dataesr/scientific-paragraphs-categorization huggingface dataset:
2) ParaView Reference  [13]  is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques (cf. Figure  7 ). The data exploration can be done interactively in 3D or programmatically by using ParaView's batch processing capabilities. ParaView itself is based on a client-server architecture, that enables the analysis of extremely large datasets, e. g. , as produced by the space weather use case (described in Section IV). The server then exploits the distributed memory computing resources on the HPC machine. While the server now runs in parallel on the supercomputers to process datasets of petascale size, the client can be executed on commodity hardware such as a laptop.
Within this eco-system, all functionalities available in TTK, as described in section III-D, can be exploited. Furthermore, interactive ray-tracing, based on Intel's OSPRay rendering library, can be performed. on virtual reality and user interaction, the seamless navigation allows end-users to explore massive geo-referenced datasets. This data exploration includes tasks such as measuring terrain structures, the identification of obstacles to guide relief forces, or to assess mitigation strategies for the disaster being studied.
As described in Section III-D, it has been also evaluated and demonstrated the usefulness of the topological proxies for urgent decision making, by combining their visualization with the simulation results. Therefore, a brushing-and-linking approach was integrated. End-users can directly interact with the persistence diagrams. They can brush data in the diagram and, then, the selected data is immediately highlighted in the 3D view of CosmoScout VR. With this feature, end-users can quickly identify and select prominent features within the ensemble simulation and directly spot the areas of interest. Finally, the user interface has been extended with a graph-based editor, as depicted in Figure  14 , to allow user-friendly data analysis. This HTTP and JavaScript-based user interface further enables direct communication with the VESTEC system by integrating the end-points of the RESTful API, described in section III.
C. PROBABILISTIC FOREST FIRE FORECAST AND MONITORING In case of a wildfire emergency, first responders require accurate prediction of: • the fire spread and behavior, • potential impact on communities and assets, • associated risk indices to optimize different suppression alternative. In this sense, Wildfire Analyst®applies research and technology together to improve the implementation of fire models  VOLUME 11, 2023  in real-time, situational awareness and decision-making in operational environments. The platform has a user-friendly software interface that includes real-time data integration (e. g. fire progression with hotspots), weather forecast services, high-resolution thematic cartography, improved fire modeling capabilities and viewers to interpret the outputs. Wildfire Analyst is the forest fire simulation software that has been integrated in the VESTEC system to address the specific emerging use case of wildfire monitoring and forecasting.
We therefore used the model described in Section IV by selecting Aedes albopictus as the vector species, chikungunya as the disease of interest, and the the Lazio region as the area to be analyzed. As shown in Figure  13 , in mosquito_init step the incident is initialized with a json file containing all the user-defined input parameters, the VESTEC system orchestrates several simulations (mosquito_simulation step) at the supercomputer, and the user has to wait until first results are available and streamed from the VESTEC system to CosmoScout VR. The user gets a feedback about the progress of simulations directly from the user interface. Initially, the maps, covering the selected area and used as input, are extracted from large datasets. These maps are used to perform the simulations and include meteorological data, the population density and the gross domestic product (GDP).
Model estimates can be obtained for any geographical area of interest, by using only data on local socio-demographic and eco-climatic conditions. Such estimates can be used in several circumstances and are especially useful when there is a lack of entomological and/or epidemiological records in the considered area, or the area to be monitored out-weight the monitoring capability of the public health system. The integration of the developed model with computational technologies provided by other partners in the VESTEC project greatly expands the potential for a useful public health application of the model results. First, the use of HPC systems enabled to speed-up the simulation. Second, the use of interactive and immersive visualization software represents a significant improvement with respect to the current state of the art that relies on static risk maps neglecting the temporal changes associated with the disease risks during the season for public health decision making.
This is performed by either starting ParaView from the local terminal or from the visualization node on the HPC machine. For example, to in-situ visualize in a privately owned cluster machine, VESTEC provides a script that automatically launches several ParaView instances and helps the user to connect them to the simulations. At this point, the user controls one (or several) ParaView instances that are ready to receive data from an active simulation. The user is then enabled to interact with said simulation, for example by pausing and resuming it. In spaceweather_simulation step, the VESTEC system launches and orchestrates several simulations of magnetic reconnection on the supercomputer. The user can either visualize the simulation while it is running or wait until all simulations are completed and then download the final results for an offline inspection.
Also, VESTEC supports the injection (performed by the Crisis management staff) of domain-specific knowledge into the system to guide an evolving simulation, so to influence the simulation with the mitigation actions prompted by the crisis manager. Lastly, VESTEC defines a standardized way to integrate new applications or other scenarios through text-based scripting languages (CWL and YAML). For further developments and contributions, the VESTEC System has been released as open source software on GitHub. 7 In order to demonstrate the scalability and effectiveness of VESTEC system in supporting urgent decision-makers, we have evaluated the system in three different use-cases: (i) Mosquito-borne diseases; (ii) space weather forecasting; (iii) probabilistic forest fire forecast and monitoring.
