c)
Energy difference values as a ... | A diagram of the different types of. | A detailed layout showing the different types of energy that are present in the same region of the earth. | A comprehensive technical explanation of the energy and energy - generating process in the environment of a city, including energy - efficient buildings, solar - powered buildings, and wind - driven - driven systems, and - driven power - off -. | Figure 4. Image reconstruction performance analysis. (a) Confusion matrix of spectral channel
accuracy. (b) Classification metrics against the number of concurrent LED illuminations: Plots of
sensitivity, specificity, and F1 score against the number of concurrent LED illuminations. (c)
Energy difference values as a ... | 0.725 | 0.2157 | 0.4703 | 974 | 833 | 1.169 | 692a32725c8ac88e9714e74741cfeb61 | images/2025/arxiv_0000018.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000019 | Figure 19 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 5. Distributions of (a) PSNR, (b) RMSE, (c) SSIM and (d) NMSE metrics for the
reconstructed images at different illumination wavelengths. | A diagram of the distribution of water. | A detailed layout showing the mean of the weight of each individual figure, from different data points to a number of different measurements. | A comprehensive technical explanation of the hypothe - and - weight distribution for different types of water, including the following three phases of the cycle of the process and the process of the hydro cycle, the process,. | Figure 5. Distributions of (a) PSNR, (b) RMSE, (c) SSIM and (d) NMSE metrics for the
reconstructed images at different illumination wavelengths. | 0.81 | 0.2684 | 0.5392 | 1,371 | 789 | 1.738 | 8c075c515dee190e59857e8224a38bd4 | images/2025/arxiv_0000019.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000020 | Figure 20 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 5. Distributions of (a) PSNR, (b) RMSE, (c) SSIM and (d) NMSE metrics for the
reconstructed images at different illumination wavelengths. | A diagram of the different phases of a. | A detailed layout showing different types of the scatters and patterns for different areas in the region. | A comprehensive technical explanation of the glycolized datasete using the rx - 3 and rxp - 2 algorithms and rp - 4 - 6f - 6 - 5 - biting functions representing all details, specifications, and configurations of the system components in full. | Figure 5. Distributions of (a) PSNR, (b) RMSE, (c) SSIM and (d) NMSE metrics for the
reconstructed images at different illumination wavelengths. | 0.725 | 0.231 | 0.478 | 957 | 550 | 1.74 | 4d39a18ad8be903db5d87b7c24ed1f27 | images/2025/arxiv_0000020.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000021 | Figure 21 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 6. (a) Distributions of structural quality metrics for the reconstructed multi-spectral
images with respect to the number of concurrent LED illuminations. (b) Confusion matrices with
different numbers of concurrent LED illuminations. | A diagram of the number of people who. | A detailed layout showing the number of individual and individual patients in each hospital room, including a number of different. | A comprehensive technical explanation of the different types of water in the world ' s oceans and oceans, including the number of lakes, land, and land surface areas, and water, from sea level to sea level, and area. | Figure 6. (a) Distributions of structural quality metrics for the reconstructed multi-spectral
images with respect to the number of concurrent LED illuminations. (b) Confusion matrices with
different numbers of concurrent LED illuminations. | 0.75 | 0.2607 | 0.5053 | 1,371 | 1,465 | 0.936 | 4e072d790924a09b959dc272d431b47d | images/2025/arxiv_0000021.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000022 | Figure 22 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 6. (a) Distributions of structural quality metrics for the reconstructed multi-spectral
images with respect to the number of concurrent LED illuminations. (b) Confusion matrices with
different numbers of concurrent LED illuminations. | A diagram of the number and type of. | A detailed layout showing the number of different types of cell phone phones, with the same number of cellular phones. | A comprehensive technical explanation of the results of the four different stages of the experiment in the following diagrams, with the following results of each one in the previous model image and the following steps on the next slide below slides. | Figure 6. (a) Distributions of structural quality metrics for the reconstructed multi-spectral
images with respect to the number of concurrent LED illuminations. (b) Confusion matrices with
different numbers of concurrent LED illuminations. | 0.75 | 0.2405 | 0.4952 | 957 | 992 | 0.965 | 92f63691f0cb4daeb06259f7123ef76c | images/2025/arxiv_0000022.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000023 | Figure 23 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 7. Network architecture. (a) Multi-spectral Fourier Imager Network (mFIN). (b) Dense
links: each output tensor of the dSPAF group is appended and fed to the subsequent one. (c)
Detailed schematic of dSPAF modules. See the Methods section for details. | A diagram of the process of the. | A detailed layout showing the process of the application for a remote control system with different functions and processes. | A comprehensive technical explanation of the application of an aspx / gdsm - based system for a remote access network in a remote area of a remote control system, with remote access and remote control systems, from a remote. | Figure 7. Network architecture. (a) Multi-spectral Fourier Imager Network (mFIN). (b) Dense
links: each output tensor of the dSPAF group is appended and fed to the subsequent one. (c)
Detailed schematic of dSPAF modules. See the Methods section for details. | 0.75 | 0.2499 | 0.5 | 1,440 | 667 | 2.159 | f9b32c7898a536895feffca4d41f82eb | images/2025/arxiv_0000023.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000024 | Figure 24 | scientific_figure | Snapshot multi-spectral imaging through defocusing and a Fourier imager network | arXiv Research Authors | 2501.14287v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Snapshot multi-spectral imaging through defocusing and a Fourier imager network. arXiv:2501.14287v1 | Figure 7. Network architecture. (a) Multi-spectral Fourier Imager Network (mFIN). (b) Dense
links: each output tensor of the dSPAF group is appended and fed to the subsequent one. (c)
Detailed schematic of dSPAF modules. See the Methods section for details. | A diagram of the process of a computer. | A detailed layout showing the use of the sdr - s and the application for the sdrs that shows key features, attributes, and data points in detail for. | A comprehensive technical explanation of the application of the p2p - 5 module for the application to the new p2psp - 4 module, based on - 5p - 3p - 6p - p2 - 8p representing all details, specifications, and configurations of the system components. | Figure 7. Network architecture. (a) Multi-spectral Fourier Imager Network (mFIN). (b) Dense
links: each output tensor of the dSPAF group is appended and fed to the subsequent one. (c)
Detailed schematic of dSPAF modules. See the Methods section for details. | 0.81 | 0.2693 | 0.5396 | 957 | 449 | 2.131 | f38d365511ca98015aeeb9745912912d | images/2025/arxiv_0000024.png | https://arxiv.org/pdf/2501.14287v1.pdf |
arxiv_0000025 | Figure 25 | scientific_figure | Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms | arXiv Research Authors | 2502.00234v2 | cs | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms. arXiv:2502.00234v2 | Figure 1: Left: Application of the uniformization algorithm to discrete diffusion models for text
generation. The x-axis denotes the time of the backward process, and the y-axis denotes the
frequency of jumps (NFE). Perplexity convergence occurs before the NFE grows unbounded. Right:
Comparison between τ-leaping and th... | A diagram of a computer screen showing. | A detailed layout showing the differences between the three different types of the model and the same type of model. | A comprehensive technical explanation of the different types of neural processing techniques for a computer application, including the following data and the following results of the process in the same process - time of the image, from the. | Figure 1: Left: Application of the uniformization algorithm to discrete diffusion models for text
generation. The x-axis denotes the time of the backward process, and the y-axis denotes the
frequency of jumps (NFE). Perplexity convergence occurs before the NFE grows unbounded. Right:
Comparison between τ-leaping and th... | 0.81 | 0.202 | 0.506 | 782 | 262 | 2.985 | ffb833c014022b869127c3255c9e4423 | images/2025/arxiv_0000025.png | https://arxiv.org/pdf/2502.00234v2.pdf |
arxiv_0000026 | Figure 26 | scientific_figure | Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms | arXiv Research Authors | 2502.00234v2 | cs | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms. arXiv:2502.00234v2 | Figure 2: Empirical KL divergence between the true
and generated distribution of the toy model vs. number
of steps. Data are fitted with linear regression with 95%
