dataesr/scientific-paragraphs-categorization huggingface dataset:
We transferred two target persons from our dataset to two videos with different camera setups and image resolutions: "bruno-mars" and "tom-cruise" sequences. In the "bruno-mars" sequence, shown in Figure  5. 4, we ran vid2vid and EBDN using their respective solution to tackle with different image resolutions and actors with different proportions from the training data. Vid2vid's strategy (scaling to keep the aspect ratio and then cropping the image) results in an actor with different proportions compared with the training data, which leads to a degradation of quality. EBDN's strategy (pose normalization, scaling to keep the aspect ratio, and then cropping the image) keeps the similarity between the input video and training data, but body and face occlusions are still problems. The red squares highlight these issues on the right side in Figure  5.
Results in the iPER Dataset Aside from our dataset, we also evaluated our approach using the iPER dataset  [Liu et al. , 2019]. Since the iPER dataset does not provide paired motions as the proposed it also allows to create a deformed model from images that can be included in virtual scenes using existing rendering frameworks, such as Blender. Figure  5. 7 illustrates an application of this capability. Regarding the method's processing time, our current implementation of the Method I is a modular Python code, but without any optimization, i. e. , the code was not parallelized either and was not adapted to run fully on a GPU. However, we highlight the available room for speeding up the processing time with a parallel implementation of different parts of the approach as, for instance, in the deformation model steps, which currently require 30 seconds per frame.
Training Setup For the training of both texture models and the mesh human deformation network we considered four-minute videos provided by our dataset, where the actors perform random movements, allowing the model to get different views from the person in the scene. We use the SMPL model parameters calculated by our Motion Estimation method (3. 2. 1) and the silhouette image segmented by MODNet  [Ke et al. , 2020]  for each frame of the video.
11. Figure  5. 8-b shows the texture quality of the models trained with and without the adversarial regime. After removing the GAN the model could not generate textures with fine details, producing blurry results. This result is also reported in the metrics of Table  5. 8, where we show the average values calculated from all motion sequences in the test data in which the model without GAN is outperformed in all results besides SSIM. This result is coherent, since SSIM is based on low-level image features, and blurred textures can lead to higher SSIM.
Quantitative Comparison with State of The Art We performed the neural rendering for actors with different body shapes, gender, clothing styles, and sizes for all considered video sequences. The video sequences used in the actor animation contained motions with different levels of difficulty, which aims to test the generalization capabilities of the methods in unseen data. Table  5. 9 shows the performance for each method considering all motion and actors types in the dataset. We can see that our Method II achieves superior peformance as compared to the methods in most of the motion sequences and actor types considering the SSIM, LPIPS, MSE, and FVD metrics. We argue that these results indicate that our second method is capable of deforming the mesh according to the shape of the given actors and then, rendering a texture optimized to fit the morphology of the person in the scene.
Qualitative Visual Analysis The visual inspection of synthesized actors also concur with the quantitative analysis. In Figure  5. 9, we provide the best frames for each movement using four actors in the dataset. Our Method I and our Method II are the only models capable of keeping the body scale of the authors along all scenes, while the other methods failed, in particular in the movements shake hands and walk. Besides generating coherent poses, our second method also generated more realistic textures in comparison to the other methods. Comparing the results of the movements jump and spinning, one can visualize some details as the shadow of the shirt sleeve of the actor and the shirt collar, respectively.
A unified methodology carefully designed to transfer motion and appearance from video to video that preserves the main features of the human movement and retains the visual appearance of the target character; ii. A retargeting technique taking into account physical constraints of the motion in 3D and the image domain; and new semantic-guided image-based rendering approach that copies local patterns from input images to the correct position in the generated images, which defines a more stable method and overcome the lack of details; iii. A novel data-driven formulation for transferring appearance and reenact human actors that produces a fully 3D controllable human model, i. e. , the user can control the human pose and rendering parameters. We performed experiments on different publicly available videos and on a dedicated collected dataset containing several types of motions, constraints, and actors with different body shapes.
Introduction Transmission spectroscopy is a powerful method for studying exoplanetary atmospheres. During the past decade, space missions and ground-based surveys have provided numerous data on exoplanetary atmospheres  (Tinetti et al. 2007; Tsiaras et al. 2018; Seidel et al. 2019; Mikal-Evans et al. 2020). In addition, new space telescopes will be launched in the current decade (James Webb Space Telescope (JWST) and Ariel) whose goals will not only be the detection of molecular features, but also the estimation, with greater accuracy, of the atmospheric molecular abundances. However, a higher precision in the observation must be followed by an improvement of theoretical and statistical tools. Several global climate models (GCM) are available  (Showman & Guillot 2002; Showman et al. 2008; Menou & Rauscher 2009; Wordsworth et al.