ACKNOWLEDGMENT (Markus Flatken, Artur Podobas, and Riccardo Fellegara contributed equally to this work. ) Author Contributions: Andreas Gerndt: leader and coordinator of the VESTEC Project; Markus Flatken and Riccardo Fellegara: design and development of in-memory visual analytics approaches and the extension of the visualization applications; Johannes Holke, David Knapp, Max Kontak, and Achim Basermann: definition and integration of numerical algorithms, data analytics, and optimization approaches in VESTEC system; Michael Nolde and Christian Krullikowski: preparing Earth observation data from space for urgent decision-making as input for simulations; Nick Brown, Rupert Nash, Gordon Gibb, and Evgenij Belikov: background to urgent computing, to the system architecture design, supercomputing orchestration, and integration of high-velocity sensor data into ensemble simulation environments; Artur Podobas, Steven W.
2% in 2013 to >80% in 2017. In additional, emission uncertainties, caused by the spatial mismatch between the locations of emissions and spatial proxies, can lead to additional uncertainties in air-quality simulations, especially in small-scale regions In this study, we developed a new version of dynamic urban-street scale model (IAQMS-street v2. 0) using a two-way coupling between the MUNICH street-scale model and NAQPMS regional air-quality model based on previous version of IAQMS-street v1. 0  (Wang et al. , 2022) , the hourly variation and spatial distribution of O3 and NOx concentrations were simulated in Beijing during August 2021. An upscaling module is added to transfer the pollutants from street scale model to regional model, which is not considered in IAQMS-street v1. 0. We used image-recognition technology based on road monitoring and traffic big data to create high-resolution traffic-emission inventories.
(  2020 ). An one-way integrated air-quality modeling system named IAQMS-street v1. 0 have been developed in previous research  (Wang et al. , 2022) , In IAQMS-street v1. 0, the pollutants background concentrations in study period were simulated by NAQPMS, and as the input data of MUNICH, the background concentration were provided for the simulation of pollutants at street scale. MUNICH was used as a standalone model with a one-way coupling approach. The influence of mass transfer of pollutants from streets to urban background was not considered in IAQMS-street v1. 0. In this study, the IAQMS-street was further developed by adding an upscaling module to achieve the feedback of MUNICH to NAQPMS (named IAQMS-street v2. 0), thereby influencing the variation in background concentrations. The simulation results from NAQPMS and MUNICH were two-way coupled to represent the hourly variation and spatial distribution of air pollutants at the regional scale and local scale.
In IAQMS-street v2. 0, the influence of mass flux from street in MUNICH to grid cell in NAQPMS are considered to update the background concentration. An upscaling module is added in IAQMS-street v2. 0: the pollutants were transferred from street to urban background by Q vert in Eq. (  3 ), the mass flux of pollutants is setting as an added traffic emission C emiss in Eq. (2) to calculate the concentration in grid cell at next time step. The pollutants concentration in grid cell at the next time step were calculated by pollutants mass in street and background mass in grid cell. C grid = M grid V grid = M bg +M street V grid , (5) Where C grid is the mean pollutants concentration in grid cell, M street is the pollutant mass in streets, M bg is the background pollutant mass, V grid is grid volume which include street volume.
In this study, NAQPMS and MUNICH were two-way coupled and applied to Beijing. The coupling schematic diagram of NAQPMS and MUNICH is show in Fig. 1. The simulation results of MUNICH at street scale are related to the simulated concentration at bottom layer of NAQPMS. In the two-way coupled module, the background concentration and meteorological data were simulated by NAQPMS and provided to street scale by downscaling module, and the influence of mass flux of pollutant from street to regional background were considered to NAQPMS by upscaling module. MUNICH was located within the lowest NAQPMS layer. After the calculation of the mass flux between the urban canopy and urban background, the upscaling module would transfer the pollutants from MUNICH to NAQPMS to compute the polllutant concentrations in bottom layer of NAQPMS.
Traffic emission model As essential inputs to regional air quality model, emission inventories are important to the simulation results. The additional uncertainties of simulation results arose for grided emissions with finer resolutions because of spatial errors, especially in urban areas  (Zheng et al. , 2017). In this study, on-road traffic emissions were calculated based on real-time traffic speed data and road-vehicle recognition technology to reduce additional uncertainties. To obtain dynamic high-resolution traffic emission inventories, a real-time on-road traffic emission model (ROE) was developed by  Wu et al. (2020) , the street network traffic emissions was calculated by using real-time traffic-speed data, traffic volume, and vehicle emission factors. Navigation application such as Gaode Map and Baidu Map provide the original traffic-speed data.