confidence interval by bootstrapping. | A diagram of the number of steps. | A detailed layout showing the number of steps to be taken in a step - by - step process that shows key features, attributes, and data points in. | A comprehensive technical explanation of a step curve in the computer system, including the steps and the number of steps that will be taken to the computer machine for each step in the next step, and the process of the next time. | Figure 2: Empirical KL divergence between the true
and generated distribution of the toy model vs. number
of steps. Data are fitted with linear regression with 95%
confidence interval by bootstrapping. | 0.81 | 0.2789 | 0.5444 | 455 | 249 | 1.827 | 32569438aca37b8e7e692411d9885df9 | images/2025/arxiv_0000026.png | https://arxiv.org/pdf/2502.00234v2.pdf |
arxiv_0000027 | Figure 27 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 1: Reinforcement learning framework for adaptive control of the over-relaxation
parameter in LBGK. Each agent samples an action from its policy, with actions in-
terpolated across the grid and applied to the environment. Agents receive local state
observations and a global reward based on the alignment of the co... | A diagram of the process of a software. | A detailed layout showing the components of the system in which a sample action is generated to be completed. | A comprehensive technical explanation of the model for the experiment in which the data is stored and displayed in a flow diagram, as well as examples of the corresponding data in the image, the sequences and other elements, the actual. | Figure 1: Reinforcement learning framework for adaptive control of the over-relaxation
parameter in LBGK. Each agent samples an action from its policy, with actions in-
terpolated across the grid and applied to the environment. Agents receive local state
observations and a global reward based on the alignment of the co... | 0.7393 | 0.2737 | 0.5065 | 1,352 | 1,126 | 1.201 | 88e80358066f9e7ecd0a4e0fc62e330a | images/2025/arxiv_0000027.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000028 | Figure 28 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 1: Reinforcement learning framework for adaptive control of the over-relaxation
parameter in LBGK. Each agent samples an action from its policy, with actions in-
terpolated across the grid and applied to the environment. Agents receive local state
observations and a global reward based on the alignment of the co... | A diagram of a group of different. | A detailed layout showing the flow of the process of an experiment, with the sequence and its corresponding components. | A comprehensive technical explanation of the algorithm for a single - action system, including the same function as the other two - step sequencer, is shown in figure - based on a computer simulations and a computer graphicsized image. | Figure 1: Reinforcement learning framework for adaptive control of the over-relaxation
parameter in LBGK. Each agent samples an action from its policy, with actions in-
terpolated across the grid and applied to the environment. Agents receive local state
observations and a global reward based on the alignment of the co... | 0.75 | 0.264 | 0.507 | 1,191 | 688 | 1.731 | 20156cdfe50f74af15c722a2c9160219 | images/2025/arxiv_0000028.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000029 | Figure 29 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 2: Example for a policy network parametrization in an environment with local
and cooperating agents. Figure 2a shows the network architecture for a fully local agent.
The agent only receives the state at its location which in this case is a six dimensional
vector. The network then consists of two fully convoluti... | A diagram of the three sections of an. | A detailed layout showing the two different sections of a building with two different angles and one side of it. | A comprehensive technical explanation of the optical system for scanning and viewing the image of a rectangled object from different angles in perspective and perspective, to which the image shows the image is not visible and the same image from. | Figure 2: Example for a policy network parametrization in an environment with local
and cooperating agents. Figure 2a shows the network architecture for a fully local agent.
The agent only receives the state at its location which in this case is a six dimensional
vector. The network then consists of two fully convoluti... | 0.75 | 0.2258 | 0.4879 | 893 | 219 | 4.078 | 132df63f8ab556450296baa400e9242a | images/2025/arxiv_0000029.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000030 | Figure 30 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 3: Neural network architecture for a centralized critic network used in the learning
phase by the MARL PPO algorithm. The Network receives a state S as input, which
is compressed by six convolutional and three fully connected layers, and outputs a value
function estimate V (S). | A diagram of a plane with a number of. | A detailed layout showing the various parts of a wooden structure and a line of wooden beams, with different sections. | A comprehensive technical explanation of the 3d image of a box and its contents and components in a single - dimensional view of it, which is an object that appears to be a box or something that is a box, that. | Figure 3: Neural network architecture for a centralized critic network used in the learning
phase by the MARL PPO algorithm. The Network receives a state S as input, which
is compressed by six convolutional and three fully connected layers, and outputs a value
function estimate V (S). | 0.75 | 0.2718 | 0.5109 | 777 | 236 | 3.292 | 5cac0cae195ecf684f44c8e79a4ec861 | images/2025/arxiv_0000030.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000031 | Figure 31 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 4: Evaluation of trained models on Kolmogorov flow at Re = 104. (4a) show the
vorticity correlation of models with the DNS. All three models are able to stabilize the
simulation. (4b) shows the energy spectra scaled by k5, averaged over the second half
of the simulation T ∈[113, 227]. All three models reproduce ... | A diagram of a graph showing the. | A detailed layout showing the results of different time - lapsing and multi - dimensional datas in the data. | A comprehensive technical explanation of the graphing process in the data visual system for the data modeling software, with the following data visual tool - based image and the data processing tool kit included in the following versions of the. | Figure 4: Evaluation of trained models on Kolmogorov flow at Re = 104. (4a) show the
vorticity correlation of models with the DNS. All three models are able to stabilize the
simulation. (4b) shows the energy spectra scaled by k5, averaged over the second half
of the simulation T ∈[113, 227]. All three models reproduce ... | 0.7957 | 0.2742 | 0.535 | 2,539 | 1,575 | 1.612 | fa7fc1eb7f7a6bab19773bbe3bc123ee | images/2025/arxiv_0000031.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000032 | Figure 32 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | A diagram of the average flow of water. | A detailed layout showing the distribution of the water vapors in the atmosphere and the temperature of the atmosphere. | A comprehensive technical explanation of the water cycle for the seawater in the uk and europe, from a data sheet for the waterworkser ' s perspective, 2012 - 2015 - 2016 - 06, with a - 20 - 07 representing all details, specifications, and. | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | 0.81 | 0.2484 | 0.5292 | 2,577 | 1,576 | 1.635 | bab79cc710f4f2deee1fafdb58737d08 | images/2025/arxiv_0000032.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000033 | Figure 33 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | A diagram of the number of people who. | A detailed layout showing the distribution of the number of different types of gas in the world ' s atmosphere. | A comprehensive technical explanation of the time series for the first half of 2009 - 2013, with a line graph of the same time and the last half of 2013 - 2013 - 2012 - 2016 - 2019 years, and the year. | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | 0.8064 | 0.2675 | 0.537 | 2,539 | 1,575 | 1.612 | bbb50b7d8968580ed8ea5f5e3cc5c0ce | images/2025/arxiv_0000033.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000034 | Figure 34 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | A diagram of the temperature curve of. | A detailed layout showing the average number of people in a group, and how they are using the data that shows key features, attributes, and data. | A comprehensive technical explanation of the mean on the watermark model for the current waveforms, with the following data of the time and volume of the stream - based data at the same time, as well as described. | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | 0.81 | 0.2517 | 0.5309 | 2,577 | 1,576 | 1.635 | 1c7fcba14479a01047fc82f2acb0df2f | images/2025/arxiv_0000034.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000035 | Figure 35 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 4: Evaluation of trained models on Kolmogorov flow at Re = 104. (4a) show the
vorticity correlation of models with the DNS. All three models are able to stabilize the
simulation. (4b) shows the energy spectra scaled by k5, averaged over the second half