We first introduce a new open-source documented version of Pytmo-sph3R  (Caldas et al. 2019 ) that computes transmission spectra for atmospheres with up to three spatial dimensions. The code is very flexible and can use 3D time-varying atmospheric structures from a GCM as well as simpler, parameterized 1D or 2D structures. Section 5 shows some application examples, highlighting the benefits of realistically describing the complex structure of the atmosphere. To allow our approach to be used in a retrieval framework, we have implemented our 2D algorithm in TauREx  (Waldmann et al. 2015a; Al-Refaie et al. 2021; Changeat et al. 2019). We then demonstrate that our implementation is sufficiently fast to allow converging on a realistic retrieval solution in a reasonable amount of time.
The number of pressure levels is noted N p,lay , the number of latitudes N lat , and the number of longitudes N lon. Pytmosph3R 2. 0 offers several options to compute the volume-mixing ratio of all gases in each cell of the atmospheric grid: (1) it can extract this information directly from the input simulation when it is present, (2) it can interpolate from a separate table providing the mixing ratios on a pressure and temperature grid (e. g. , when thermodynamical equilibrium is assumed), (3) it can use analytical formulae (e. g. , those of  Parmentier et al. 2018  where thermal dissociation of key molecules is accounted for), (4) it can call a chemistry module like FastChem  (Stock et al. 2018 ) to compute the abundances on the fly, and (5) the users can specify their own constant mixing ratios.
Experimental setup The 1D model is based on the TauREx 3 implementation (Al-Refaie et al. 2021). We developed the 2D model in the same framework, extending each 1D data profile to a 2D alternative, and using Eq. (  1 ) for the temperature map. The 3D model, developed in a first version by  Caldas et al. (2019)  was reimplemented here in a second version, Pytmosph3R 2. 0 1. All models are coded in Python 3, with computationally intensive operations relying on numba  (Lam et al. 2015). The machines on which the numerical experiments were performed are Intel®Xeon®Gold 6138 CPUs at 2. 00 GHz, with 128 GB of RAM.
2020) , where the jet stream cools down the west limb and heats up the east limb. In these configurations, our 2D model, which is symmetric with respect to the star-observer line, would not be able to give a better solution than a 1D model because its limb is homogeneous by definition. 2D retrievals using adapted configurations depending on the type of observed exoatmospheres are required. We could also develop hybrid 2D models that would take several geometric effects into account, keeping in mind that too many parameters in a retrieval code may create degeneracies. Overall, TauREx 2D can infer the atmospheric parameters of specific exoplanetary types, that is, ultra hot Jupiters, with a good compromise between computational time and model precision. This 2D version of TauREx will be made publicly available in the near future.
Acknowledgements. We are grateful to all of the TauREx developping team. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n • 679030/WHIPLASH). We thank the  Programme National de Planétologie  ( CNRS/INSU/PNP ) and the  CNES  for their financial support. A41, page 11 of  12 A&A 658 ,  A41 (2022 ).
The focus is on the multi-frequency sensors (or combination of sensors) found in operational and in-preparation radiometry missions such as SMOS  (Kerr et al. , 2010) , SMAP  (Entekhabi et al. , 2010) , AMSR-E, AMSR2, AMSR3  (Kasahara et al. , 2020)  and CIMR  (Kern et al. , 2020). The overarching goal is to refine the interpretation of the datasets produced with the existing algorithms, and to pave the way to new, more advanced, melt detection algorithms. This study is particularly aimed at users who need a better and more quantitative understanding of the various microwave melt products for detailed investigations and thorough firn model evaluations. To conduct realistic simulations with wet snow, a pre-requisite is an accurate representation of the snowpack at the onset of the melt season.
With firn and ice deeper than 100 m, the influence of the underlying substrate is only significant at L band in dry conditions and is treated appropriately in our modeling setup. Passive microwave observations acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on-board Japan's Global Change Observation Mission 1st -Water "SHIZUKU" (GCOM-W1) satellite are used for the retrieval, general statistics and melt detection. Data at 6, 10, 19, and 37 GHz at vertical and horizontal polarizations are extracted from the National Snow and Ice center (NSIDC) AMSR-E/AMSR2 Unified Level 3 daily product, version 2. The product has a resolution of 25 km at 6 and 10 GHz and 12. 5 km at 19 and 37 GHz. The typical brightness temperature accuracy is ±1 K. The Soil Moisture Ocean Salinity (SMOS) from the European Space Agency (ESA), the Centre National d'Études Spatiales (CNES) and the Centro para el Desarrollo Tecnológico Industrial (CDTI) are also used to provide L-band data at 1.