Based on traffic big data, the traffic volume was calculated by using the Underwood speed-volume calculation formula  (Greenshields et al. , 1961) , with the proportion of different vehicles on the road being set before using the ROE. For a detailed description of ROE, we refer to  Wu et al. (2020) ; for the model configuration of traffic emissions in Beijing, we refer to  Wang et al. (2022). It is worth noting that because of the implementation of road-traffic control measures (e. g. , diesel vehicles below the National Grade IV are forbidden from entering the Fifth Ring Road in Beijing), the proportions of vehicles in urban and suburban areas were different in Beijing. To obtain information on road vehicles on urban and suburban roads in Beijing, the traffic flow and proportion of vehicles on different roads were counted using a real-time object detection system (YOLOv5s; https://github.
Acknowledgements. We thank the  National Key Scientific and Technological Infrastructure project " Earth System Science Numerical Simulator Facility " (EarthLab). We acknowledge  Xuemei Wang  and Luolin wu from  Jinan university  for providing code and guidance on the use of ROE model. Financial support. This work is funded by the  National Key R&D Program of China  (Grant No. 2022YFC3700703 ). https://doi. org/10. 5194/gmd-2023-5  Preprint. Discussion started: 24 March 2023 c Author(s) 2023. CC BY 4. 0  License.
The aim of this paper is to extend and exploit the methodologies illustrated in  [3, 14, 15] , and to study the usability of an explanation method into a real medical setting. In particular, we focus on skin lesion diagnosis from images. We rely on the labeled dataset available from the ISIC 2019 (International Skin Imaging Collaboration) image classification challenge. We train a state-of-the-art deep learning classifier using the ResNet  [16]  architecture on the dataset. After that, we explain the classifier decisions through abele  [14]. In this way, the practitioner can easily reason on top of the exemplars and counter-exemplars returned by the explainer. Our goal is to assess to which extent these exemplar and counter-exemplar explanations are effectively useful through a user study involving humans.
We design the experiment as a survey where participants are asked to address certain tasks on the basis of the classification outcome and the explanations. The novel contributions of this study w. r. t. existing ones consist of the following aspects. First, further refinement and evaluation of abele w. r. t. a real case study in the medical domain. Second, we introduce an analysis of the latent space of the adversarial autoencoder provided by abele. Third, we develop a user visualization module to analyze the explanations returned by abele. Fourth, we conduct a user study with domain experts, beginners, and unskilled people to assess the effectiveness of the explanations provided by abele. The results of the survey show that the usage of explanations increases trust and confidence in the automatic decision system.
ResNet classifier In order to provide a black box classifier that performs sufficiently well for downstream learning steps, we choose to train a neural network with an architecture powerful enough to accomplish image classification. In particular, we selected a ResNet, a popular architecture providing validated performance on many complex datasets and tasks  [16]. Instead of training the ResNet from scratch, we choose to perform a transfer learning task with a ResNet pre-trained on the Ima-geNet dataset. This training strategy is largely applied for the case where the number of data is limited with respect to the complexity of a network  [33]. To perform the transfer learning, we replace the last fully connected layer with the newly initialized one. The number of output dimensions is adapted to the number of classes in the dataset.
ABELE explainer abele is a local model agnostic explainer that takes as input an image x and a black box classifier b, and returns (i) a set of exemplar and counter-exemplar images, and (ii) a saliency map. Exemplars and counter-exemplars are images synthet- Fig. 2  Latent local rules extractor (llore) module,  [14]  ically generated and classified with the same outcome as x, and with an outcome other than x, respectively. They can be visually analyzed to understand the reasons for the decision. The saliency map highlights the areas of x that contribute to its classification and areas that push it into another class. In short, abele works as follows. First, it generates a neighborhood H in the latent feature space exploiting an Adversarial Autoencoder (AAE)  [34]. The AAE architecture (Fig.
1 ) includes an encoder: R n → R k , a decoder: R k → R n and a discriminator: R k → [0, 1] where n is the number of pixels in an image and k is the number of latent features. The image x ∈ R n to be explained is passed as input to the autoencoder where the encoder returns the latent representation z ∈ R k using k latent features with k n. The neighborhood generation of H (neighgen module in Fig. 2 ) may be accomplished using different strategies. In our experiments, we adopt a genetic approach maximizing a fitness function like in  [35]. The module in Fig. 2  is named llore, Latent Local Rules Extractor, as a latent variant of lore  [35]. After the generation process, for any instance h ∈ H , abele exploits the disde module (Fig. 1   The overall effectiveness of abele lies in the goodness of the encoder and decoder function adopted: the better is the autoencoder, the more realistic and useful will be the explanations.