of the simulation T ∈[113, 227]. All three models reproduce ... | A diagram of the same line and the. | A detailed layout showing the various stages of a wave and its characteristics in different directions, including the wave. | A comprehensive technical explanation of the data visual for a single - phase system of data visual in a single file system, with data visual of the same stage 3 / 4 / 3 / 3, 5 / 6 / 8 / 6 representing all details, specifications, and. | Figure 4: Evaluation of trained models on Kolmogorov flow at Re = 104. (4a) show the
vorticity correlation of models with the DNS. All three models are able to stabilize the
simulation. (4b) shows the energy spectra scaled by k5, averaged over the second half
of the simulation T ∈[113, 227]. All three models reproduce ... | 0.75 | 0.2251 | 0.4875 | 1,191 | 458 | 2.6 | 2656148faef68afe6eb2561b0d287ea3 | images/2025/arxiv_0000035.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000036 | Figure 36 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | A diagram of a curve and a line graph. | A detailed layout showing the differences between the two data fields on this model and the same data field. | A comprehensive technical explanation of the data visual data for the two different systems, including the data and the data processing process and the information processing process on the same one line graphing process is also the same line with. | Figure 5: Evaluation of trained models on an unforced decaying flow at Re = 104.(5a)
show the vorticity correlation of models with the DNS. (5b) shows the energy spectra
scaled by k5, averaged over the second half of the simulation T ∈[113, 227]. | 0.7957 | 0.2438 | 0.5197 | 1,191 | 365 | 3.263 | fa1162b0549c469d7bb23fd5135cd812 | images/2025/arxiv_0000036.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000037 | Figure 37 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 6: Evaluation of trained models on Kolmogorov flow at Re = 105. (6a) show the
vorticity correlation of models with the DNS. (6b) shows the energy spectra scaled by k5,
averaged over the second half of the simulation T ∈[113, 227]. | A diagram of the flow of water from a. | A detailed layout showing the various types of water vapors and their characteristics in the atmosphere of the earth. | A comprehensive technical explanation of the temperature curve and its relationship to the temperature of the water source, including the temperature, temperature, and temperature, in the water cycle, and heat area of the earth ' s surface ' s. | Figure 6: Evaluation of trained models on Kolmogorov flow at Re = 105. (6a) show the
vorticity correlation of models with the DNS. (6b) shows the energy spectra scaled by k5,
averaged over the second half of the simulation T ∈[113, 227]. | 0.75 | 0.2178 | 0.4839 | 2,577 | 1,576 | 1.635 | a93964e0c253144a245d0134ead3e551 | images/2025/arxiv_0000037.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000038 | Figure 38 | scientific_figure | Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning | arXiv Research Authors | 2504.14422v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. arXiv:2504.14422v1 | Figure 7: Comparison of the vorticity fields of all models on the three test cases: Kol-
mogorov flow at Re = 104 (7a), decaying unforced flow at Re = 104 (7b), and Kolmogorov
flow at Re = 105 (7c). | A diagram of the image shows a variety. | A detailed layout showing the formation of a large group of cells that are in a single celled cell that shows key features, attributes, and data. | A comprehensive technical explanation of the structure and function of a cell membranes, including its surface and the number of cells in the cells, as well as described by the following the image, the image is shown. | Figure 7: Comparison of the vorticity fields of all models on the three test cases: Kol-
mogorov flow at Re = 104 (7a), decaying unforced flow at Re = 104 (7b), and Kolmogorov
flow at Re = 105 (7c). | 0.81 | 0.2562 | 0.5331 | 1,416 | 2,030 | 0.698 | 7d128eb2d68e2803e6ad22840f50abac | images/2025/arxiv_0000038.png | https://arxiv.org/pdf/2504.14422v1.pdf |
arxiv_0000039 | Figure 39 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 1. Illustration of the three force-generation strategies for a sin-
gle unimodal distribution (e.g., a bond length): Bottom panel: Three
points sampled from the data distribution. When atomistic forces
are available, they can be used directly in subsequent applications.
Middle panel: Each of the three samples is p... | A diagram of the different types of. | A detailed layout showing the motion of a wave in different stages, including a transverse noise kernel and a noise kernel. | A comprehensive technical explanation of the noise and noise in the sound waves of the sound source from the sound wave, the noise is the result of the amplitude of the waves in the noise moving waves of sound waves on the sound. | FIG. 1. Illustration of the three force-generation strategies for a sin-
gle unimodal distribution (e.g., a bond length): Bottom panel: Three
points sampled from the data distribution. When atomistic forces
are available, they can be used directly in subsequent applications.
Middle panel: Each of the three samples is p... | 0.75 | 0.3689 | 0.5595 | 479 | 748 | 0.64 | 9a4fa0ed63f3576d5cdde8b7196dd300 | images/2025/arxiv_0000039.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000040 | Figure 40 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 2. Ramachandran plots for alanine dipeptide for the different models trained on 10% of the training set. | A diagram of a green and blue. | A detailed layout showing the structure of a molecule with four arrows and two ends, and two red and one blue. | A comprehensive technical explanation of the structure of a hydroic compound, 3d - generated image courtesy university of technology and applied graphics, university of engineering, university at albany, 2013 - new hampshire, cambridge, london. | FIG. 2. Ramachandran plots for alanine dipeptide for the different models trained on 10% of the training set. | 0.7429 | 0.3208 | 0.5318 | 1,280 | 1,072 | 1.194 | b130da4aa72872133c488df0e4fe391d | images/2025/arxiv_0000040.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000041 | Figure 41 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 3. Comparison of global-feature accuracy (dihedral-angle distributions or TICA projections) versus local-feature accuracy (bond-length
distributions). Left column: The three benchmark systems and their CG representations: Alanine dipeptide, Chignolin, and Trp-cage. Each
row corresponds to one system, with panels s... | A diagram of a 3d model of an animal. | A detailed layout showing the structure of a cell phone with multiple colored balls and needles attached to it. | A comprehensive technical explanation of the 3d model of an anti - cell system, with multiple structures and a multicolored structure, including the nucleus - platelets and the same colored spheres on the outer surface as well as. | FIG. 3. Comparison of global-feature accuracy (dihedral-angle distributions or TICA projections) versus local-feature accuracy (bond-length
distributions). Left column: The three benchmark systems and their CG representations: Alanine dipeptide, Chignolin, and Trp-cage. Each
row corresponds to one system, with panels s... | 0.7464 | 0.2562 | 0.5013 | 1,280 | 960 | 1.333 | a8a13557afffd30340745c31a358eae9 | images/2025/arxiv_0000041.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000042 | Figure 42 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 2. Ramachandran plots for alanine dipeptide for the different models trained on 10% of the training set. | A diagram of the structure of a green. | A detailed layout showing the structure of a multicolored liquid molecule, with multiple small metal structures. | A comprehensive technical explanation of the mechanism of a multi - dimensional molecule, based on the structure of a protein cell, and its structural properties, including the structure, and functions, and function, and processes, together,. | FIG. 2. Ramachandran plots for alanine dipeptide for the different models trained on 10% of the training set. | 0.75 | 0.3204 | 0.5352 | 1,280 | 1,072 | 1.194 | 36fad7a42455d9535b85f0dad993a0a6 | images/2025/arxiv_0000042.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000043 | Figure 43 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 2. Ramachandran plots for alanine dipeptide for the different models trained on 10% of the training set. | A diagram of the temperature and. | A detailed layout showing the various types of waves in different directions, and their corresponding colors and sizes. | A comprehensive technical explanation of the high - resolution image of a waveformer ' s speed and time of the waveformers in a single - stream, with different waves and several different stages of motion, each waveforms. | FIG. 2. Ramachandran plots for alanine dipeptide for the different models trained on 10% of the training set. | 0.75 | 0.2398 | 0.4949 | 854 | 184 | 4.641 | ff2bb5569e0b423ea74feb63b807506d | images/2025/arxiv_0000043.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000044 | Figure 44 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 3. Comparison of global-feature accuracy (dihedral-angle distributions or TICA projections) versus local-feature accuracy (bond-length
distributions). Left column: The three benchmark systems and their CG representations: Alanine dipeptide, Chignolin, and Trp-cage. Each