This algorithm is an efficient Markov Chain Monte-Carlo (MCMC) method  (ter Braak and Vrugt, 2008; Laloy and Vrugt, 2012; Shockley et al. , 2017). 16 chains (recommended range d/2 -2d, with d the number of unknowns) are run in parallel over 2000 iterations and the first 50% samples are discarded ("burn-in"). The output is an ensemble of 16000 tie-point sets following the posterior probability. However, the MCMC methods tend to produce auto-correlated samples and in our case, the effective sample size  (Martin, 2022)  is estimated ≈100. To assess the MCMC convergence, we calculated the potential scale reduction factor ( R)  (Gelman and Rubin, 1992)  as provided by the ArviZ software  (Kumar et al. , 2019). Overall we obtain R values lower than 1. 2 for all parameters and all sites (in the burned-in ensemble) which is acceptable.
Here we investigate the risk of misclassifying such a pixel as not melting, despite the obvious presence of meltwater. Two cases are studied: a snow layer saturated with water and supraglacial lakes. Saturated snow is modeled as a mixture of ice scatterers in a water background. Fig. 14  shows the variations of brightness temperature when the proportion of water increases from pure ice to pure water. When this layer is exactly at the surface (solid lines) the brightness temperature first increases (due to the increasing absorption), and then decreases to very low values (due to the increasing reflectivity of the wet interface). Browsing the AMSR2 dataset, such values much lower than the winter average have never been observed on glacial ice (it is however common on sea-ice), which questions the realism of this simulation.
Such high areal lake coverage fractions have rarely been observed in Antarctica  (Arthur et al. , 2022). For example, even before the near-complete collapse of the Larsen B Ice Shelf in 2002, which has been attributed to about 3000 rapidly draining lakes  (Banwell et al. , 2013; Robel and Banwell, 2019) , the fractional lake coverage reached a maximum of only 10%  (Banwell et al. , 2014). Or in more recent melt records on shelves subject to ponding, such as the north George VI Ice Shelf where a maximum of 15% lake coverage was observed  (Banwell et al. , 2021) , or in our observations of the eastern Roi Baudouin Ice Shelf, we have not identified a significant decrease in brightness temperature in the AMSR2 dataset. We can conclude that false negative melt detection due to supraglacial lake is unlikely at present time in Antarctica.
Acknowledgements. This work is funded by the  ESA  Project  ESA/AO/1-9570/18/I-DT -"4D   Antarctica ". SMRT was developed in the  ESA  Project  4000112698/14/NL/LvH  " Microstructural Origin of Electromagnetic Signatures in Microwave Remote Sensing of Snow ". Alison F. Banwell received support from the  U. S. National Science Foundation (NSF)  under award  1841607  to the  University of Colorado Boulder. We thank  Carlo Marin  for debugging the H86 permittivity formulation in SMRT. The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the authors and do not necessarily reflect those of NOAA or the  U. S. Department of Commerce.
However, we stress that fundamental knowledge is still missing regarding the wet snow permittivity, the small scale liquid water variability, and the snowpack properties in the coastal Antarctic environment, particularly the ice shelves. Future work should focus on filling these gaps given the growing importance of the ice-sheet hydrology as the climate warms. Code and data availability. The SMRT model code, including the new permittivity formulations, is available from https://github. com/ smrt-model/smrt. The code to produce the figures will be posted as well https://github. com/smrt-model/smrt_liquid_water_paper. This repository will also include the retrieved snowpack properties and the observed and simulated brightness temperatures at the select study sites.
Acknowledgements COMET is the  NERC Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics , a partnership between UK Universities and the  British Geological Survey. LiCSAR uses JASMIN , the UK's collaborative data analysis environment (http://jasmin. ac. uk). This study contains modified Copernicus Sentinel data ( 2015-2022 ). We thank  Elias Lewi Teklemariam ,  Patricia Mothes ,  Pedro Espín Bedón ,  Marco Yépez ,  Marco Córdova  and  Santiago Aguaiza  for feedback during 640 the development of the COMET Volcano Deformation Portal. https://doi. org/10. 5194/gc-2022-15Preprint. Discussion started: 14 December 2022 c Author(s)  2022. CC BY 4. 0 License.
The collaboration addressed the difficulties of open-source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user-group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use-case to improve their systems.
We also discuss the software engineering procedures we employed to ensure that the scientists could trust and depend on the in-progress upstream cloud-based service. We review traditional and Agile software development procedures and provide an introduction to Agile abstractions in Section 2. We provide an overview the two groups in Section 3. We describe the creation of our collaboration and the risks involved in Section 4. Our approach to using Agile Scrum to resolve these risks is described in Section 5. We assess the collaboration's Agile Scrum framework and the outcomes for each organization in Section 6. We provide connections between this case study and other related works in Section 7. Section 8 summarizes the takeaways of this case study for the scientific computing community.