Progressive growing adversarial autoencoder We summarize here the customization of abele we carried on in order to make it usable for complex image classification task. Details can be found in  [3]. Generative Adversarial models are generally not easy to train as they are usually affected by a number of common failures. These problems vary from a diversified spectrum of failures in convergence to the famous mode collapse  [36] , the tendency by the generator network to produce a small variety of output types. Such problems mainly arise from the competing scheme generator and discriminator are trained on. In addition, we often face the further complication to deal with real world datasets that are far from ideal: fragmentation, imbalance, lack of uniform digitization, shortage of data are primary challenges of big data analytic for healthcare.
Training an AAE in a standard fashion to reproduce samples from ISIC dataset without taking special care of all issues mentioned above resulted in extremely poor performance, mostly due to a persistent collapse mode. In order to overcome such generative failure and dataset limitations, we implemented a collection of cutting edge techniques that altogether are capable of addressing all the issues we mentioned and successfully training an AAE with adequate performance. In particular, we address mode collapse using ad hoc tricks like Mini Batch Discrimination  [37]  and Denoising autoencoders  [38]. As model, we implemented a Progressive Growing Adversarial Autoencoder. Progressive Growing GANs  [39]  have been introduced as an extension of GANs. Progressive growing helps to achieve a more stable training of generative models for high resolution images like in our case.
Dataset The International Skin Imaging Collaboration (ISIC), sponsored by the International Society for Digital Imaging of the Skin (ISDIS) proposed the skin lesion analysis towards melanoma detection challenge to improve the international effort in melanoma diagnosis. 1 The challenge consists in 1 https://challenge2019. isic-archive. com/. developing a classifier to recognize among nine different diagnostic categories of skin cancer: MEL (Melanoma), NV (Melanocytic nevus), BCC (Basal cell carcinoma), AK (Actinic keratosis), BKL (Benign keratosis), DF (Dermatofibroma), VASC (Vascular lesion), SCC (Squamous cell carcinoma), UNK (Unknown, none of the others/out-ofdistribution). The dataset is composed of a training set of 25,331 images of skin lesions and their category (labels); a test set of 8238 images of which the label is not publicly accessible.
838 of balanced multi-class accuracy on the test set. Also, since the images are captured under controlled conditions, there is no significant distributional shift in the shuffled data samples used to create the training set and the validation sets, so the difference between them is small and the cross-validation is not necessary. Since the amount of data is small, we fine-tune an off-the-shelf pre-trained ResNet on our dataset to avoid overfitting. The architecture is chosen beforehand according to its past performance on the image classification task and its computational complexity, not according to its performance on our dataset. The learning rate is selected from a sparse grid and set to 10 -4 according to the convergence speed and accuracy on the validation set. Finally, we keep the model with the best score on the validation set.
e. , 224 × 224, it is common in the literature to choose a number of latent features that varies between 64 and 512. Data augmentation was necessary to overcome scarcity and imbalance of the dataset. Mode collapse was greatly reduced, and we were able to generate variegate and good quality skin lesion images (Fig. 4 ). abele equipped with PGAAE, can produce meaningful explanations, as demonstrated in a survey of real participants, which is discussed in the following section. We present here the novel visualization module for the explanations returned by abele. The module helps users understand both the suggestions made by the black box model and the explanations produced by abele. Figure  5  shows a screenshot from a web application  3  we implemented to present to the user the outcome of our system on a specific instance.
The application is divided into two sections, with the upper section showing the analyzed image, the classification made by the black box, and a synthetic counter-exemplar image generated by abele. In Fig. 5 , we have an example of an image x of Melanoma and a similar synthetic image classified as Dermatofibroma as a counter-exemplar. The neighborhood generated by abele, represented as a list, provides a glimpse into the diverse instances in the latent space surrounding the analyzed image. The counter-exemplar, i. e. an image classified differently from the original instance, is selected among this list as the image that minimizes the euclidean distance with x (over the latent space) and maximizes the classification prediction but w. r. t. a different label. Finally, the bottom section of the module displays four exemplars, a set of images generated by abele that have the same label assigned by the black box to x.
Survey structure The survey is composed of ten questions, each presenting a different medical image case. Each question is organized into four different points, following the same structure for the ten cases. We denote each question with its progressive number, i. e. , Qi with i ∈  [1, 10]. Point 1 (P1). The participants are presented with an unlabeled skin lesion image randomly chosen among the dataset and its explanation as generated by abele and presented by the visualization module. In particular, we presented two exemplars and two counter-exemplars of another lesion class to the user. Participants were asked to classify the given image among two different given classes exploiting the explanation. This point aims to understand if the explanations returned by abele significantly help in separating different images, even for non-expert users.