row corresponds to one system, with panels s... | A diagram of the different types of. | A detailed layout showing the different types of data for different areas of the body and their characteristics, including. | A comprehensive technical explanation of the different types of the data stored in the data storage area of a computer system, including data storage and data storage for all data storages and storages, including the datas, as well. | FIG. 3. Comparison of global-feature accuracy (dihedral-angle distributions or TICA projections) versus local-feature accuracy (bond-length
distributions). Left column: The three benchmark systems and their CG representations: Alanine dipeptide, Chignolin, and Trp-cage. Each
row corresponds to one system, with panels s... | 0.75 | 0.2752 | 0.5126 | 760 | 598 | 1.271 | 8734dec9bd05d90c6e687bae66457df9 | images/2025/arxiv_0000044.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000045 | Figure 45 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 4. TICA projections for Chignolin for the different models trained on 10% of the training set. | A diagram of the different types of. | A detailed layout showing the distribution of the ionation of the plasmas in the human body and the number of the photon. | A comprehensive technical explanation of the time - laps for multiple images of a cell phone showing the different stages of cell phone life cycle and the same time of the cell phone - laps, as well as seen from the time. | FIG. 4. TICA projections for Chignolin for the different models trained on 10% of the training set. | 0.81 | 0.2413 | 0.5257 | 789 | 193 | 4.088 | 043df06d26db1afba78d491f8b9c5fe0 | images/2025/arxiv_0000045.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000046 | Figure 46 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 5. Top row: Distribution of minimized energies for backmapped CG configurations across the three benchmark systems, comparing all
models. Bottom row: Corresponding example bond distributions. | A diagram of different types of energy. | A detailed layout showing the various types of the different energy levels of an ion in the atmosphere and the corresponding. | A comprehensive technical explanation of the different types of chiromin and chiromine in the various phases of the cycle of chironin, chiroquin, chirominoid and chironon energy, and chirodonol representing all details, specifications, and. | FIG. 5. Top row: Distribution of minimized energies for backmapped CG configurations across the three benchmark systems, comparing all
models. Bottom row: Corresponding example bond distributions. | 0.75 | 0.2698 | 0.5099 | 956 | 464 | 2.06 | 8f17371ebf4a404dab91193439546ad5 | images/2025/arxiv_0000046.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000047 | Figure 47 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 6. Ramachandran plots for the AV and KS dipeptides, samples
generated with different models. The pretrained Timewarp model is
used to obtain forces via the transition kernel. | A diagram of the different types of. | A detailed layout showing the differences between the two phases of the h2h2 and h3h5 that shows key features, attributes, and data points in detail. | A comprehensive technical explanation of the time - laps of an experiment using the tim - t2s and the timeframes t4s - t5s t7s t3s t1s t9s representing all details, specifications, and configurations of the system components in full context with. | FIG. 6. Ramachandran plots for the AV and KS dipeptides, samples
generated with different models. The pretrained Timewarp model is
used to obtain forces via the transition kernel. | 0.81 | 0.2005 | 0.5052 | 495 | 272 | 1.82 | baaf205aed0047988640b147fc6c7fbc | images/2025/arxiv_0000047.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000048 | Figure 48 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 7. Ramachandran plots for alanine dipeptide at varying training-set sizes: top row 100% of the data; middle row 10%; bottom row 2%.
Note that all Atomistic model simulations diverged at the 2% data level. | A diagram of the different phases of a. | A detailed layout showing the different types of heat and temperature for the area that is located on the surface. | A comprehensive technical explanation of the time series for the tms - 1 model and the tm - 3 model, including the time, time, and current timeframes, and timeframe, and temperatures, the time representing all details, specifications, and. | FIG. 7. Ramachandran plots for alanine dipeptide at varying training-set sizes: top row 100% of the data; middle row 10%; bottom row 2%.
Note that all Atomistic model simulations diverged at the 2% data level. | 0.75 | 0.2435 | 0.4968 | 956 | 448 | 2.134 | 1a90ebc70d48a136f3ef63fe7fb97824 | images/2025/arxiv_0000048.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000049 | Figure 49 | scientific_figure | Operator Forces For Coarse-Grained Molecular Dynamics | arXiv Research Authors | 2506.19628v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Operator Forces For Coarse-Grained Molecular Dynamics. arXiv:2506.19628v1 | FIG. 8. TICA plots for Chignolin at varying training-set sizes: top row 100% of the data; middle row 10%; bottom row 2%. | A diagram of the various types of. | A detailed layout showing the different stages of the human brain and how it is used to study the different areas. | A comprehensive technical explanation of the results of a multi - dimensional pattern of human eye and nose tissues, from the study of the study for the study in the study on - to the study atc, jl representing all details, specifications, and. | FIG. 8. TICA plots for Chignolin at varying training-set sizes: top row 100% of the data; middle row 10%; bottom row 2%. | 0.75 | 0.1812 | 0.4656 | 956 | 412 | 2.32 | 3b17b3e39201ea16c17d760ffb6cb885 | images/2025/arxiv_0000049.png | https://arxiv.org/pdf/2506.19628v1.pdf |
arxiv_0000050 | Figure 50 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 1. Representative results of our method (PnP-CM) across a diverse set of inverse problems. Left to right: linear inverse problems
(Gaussian deblurring, super-resolution, inpainting) and nonlinear inverse problems (JPEG artifact removal, nonlinear deblurring, phase
retrieval), all with additive Gaussian noise σ =... | A diagram of the images of people and. | A detailed layout showing a group of people who are smiling and having different faces on them in different colors. | A comprehensive technical explanation of the differences between multiple personalitys and what they are supposed to be, including the same face of a woman, a man, a woman and a man and a woman with glasses, a couch,. | Figure 1. Representative results of our method (PnP-CM) across a diverse set of inverse problems. Left to right: linear inverse problems
(Gaussian deblurring, super-resolution, inpainting) and nonlinear inverse problems (JPEG artifact removal, nonlinear deblurring, phase
retrieval), all with additive Gaussian noise σ =... | 0.75 | 0.2864 | 0.5182 | 4,878 | 2,294 | 2.126 | d6e733750237ec476df728c3edf5c05e | images/2025/arxiv_0000050.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000051 | Figure 51 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 1. Representative results of our method (PnP-CM) across a diverse set of inverse problems. Left to right: linear inverse problems
(Gaussian deblurring, super-resolution, inpainting) and nonlinear inverse problems (JPEG artifact removal, nonlinear deblurring, phase
retrieval), all with additive Gaussian noise σ =... | A diagram of the four pictures on the. | A detailed layout showing the different photos of people in a photo album and how they are using them. | A comprehensive technical explanation of the role of the first woman in the u s armed forces in the war of the worlds, including the first female soldier in the united states and second woman in world wars with a world war,. | Figure 1. Representative results of our method (PnP-CM) across a diverse set of inverse problems. Left to right: linear inverse problems
(Gaussian deblurring, super-resolution, inpainting) and nonlinear inverse problems (JPEG artifact removal, nonlinear deblurring, phase
retrieval), all with additive Gaussian noise σ =... | 0.7143 | 0.227 | 0.4707 | 1,224 | 897 | 1.365 | 757ac855d3bed0d814a71a7cc4dfc65b | images/2025/arxiv_0000051.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000052 | Figure 52 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 2. Representative results for Gaussian deblurring, inpainting (70%), and super-resolution (×4). PnP-CM produces sharp and
coherent reconstructions, preserving fine details while avoiding the over-smoothing observed in DPS. Compared to CM-based methods, it
more reliably recovers structured content, and achieves v... | A diagram of multiple pictures of a. | A detailed layout showing multiple images of a woman with various facial expressions and hair styles, including a woman ' s face. | A comprehensive technical explanation of a multiple image of a woman with different makeup looks and facial expressions, all in different colors, from brown to blonde to red and black and green and yellow to blue and white and orange and red. | Figure 2. Representative results for Gaussian deblurring, inpainting (70%), and super-resolution (×4). PnP-CM produces sharp and
coherent reconstructions, preserving fine details while avoiding the over-smoothing observed in DPS. Compared to CM-based methods, it