Collaboration opportunity SNEWS was preparing to upgrade its alert software framework from the current version of SNEWS to SNEWS 2. 0. SNEWS 2. 0 would require a scalable and reliable publish-subscribe system, potentially based on cloud-hosted web-services. The SNEWS team had already created the requirements for their first alert software as part of a Waterfall methodology and was exploring technology options for a purely in-house development effort. 10 Initially, SNEWS considered two options for their software development upgrade. SNEWS could either (1) build these systems from scratch, or (2) explore existing software. Each option would offer both advantages and challenges. Option 1 would allow SNEWS to maintain direct control over the development and ensure their needs were met, but it would impose the burdens of long-term development, support, and maintenance entirely on SNEWS developers.
This could ultimately become problematic as SNEWS most likely would not have the resources and personnel to sustainably maintain the software long-term. Option 2 would avoid these burdens by offloading the core programming to an external software group, but it would reduce SNEWS's control in the process and potentially limit their representation and communication during development. At the same time that SNEWS was assessing the upgrade process, SCiMMA was in the midst of its first release cycle for new cyberinfrastructure systems, HOPSKOTCH and the hop-client (Figure  1 ), using an Agile Scrum methodology. 13, 48 hese systems were developed as part of SCiMMA's goal of supporting the MMA community's need for sustainable, industry-standard software tools and cyberinfrastructure for alert data management and sharing, in order to support the use-cases of its current stakeholders from LIGO, VRO, and IceCube.
11 he connection between SNEWS and SCiMMA began when a principle investigator from SNEWS met with leadership from the SCiMMA team at an in-person National Science Foundation "Harnessing the Data Revolution" grant meeting. 54 During the discussions, it became clear that HOPSKOTCH either provided or would provide the functionality required by the SNEWS 2. 0 system, and that SNEWS could serve as SCiMMA's first true user group. While none of the developers from SNEWS and SCiMMA had met, the members shared a common language of scientific software development and the principle investigator discussions indicated that cooperation could lead to potentially mutual benefits. Given this clear overlap of interests, the two groups proposed an informal, remote cooperation between SNEWS and SCiMMA. F I G U R E 1 Schematic of the services developed by SCiMMA (blue) and their integration into the MMA community (red).
The hop-client  48  provides MMA observatories, systems, and users with authenticated publish/subscribe access to the HOPSKOTCH cloud  13, 50  of Kafka topics. Initially, the cooperation began experimentally without direct collaboration between the groups. To test the hypothesis that HOPSKOTCH would meet SNEWS's requirements, the SNEWS implementation team carried out their own development using publicly available code from SCiMMA  48, 49  without directly engaging the SCiMMA client development team. This allowed SNEWS to explore the potential benefits of SCiMMA's systems without significant financial investment, as this required only partial effort from a handful of individuals without the need for a separate grant or funding source. To become more familiar with the code, the SNEWS team engaged with one of SCiMMA's virtual community workshops to further assess the value of a collaboration.
45 This phase was a traditional example of a user-group adopting a piece of open-source software, wherein developers modify another group's existing codebase to fit their own needs without coordination or direct interaction between the groups. Once SNEWS developers began integrating HOPSKOTCH into their early prototype of SNEWS 2. 0, they found that they required more extensive features that were not available in the current version of SCiMMA's systems. SNEWS already knew exactly what features they needed in SCiMMA's software, but did not have the developer effort necessary to implement them. SCiMMA had ample professional software development effort to provide for this need, but did not have an understanding of SNEWS's specific scientific requirements and software needs. Given this split of domain knowledge and software development talent between the organizations, the traditional open-source development strategy of making feature requests or independently customizing the software would not meet SNEWS's needs.
COLLABORATION APPREHENSION While the collaboration presented a unique opportunity to accelerate development, it brought risks to both teams. The concerns of each group provided an outline for the nature of the collaboration, which evolved through several stages of bilateral development effort and progress evaluations. To address these concerns (Section 5), the collaboration adopted its own Agile Scrum framework in parallel with SCiMMA's own Agile Scrum process. This new weekly sprint trailed SCiMMA's ongoing bi-weekly sprint. This promoted communication and drove software development in response to changes introduced by both the SNEWS requirements and the original SCiMMA stakeholders.
Concerning the reliance on external software Upon learning about SCiMMA and HOPSKOTCH, SNEWS considered using these services for their own development prototyping. This would allow SNEWS to actuate its initial development using the already-existing systems and resources provided by SCiMMA, which would be supported and maintained by professional software developers long-term. However, the main concern of the SNEWS team was that the development priorities of SCiMMA may not align with SNEWS's feature requests and bug reports, which could cause a potential latency or disconnect between SNEWS's requirements and SCiMMA's implementation. Moreover, the software could become unmaintainable if the SNEWS team was not engaged in the development process. Nonetheless, SCiMMA's available features and end goal appeared to fit all of the initially identified needs, so SNEWS decided to proceed.