more reliably recovers structured content, and achieves v... | 0.75 | 0.2825 | 0.5162 | 4,203 | 2,545 | 1.651 | 511e8727dc6f06244c170f163bfd8238 | images/2025/arxiv_0000052.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000053 | Figure 53 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 2. Representative results for Gaussian deblurring, inpainting (70%), and super-resolution (×4). PnP-CM produces sharp and
coherent reconstructions, preserving fine details while avoiding the over-smoothing observed in DPS. Compared to CM-based methods, it
more reliably recovers structured content, and achieves v... | A diagram of multiple images with. | A detailed layout showing multiple images of a woman with different hair styles and make - up for her. | A comprehensive technical explanation of a photo montage of a woman with long hair and makeup products on her face, showing multiple stages of the process in the same picture, with a woman ' s face, from the same image. | Figure 2. Representative results for Gaussian deblurring, inpainting (70%), and super-resolution (×4). PnP-CM produces sharp and
coherent reconstructions, preserving fine details while avoiding the over-smoothing observed in DPS. Compared to CM-based methods, it
more reliably recovers structured content, and achieves v... | 0.7143 | 0.2983 | 0.5063 | 1,224 | 762 | 1.606 | 24af191e37270b3f71a6316e3122729f | images/2025/arxiv_0000053.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000054 | Figure 54 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 3. Comparison of reconstruction quality for nonlinear for-
ward models. PnP-CM yields sharp and coherent reconstructions,
with quality that is competitive with or improved over existing
methods, highlighting robust performance in challenging nonlin-
ear settings while requiring substantially fewer NFEs. m > n, a... | A diagram of a woman ' s face with. | A detailed layout showing the different angles of a woman ' s face with her hair styled, and the same side by side. | A comprehensive technical explanation of the three photos show the same amount of light in each room and the same person with the same hair style on the other side of the picture, in the same room, the same picture, the room. | Figure 3. Comparison of reconstruction quality for nonlinear for-
ward models. PnP-CM yields sharp and coherent reconstructions,
with quality that is competitive with or improved over existing
methods, highlighting robust performance in challenging nonlin-
ear settings while requiring substantially fewer NFEs. m > n, a... | 0.75 | 0.2503 | 0.5001 | 2,273 | 990 | 2.296 | cc601a54a13cef035471602d6313c591 | images/2025/arxiv_0000054.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000055 | Figure 55 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 4. Qualitative comparisons of DPS, DDS, CM4IR, and
PnP-CM. Top: Coronal PD with R = 8. Bottom: Coronal PD-FS
with R = 8. PnP-CM effectively reduces artifacts and blurring
that are not removed by other methods (red and yellow arrows). | A diagram of different stages of knee. | A detailed layout showing the different stages of an ostrich joint with a cross section in the middle. | A comprehensive technical explanation of the impact of ostexyal and ostexial injuries on the knee, including ostex, osteoplasmos and ostrictalitis, ostremals, ostric, ostraplorosis, ostri representing all details, specifications, and configurations. | Figure 4. Qualitative comparisons of DPS, DDS, CM4IR, and
PnP-CM. Top: Coronal PD with R = 8. Bottom: Coronal PD-FS
with R = 8. PnP-CM effectively reduces artifacts and blurring
that are not removed by other methods (red and yellow arrows). | 0.7143 | 0.2098 | 0.4621 | 5,016 | 2,573 | 1.949 | 2f4062da5cdd5748410e4bb882fe6ed2 | images/2025/arxiv_0000055.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000056 | Figure 56 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 3. Comparison of reconstruction quality for nonlinear for-
ward models. PnP-CM yields sharp and coherent reconstructions,
with quality that is competitive with or improved over existing
methods, highlighting robust performance in challenging nonlin-
ear settings while requiring substantially fewer NFEs. | A diagram of the different images of a. | A detailed layout showing multiple images of a woman and man with different expressions, including the same person. | A comprehensive technical explanation of the multiple image editing process for a photo booth or video booth, including the same image, and the same picture in one image, the same photo, different format, with each, each, different. | Figure 3. Comparison of reconstruction quality for nonlinear for-
ward models. PnP-CM yields sharp and coherent reconstructions,
with quality that is competitive with or improved over existing
methods, highlighting robust performance in challenging nonlin-
ear settings while requiring substantially fewer NFEs. | 0.75 | 0.3174 | 0.5337 | 553 | 685 | 0.807 | fc6f29fd65782b16f18e674cc262b6b7 | images/2025/arxiv_0000056.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000057 | Figure 57 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 4. Qualitative comparisons of DPS, DDS, CM4IR, and
PnP-CM. Top: Coronal PD with R = 8. Bottom: Coronal PD-FS
with R = 8. PnP-CM effectively reduces artifacts and blurring
that are not removed by other methods (red and yellow arrows). | A diagram of multiple images of the. | A detailed layout showing multiple images of the various structures of a human body with no visible organs or tissues. | A comprehensive technical explanation of the effects of the nephoidic in the right knee, and the effects to the left knee are shown in red and green arrows on the lower left side of the image with the image below. | Figure 4. Qualitative comparisons of DPS, DDS, CM4IR, and
PnP-CM. Top: Coronal PD with R = 8. Bottom: Coronal PD-FS
with R = 8. PnP-CM effectively reduces artifacts and blurring
that are not removed by other methods (red and yellow arrows). | 0.75 | 0.2249 | 0.4874 | 553 | 530 | 1.043 | a2d0f85941558bd8974d6cc9d5459fda | images/2025/arxiv_0000057.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000058 | Figure 58 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 5. Absolute change in PSNR and LPIPS when perturbing
the reported PnP-CM hyperparameters. Even with up to 25% vari-
ation, the deviations remain small, indicating strong robustness. | A diagram of the distribution of water. | A detailed layout showing the distribution of the perturation from the data of the system and the distribution. | A comprehensive technical explanation of the flow rate of the aspirators and aspirants in the following stages of the process, asp106, aspiriumation viab / blprs is perturtion representing all details, specifications, and configurations of the. | Figure 5. Absolute change in PSNR and LPIPS when perturbing
the reported PnP-CM hyperparameters. Even with up to 25% vari-
ation, the deviations remain small, indicating strong robustness. | 0.8064 | 0.2766 | 0.5415 | 513 | 324 | 1.583 | 37e63634321cd9ce3d6de9f8f1293efc | images/2025/arxiv_0000058.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000059 | Figure 59 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 8. Illustrative 4× super-resolution results on CelebA-HQ with σy = 0.05. PnP-CM restores high-frequency details with improved
clarity and denoising, yielding visually richer reconstructions. | A diagram of many different faces of. | A detailed layout showing the faces of many people on the red carpet at an event, with multiple pictures of them. | A comprehensive technical explanation of this photograph shows the many faces of people in the photo booth on the red carpet of a red carpet event, including president obama and then obama and the other presidents, barack and clintons, obama. | Figure 8. Illustrative 4× super-resolution results on CelebA-HQ with σy = 0.05. PnP-CM restores high-frequency details with improved
clarity and denoising, yielding visually richer reconstructions. | 0.75 | 0.3458 | 0.5479 | 4,026 | 4,125 | 0.976 | b3d0181a18c74ad32ab7e9c9aa15e5ee | images/2025/arxiv_0000059.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000060 | Figure 60 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 8. Illustrative 4× super-resolution results on CelebA-HQ with σy = 0.05. PnP-CM restores high-frequency details with improved
clarity and denoising, yielding visually richer reconstructions. | A diagram of the many faces of people. | A detailed layout showing a series of different faces of men and women, including one with blonde hair. | A comprehensive technical explanation of many faces in a collage of people and their faces, including barack obama, michelle obama, donald trump, mello ross, and others, and hillary clinton, and other presidents, all. | Figure 8. Illustrative 4× super-resolution results on CelebA-HQ with σy = 0.05. PnP-CM restores high-frequency details with improved
clarity and denoising, yielding visually richer reconstructions. | 0.7179 | 0.3025 | 0.5102 | 1,224 | 1,276 | 0.959 | 4d02fe8d023deaedcded932cc5835991 | images/2025/arxiv_0000060.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000061 | Figure 61 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 9. Representative Gaussian deblurring results on LSUN Bedroom with σy = 0.05. Comparisons with all baseline methods show
that PnP-CM restores textures more faithfully and avoids oversmoothing. | A diagram of a church with rows of. | A detailed layout showing multiple photos of a hotel lobby with floral arrangements on the tables and chairs, and a bench in front. | A comprehensive technical explanation of a video editing process including multiple images and texting options, including multiple frames of the same image, and multiple images of each one of a woman in a man sitting on a chair at a table. | Figure 9. Representative Gaussian deblurring results on LSUN Bedroom with σy = 0.05. Comparisons with all baseline methods show
that PnP-CM restores textures more faithfully and avoids oversmoothing. | 0.81 | 0.2298 | 0.5199 | 4,026 | 4,125 | 0.976 | 03892ee3d7d32c85d612801717b16679 | images/2025/arxiv_0000061.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000062 | Figure 62 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 9. Representative Gaussian deblurring results on LSUN Bedroom with σy = 0.05. Comparisons with all baseline methods show
that PnP-CM restores textures more faithfully and avoids oversmoothing. | A diagram of a machine in a factory. | A detailed layout showing rows of rows of empty seats in a church with a clock on the wall that shows key features, attributes, and data points in. | A comprehensive technical explanation of a machine to make a quilter ' s fabric on the machine is shown in multiple pictures with a variety of fabrics and colors of machines in front rows of them, including white and pink and red. | Figure 9. Representative Gaussian deblurring results on LSUN Bedroom with σy = 0.05. Comparisons with all baseline methods show
that PnP-CM restores textures more faithfully and avoids oversmoothing. | 0.81 | 0.2677 | 0.5389 | 1,224 | 1,276 | 0.959 | 2a54917a31140f0125b69e3df225ee9f | images/2025/arxiv_0000062.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000063 | Figure 63 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 11. Demonstration of super-resolution (×4) results on LSUN Bedroom with σy = 0.05. Reconstructions are compared against all
baseline methods, with PnP-CM producing sharper details and closer resemblance to the ground truth. | A diagram of multiple images of a. | A detailed layout showing the multiple images of a coffee machine and cups on display at a storefront. | A comprehensive technical explanation of the process of printing a product in multiple stages, including a print and color scheme and a selection of colors for each product, from different to which one is available or more than one color. | Figure 11. Demonstration of super-resolution (×4) results on LSUN Bedroom with σy = 0.05. Reconstructions are compared against all
baseline methods, with PnP-CM producing sharper details and closer resemblance to the ground truth. | 0.7143 | 0.2775 | 0.4959 | 4,026 | 4,125 | 0.976 | f51d4254e11160f52dbfe0ffd6ccb9e8 | images/2025/arxiv_0000063.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000064 | Figure 64 | scientific_figure | PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems | arXiv Research Authors | 2509.22736v2 | eess | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems. arXiv:2509.22736v2 | Figure 11. Demonstration of super-resolution (×4) results on LSUN Bedroom with σy = 0.05. Reconstructions are compared against all
baseline methods, with PnP-CM producing sharper details and closer resemblance to the ground truth. | A diagram of the different machines. | A detailed layout showing the various machines used in the manufacturing process and how they work together to produce products. | A comprehensive technical explanation of the different types of dyes available in the market for sale on the shelves of a store or retail store, including the color and the machine and the bottles of the mixers and the labels. | Figure 11. Demonstration of super-resolution (×4) results on LSUN Bedroom with σy = 0.05. Reconstructions are compared against all
baseline methods, with PnP-CM producing sharper details and closer resemblance to the ground truth. | 0.75 | 0.218 | 0.484 | 1,224 | 1,276 | 0.959 | d86a35cc9a6e4c407a685197f2f7e6d0 | images/2025/arxiv_0000064.png | https://arxiv.org/pdf/2509.22736v2.pdf |
arxiv_0000065 | Figure 65 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 1: Diagram of the divergence-free NN (dfNN) model architecture and directional guidance
learning strategy (left). The quiver plot (right) shows an on-grid ice flux reconstruction by the dfNN
with directional guidance (best model) for a subset of the experimental region over Byrd Glacier. limit data collection, m... | A diagram of the different phases of. | A detailed layout showing the three phases of an artificial cell membrane and the different types of it that are used to produce. | A comprehensive technical explanation of the quantum - based magnetic field experiment of an n - - inferetry - v - e - v, and the diff - v v - vf - vg - vd representing all details, specifications, and configurations of the system components in. | Figure 1: Diagram of the divergence-free NN (dfNN) model architecture and directional guidance
learning strategy (left). The quiver plot (right) shows an on-grid ice flux reconstruction by the dfNN
with directional guidance (best model) for a subset of the experimental region over Byrd Glacier. limit data collection, m... | 0.75 | 0.2459 | 0.498 | 3,190 | 1,072 | 2.976 | 53b91513a3a9cc7f77e348e18a8253f2 | images/2025/arxiv_0000065.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000066 | Figure 66 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 1: Diagram of the divergence-free NN (dfNN) model architecture and directional guidance
learning strategy (left). The quiver plot (right) shows an on-grid ice flux reconstruction by the dfNN
with directional guidance (best model) for a subset of the experimental region over Byrd Glacier. | A diagram of the structure of a cell. | A detailed layout showing the process of an invertitotor to produce a cell phone battery using a cellphone. | A comprehensive technical explanation of the structure and function of an ion - activated cell phone, including the location of the battery and the potential energy source for it to operate it inverte to charge it to run it,. | Figure 1: Diagram of the divergence-free NN (dfNN) model architecture and directional guidance
learning strategy (left). The quiver plot (right) shows an on-grid ice flux reconstruction by the dfNN
with directional guidance (best model) for a subset of the experimental region over Byrd Glacier. | 0.7321 | 0.2268 | 0.4794 | 1,224 | 414 | 2.957 | e13a64bcb8cb4de05564a0a149be1a3a | images/2025/arxiv_0000066.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000067 | Figure 67 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 2: Test RMSE (↓) comparison across all model variants, averaged over five runs. Boxed values
indicate mean RMSE, with error bars showing ± std. MAD (top) denotes the Mean Absolute Diver-
gence. dfNNs (proposed, in bold) outperform PINNs & NNs, while directional guidance (proposed,
in bold) improves all models an... | A diagram of the number of different. | A detailed layout showing the number of different hb - 2s and hb2s in each cell that shows key features, attributes, and data points in detail for. | A comprehensive technical explanation of the number and type of cases with the same amount of cases per year, by type of case, as described in this figure 1, from the study of the data table 2, 2011 - 3. | Figure 2: Test RMSE (↓) comparison across all model variants, averaged over five runs. Boxed values
indicate mean RMSE, with error bars showing ± std. MAD (top) denotes the Mean Absolute Diver-
gence. dfNNs (proposed, in bold) outperform PINNs & NNs, while directional guidance (proposed,
in bold) improves all models an... | 0.81 | 0.2739 | 0.542 | 3,780 | 933 | 4.051 | ca009c919487262189af784df58ab12e | images/2025/arxiv_0000067.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000068 | Figure 68 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 3: Gridded predictions of models + dir (best variant per model) for a small test region (white). | A diagram of the different phases of. | A detailed layout showing the different phases of an air - plane flight from the earth to the moon that shows key features, attributes, and data. | A comprehensive technical explanation of the different types of wind patterns in the atmosphere of earth ' s surface, including wind speed, wind flow, and air pressure, and temperature, and water pressure, in the earth ' nn. | Figure 3: Gridded predictions of models + dir (best variant per model) for a small test region (white). | 0.81 | 0.2277 | 0.5189 | 3,174 | 1,087 | 2.92 | 18205e98d78bb9230048f37c3353cad7 | images/2025/arxiv_0000068.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000069 | Figure 69 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 2: Test RMSE (↓) comparison across all model variants, averaged over five runs. Boxed values
indicate mean RMSE, with error bars showing ± std. MAD (top) denotes the Mean Absolute Diver-
gence. dfNNs (proposed, in bold) outperform PINNs & NNs, while directional guidance (proposed,
in bold) improves all models an... | A diagram of a bar chart with the. | A detailed layout showing the distribution of the number of different cell phones in the world, including the number. | A comprehensive technical explanation of the number of patients who have been treated for the same type of treatment as well as their age and weight, as per table numbers of patients with a total and age, in the number,. | Figure 2: Test RMSE (↓) comparison across all model variants, averaged over five runs. Boxed values
indicate mean RMSE, with error bars showing ± std. MAD (top) denotes the Mean Absolute Diver-
gence. dfNNs (proposed, in bold) outperform PINNs & NNs, while directional guidance (proposed,
in bold) improves all models an... | 0.81 | 0.2426 | 0.5263 | 1,224 | 343 | 3.569 | 94e3149148806208c40530879eb5073a | images/2025/arxiv_0000069.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000070 | Figure 70 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 3: Gridded predictions of models + dir (best variant per model) for a small test region (white). | A diagram of the two different types. | A detailed layout showing an image of the same number of different elements in the model, each with its own. | A comprehensive technical explanation of the new and old model for pnm - sn9, which is being developed by the company in the united states of new york and the us, with the same company in 1994 representing all details, specifications, and. | Figure 3: Gridded predictions of models + dir (best variant per model) for a small test region (white). | 0.7957 | 0.233 | 0.5143 | 1,224 | 605 | 2.023 | 0ced72ee31573f44cff0e91c4554d79d | images/2025/arxiv_0000070.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000071 | Figure 71 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 4: Train-Test chequerboard over Byrd Glacier, Antarctica. Training regions are shown in
grey and test regions are shown in white. Points indicate locations with ice flux observations,
collected mainly through airborne geophysical surveys, highlighting the anisotropic nature of the
‘flight line’ data. The Antarct... | A diagram of the location of an area. | A detailed layout showing the area of a city with many dots and squares in blue, and a small square. | A comprehensive technical explanation of the data visual for the solar system in the city of washington, dc, and washington d c, and the us states of america, and canada, and europe, with a graphed region labeled. | Figure 4: Train-Test chequerboard over Byrd Glacier, Antarctica. Training regions are shown in
grey and test regions are shown in white. Points indicate locations with ice flux observations,
collected mainly through airborne geophysical surveys, highlighting the anisotropic nature of the
‘flight line’ data. The Antarct... | 0.7071 | 0.2426 | 0.4748 | 2,072 | 2,003 | 1.034 | 5eb534c9530048519f18560744bb5992 | images/2025/arxiv_0000071.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000072 | Figure 72 | scientific_figure | Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields | arXiv Research Authors | 2510.06286v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Mass Conservation on Rails -- Rethinking Physics-Informed Learning of Ice Flow Vector Fields. arXiv:2510.06286v1 | Figure 4: Train-Test chequerboard over Byrd Glacier, Antarctica. Training regions are shown in
grey and test regions are shown in white. Points indicate locations with ice flux observations,
collected mainly through airborne geophysical surveys, highlighting the anisotropic nature of the
‘flight line’ data. The Antarct... | A diagram of the intersection of two. | A detailed layout showing the location of a network in a city area and a blue line of information about the area. | A comprehensive technical explanation of the application of the algorithm for using the data in the following image, the data is displayed on the map and the data on the screen below it is the data barcoded as described by the data. | Figure 4: Train-Test chequerboard over Byrd Glacier, Antarctica. Training regions are shown in
grey and test regions are shown in white. Points indicate locations with ice flux observations,
collected mainly through airborne geophysical surveys, highlighting the anisotropic nature of the
‘flight line’ data. The Antarct... | 0.81 | 0.2063 | 0.5081 | 1,224 | 1,303 | 0.939 | 81c02bef2e520d3cf9b2fcafd9ad7867 | images/2025/arxiv_0000072.png | https://arxiv.org/pdf/2510.06286v1.pdf |
arxiv_0000073 | Figure 73 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 1. (a) Darkfield (DFI) image (b) Attenuation (ATTN) image. In the attenuation image, several regions of
the lungs are partially obscured by the cardiac volume and other organs, whereas in the DFI image these areas
remain visible as regions of reduce, but still detectable small-angle scattering intensity. In pa... | A diagram of the diaploid and dfi of. | A detailed layout showing the diaploid of the chest before surgery with the dfi arrows pointing to the abdomen. | A comprehensive technical explanation of the dfi on the chest and abdomen of a patient with no surgery, including the dft on the abdomen of the chest, and the dfl on the left side of the abdomen, is shown. | Figure 1. (a) Darkfield (DFI) image (b) Attenuation (ATTN) image. In the attenuation image, several regions of
the lungs are partially obscured by the cardiac volume and other organs, whereas in the DFI image these areas
remain visible as regions of reduce, but still detectable small-angle scattering intensity. In pa... | 0.7464 | 0.2979 | 0.5222 | 519 | 555 | 0.935 | f75d162f0215b864f779d234ba0061fd | images/2025/arxiv_0000073.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000074 | Figure 74 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | null | A diagram of the chest and abdomen. | A detailed layout showing the position of the rib cage, which is not visible for all of the radiologists to see. | A comprehensive technical explanation of the anatomy of the human chest and abdomens, including the thorble, the thorbla, and the ribs of the abdomens and the abdomen, from the front view of the chest and the upper part of the hip. | null | 0.75 | 0.3228 | 0.5364 | 524 | 555 | 0.944 | 17100324f67d5cdbcb86a0c4dd3104e1 | images/2025/arxiv_0000074.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000075 | Figure 75 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | and | A diagram of an x - ray image of a man. | A detailed layout showing the different areas of the chest and the posterior of the abdomen, including the breast. | A comprehensive technical explanation of the radiology of the chest and abdomen, showing the various areas of the human body with a single breast in the middle section and a three sections on the upper part of the front of the top. | and | 0.81 | 0.2491 | 0.5295 | 519 | 555 | 0.935 | 18924c6d23bb1501c0555a73b3c2327e | images/2025/arxiv_0000075.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000076 | Figure 76 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 1. | A diagram of the chest and chest area. | A detailed layout showing the ribs and chest of an infant with a broken rib - xray image that shows key features, attributes, and data points in. | A comprehensive technical explanation of the location of the chest radiography showing the location and location of a chest radiograph, including the ribs and the chest, the top of a man with a chest, in the chest and an x - ray. | Figure 1. | 0.81 | 0.3476 | 0.5788 | 519 | 555 | 0.935 | ad09c5f15ec004a4773be64e824cb292 | images/2025/arxiv_0000076.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000077 | Figure 77 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 1. (a) Darkfield (DFI) image (b) Attenuation (ATTN) image. In the attenuation image, several regions of
the lungs are partially obscured by the cardiac volume and other organs, whereas in the DFI image these areas
remain visible as regions of reduce, but still detectable small-angle scattering intensity. In pa... | A diagram of the chest showing the. | A detailed layout showing the location of a chest, with a large amount of small areas in the abdomen. | A comprehensive technical explanation of the anatomy of the chest and the lungs, including the thorbone and chest area areas, from the study of the study, c - fyc / p - t, e - l - l representing all details, specifications, and configurations of. | Figure 1. (a) Darkfield (DFI) image (b) Attenuation (ATTN) image. In the attenuation image, several regions of
the lungs are partially obscured by the cardiac volume and other organs, whereas in the DFI image these areas
remain visible as regions of reduce, but still detectable small-angle scattering intensity. In pa... | 0.7107 | 0.2777 | 0.4942 | 976 | 944 | 1.034 | c9b114c2b4ba27986cc301f97cbe7c04 | images/2025/arxiv_0000077.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000078 | Figure 78 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 2. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b) Correspondi... | A diagram of the chest showing an area. | A detailed layout showing the location of the x - rays and the location on the left side of the chest. | A comprehensive technical explanation of the impact of the presence of the small dots on the chest and the large dots on a large dot on the upper part of the lower part of a large bone, on the right side of the abdomen. | Figure 2. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b) Correspondi... | 0.7143 | 0.2928 | 0.5036 | 980 | 640 | 1.531 | 9887a1ac48f3b937bbd64dff8b9fb0db | images/2025/arxiv_0000078.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000079 | Figure 79 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Original dark-field image shown for reference. | A diagram of the interior of an. | A detailed layout showing the cat standing on the rock looking down at the camera, in black and white. | A comprehensive technical explanation of the human body ' s skeletal muscles and muscles, including the ribs, legs, and abdomens, and the upper limb bones and the hip bones, from the top view of the posterior view. | Original dark-field image shown for reference. | 0.7143 | 0.2481 | 0.4812 | 469 | 640 | 0.733 | dac6dccba88d7bb4d2586c0c128417e8 | images/2025/arxiv_0000079.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000080 | Figure 80 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 3. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | A diagram of the chest and ribs shows. | A detailed layout showing the presence of the metacline on the right side of the chest, and an x - ray image of the left side of a man ' s chest. | A comprehensive technical explanation of the presence of the metaclost on the thorric ribs of a patient in this case, the thorrotrotrotormaploidia is a non - thorromal metator and metatriized representing all details, specifications, and. | Figure 3. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | 0.75 | 0.2918 | 0.5209 | 971 | 700 | 1.387 | 943b2517757b7810a5de9125fe512fed | images/2025/arxiv_0000080.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000081 | Figure 81 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 3. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | A diagram of a plane flying over a. | A detailed layout showing the dark gray background and the white light on the left side of the image. | A comprehensive technical explanation of the structure of a moon ' s face and its shadow, including the moon ' ' s tail, which is pointed toward to the left at the sun ' s center of the moon, and the moon. | Figure 3. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | 0.7107 | 0.2651 | 0.4879 | 461 | 700 | 0.659 | e9e991bdb7a4602df1ffc41eac886dd3 | images/2025/arxiv_0000081.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000082 | Figure 82 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 2. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b) Correspondi... | A diagram of the different areas of. | A detailed layout showing the location of the left shoulder and the right shoulder bone, and the patient ' s chest. | A comprehensive technical explanation of the bone marrow and its role in surgery for the hip joint, including the pelvic region and the lumbas of the pelvis bone verteus, for the pectoral bone representing all details, specifications, and. | Figure 2. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b) Correspondi... | 0.75 | 0.2282 | 0.4891 | 987 | 437 | 2.259 | b1d87ac95aa22b481f169b9f28adb3ed | images/2025/arxiv_0000082.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000083 | Figure 83 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 3. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | A diagram of the normal and a normal. | A detailed layout showing the presence of the papii on the left side of the chest, and the location of the peritoidus on the right side. | A comprehensive technical explanation of x - ray images showing the location and function of the bone, including the end of the right side of the thor and the thorcrasm, and the upper thorblex, and upper. | Figure 3. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly higher
attenuation than the surrounding lung tissue, making them visible in the upper lung regions but nearly
indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | 0.75 | 0.2463 | 0.4981 | 987 | 485 | 2.035 | c25df9053237336407ce267775d20a5e | images/2025/arxiv_0000083.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000084 | Figure 84 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | Figure 4. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly
higher attenuation than the surrounding lung tissue, making them visible in the upper lung regions but
nearly indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | A diagram of the head of a man with a. | A detailed layout showing the location of the left vental in the upper part of the abdomen, and the yellow line on the lower part of an x - ray. | A comprehensive technical explanation of the chest radiograph of a person with a proxitoid on the right side of the body and a yellow line indicating a large area on the left side of his chest radiogram in the chest. | Figure 4. (a) Contoured attenuation (ATTN) image with inserted tumors. The tumors exhibit slightly
higher attenuation than the surrounding lung tissue, making them visible in the upper lung regions but
nearly indistinguishable in the lower sections, where overlapping organs project higher attenuation. (b)
Correspond... | 0.75 | 0.3253 | 0.5376 | 432 | 715 | 0.604 | 3f3de7716feb839de1a2cc3e74ff6a75 | images/2025/arxiv_0000084.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000085 | Figure 85 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | null | A diagram of the presence of a cell in. | A detailed layout showing the structure of the cell membrane of the microtubr, which is a layer that shows key features, attributes, and data points. | A comprehensive technical explanation of the structure of the human cell membranes and its connections to the cell wall and surrounding the cell, from the surface of the cell line of the plate, which is shown in yellow lines on the image. | null | 0.87 | 0.2368 | 0.5534 | 435 | 715 | 0.608 | 5bf83da991feb9ad3f252a2df520b0aa | images/2025/arxiv_0000085.png | https://arxiv.org/pdf/2510.27679v1.pdf |
arxiv_0000086 | Figure 86 | scientific_figure | Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models | arXiv Research Authors | 2510.27679v1 | physics | 2025 | http://creativecommons.org/licenses/by/4.0/ | arXiv Research Authors (2025). Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models. arXiv:2510.27679v1 | null | A diagram of a cat ' s head showing. | A detailed layout showing the light from a microscope at night, and in the dark of the image that shows key features, attributes, and data points in. | A comprehensive technical explanation of the first ever image of a horse in the dark, with a white spot on its forehead and a black background to it ' s left side and right of the head, as well as well,. | null | 0.81 | 0.2609 | 0.5354 | 437 | 715 | 0.611 | e70a04a1202009a79fcc4cb0f89b48fe | images/2025/arxiv_0000086.png | https://arxiv.org/pdf/2510.27679v1.pdf |
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SciFig-Bench: Scientific Figure & Multi-Scale Caption Dataset Card
Dataset Description
This dataset contains high-quality scientific figures, architecture diagrams, charts, and visualizations extracted from scientific papers (arXiv & local PDFs). It includes multi-level captions (Small, Medium, Large), extracted embedded OCR text, paper metadata, and alignment quality scores.
- Total Figure Records: 589
- Average CLIP Alignment Score: 0.2721
- Average Composite Score: 0.5209
- Supported Formats: JSONL (
dataset.jsonl), CSV (dataset.csv)
Dataset Schema
| Field Name | Type | Description |
|---|---|---|
id |
String | Unique figure identifier (e.g. arxiv_0000001) |
figure_id |
String | Original figure caption prefix (e.g. Figure 1) |
figure_type |
String | Classified figure type (architecture_diagram, line_plot, bar_chart, etc.) |
paper_title |
String | Title of the source scientific paper |
authors |
List/String | List of paper authors |
arxiv_id |
String | arXiv paper identifier if available |
category |
String | Subject category classification (e.g., cs, stat, physics) |
year |
String | Publication year |
license |
String | Paper license URL if specified |
citation |
String | Bibliographic citation string |
original_caption |
String | Exact figure caption extracted from the paper |
small_caption |
String | Short caption (30–50 chars) for quick retrieval |
medium_caption |
String | Detailed caption (100–180 chars) describing key components |
large_caption |
String | Comprehensive caption (200–300 chars) with detailed visual explanation |
extracted_text |
String | OCR-extracted text labels embedded inside the figure image |
clip_score |
Float | CLIP visual-text alignment score [0, 1] |
composite_score |
Float | Combined text quality and CLIP alignment score |
image_width |
Integer | Image width in pixels |
image_height |
Integer | Image height in pixels |
aspect_ratio |
Float | Image aspect ratio (width / height) |
image_hash |
String | Perceptual dHash fingerprint for deduplication |
Dataset Statistics
Category Breakdown
| Subject Category | Record Count |
|---|---|
cs |
370 |
physics |
79 |
eess |
15 |
stat |
125 |
Figure Type Distribution
| Figure Type | Count |
|---|---|
scientific_figure |
589 |
Usage & Quality Control
Quality Filters Applied
- Resolution & Aspect Ratio: Rejects images smaller than 80×80 px or with extreme aspect ratios (>8:1).
- Blank/Low-Contrast Check: Filters out low-contrast background regions and empty boxes.
- Perceptual Deduplication: SHA-256 and dHash checks prevent duplicate images across papers.
- CLIP Visual Alignment: Measures semantic alignment between figure image and generated captions.
Citation & Licensing
When using records from this dataset, please attribute the original paper authors according to the citation field provided in each dataset record.
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