dataesr/scientific-paragraphs-categorization huggingface dataset:
The main run function now takes a dictionary of arguments, and if the "YODASTREAM" term contains a Python StringIO -which can be converted to and from a C++ stringstream -then Contur will run on that stream. When run like this, the Contur main will return a dictionary containing various statistics. Which outputs appear in the dictionary is controlled by the "YODASTREAM_API_OUTPUT_OPTIONS" argument, and the options here are summarised by Table  5.
Acknowledgements We thank our colleagues in the  GAM-BIT Community  for helpful discussions and comments. For computing resources, we thank  PRACE  for awarding us access to Marconi at CINECA and  Joliot-Curie  at  CEA. Computing resources were also provided by  Sigma2 , the  National Infrastructure for HPC in Norway , under project  NN9284K. AF was supported by the  National Natural Science Foundation of China (NNSFC)   Research Fund for International Excellent Young Scientists grant   1950410509. ABu and JB were supported by the  UK Science and Technology Facilities Council (STFC)  Consolidated Grant programme awards  ST/S000887/1  and  ST/S000666/1  respectively, ABe by  STFC  grant  ST/T00679X/1 , and TP by the  STFC Doctoral Training Programme. The work of CB was supported by the  Australian Research Council Discovery Project  grant  DP210101636.
AK and AR were supported by the  Research Council of Norway FRIPRO  grant  323985. TEG was funded by the  Deutsche Forschungsgemeinschaft (DFG)  through the  Emmy Noether Grant  No. KA 4662/1-1. The work of VA was supported by the  European Union Framework Programme for Research and Innovation Horizon 2020  ( 2014-2021 ) under the  Marie Sklodowska-Curie Grant  Agreement No. 765710. YZ was supported by the  NNSFC  under grant No. 12105248  and  12047503  ( Peng-Huan-Wu Theoretical Physics Innovation Center ). MJW is supported by the  ARC Centre of Excellence for Dark Matter Particle Physics  ( CE200100008 ). We made use of pippi v2. 2 [202] for this work.
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and International Conference On Medical Image Computing & Computer Assisted Intervention (MICCAI) 2017. Twenty-four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually blind reviewed by independent three radiologists.
All of these methods were validated on either relatively small datasets, private data sets or the segmentations are available either for liver or liver tumors. Therefore, it remains difficult to judge which methods will worthwhile to pursue in research and clinical practice. Furthermore, the exact performance of the best algorithms available today can not be can not be determined unequivocally. Moreover, and how well current segmentations generated by automated computational methods compare with human expert groups ratings. In order to judge the current state-of-the-art in automated liver and liver tumor segmentation and compare different methods, we organized the Liver Tumor Segmentation Challenge (LiTS) in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and with the conference on Medical Image Computing and Computer Assisted Interventions (MICCAI) 2017.
Compared to ISBI 2017 we added tasks for liver segmentation and tumor burden estimation for MICCAI 2017. We prepared a unique dataset of 201 CT scans of patients with hepatocellular carcinoma (HCC) including expert ground-truth segmentations of liver and tumor lesions and made it publicly available. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. As shown in figure  1  the heterogeneity in liver and lesion contrast is very large among subjects. The contrast, size and shape of liver lesions varies a lot among subjects as shown in figure  1  and in particular their corresponding histograms. This complexity of contrast differences make it difficult for intensity-based methods to generalize to unseen test cases from different clinical sites.
A fully automatic liver and tumors segmentation allows easier computation of tumor burden and consequently simplifying the surgical liver resection planning. Our contributions are three-fold. First a new reference dataset of 201 abdominal CT Volumes (131 training, 70 test) including liver and liver tumor ground truth segmentations is released. Second, the set-up and learnings of our medical image benchmark, which was presented as two Grand Challenges organized in conjunction with ISBI 2017 and MICCAI 2017 is described. Third, the resulting state-of-the-art algorithms of the benchmark are reviewed, evaluated and ranked. The paper is structured as follows. We review the current state-of-the-art in automated liver segmentation and respectively liver tumor segmentation as well as relevant public datasets of liver and liver tumors, benchmark efforts in other biomedical image analysis tasks, in Section II.
Moreover, variations and further enhancement of SSM such as 3D-SSM based on an intensity profiled appearance model  [29] , incorporating non-rigid template matching  [30] , initialization of SSMs utilizing an evolutionary algorithm  [24] , hierarchical SSMs  [31] , and deformable SSM  [26]  had been proposed to solve liver segmentation tasks automatically. (2) Methods based local image features and spacial context A probabilistic atlas (PA) is a specific model for a population of images with parameters that are learned from a training dataset. Park et al. proposed the first PA utilizing 32 abdominal CT series for registration based on mutual information and thin plate splines as warping transformations  [32]  and a Markov random field (MRF)  [32]  for segmentation. Further proposed atlas based methods differ in their computation of PA and the way the PA it is incorporated into the segmentation task.
Moreover, a 3D CNN is has been proposed to to learn a subjectspecific probability map of the liver. Afterwards global and local appearance information is incorporated into a segmentation model which is globally optimized for surface evolution  [59]. (5) Semi-automated methods Automated segmentation methods require a large dataset for modeling the different variation of livers. Semi-automatic methods were generally regarded as more robust especially in the case of irregular livers. However, segmentation results of semi-automatic methods are reliant on the operator skill and can differ. Semi-automated methods differ in their required degree of manual input. Popular methods such as region growing generally require often costly pre-and post processing in form of manual seed region initialization and segmentation refinement.
Medical Image Segmentation Benchmarks In the last decade benchmarks have played an increasingly important role in the medical image analysis community. Between 2010 and 2017 overall more than 90 medical imaging benchmarks have been organized. The most prominent tasks were segmentation, detection, classification and prediction tasks. For the years of 2018/19, already 39 known benchmarks have been started and/or announced. [106]  Shared characteristics of benchmarks, also known as "challenges", are that independent individuals and groups optimize their methods based on an open available training dataset provided by the challenge organizer. The optimized methods are then applied to an test dataset. Therefore necessary conditions are set for a structured comparison and assessment of different methods.
Well established benchmarks often set guiding principles by the provided dataset and evaluation methods and therefore define a baseline for future improvements for certain medical imaging problems  [107]. Furthermore, when new data is added to a benchmark dataset, annotation and evaluation protocols can be applied to ensure a consistent and continuous quality for future learning methods. The ground truth of the test dataset is stored privately on an online platform which provides automatic evaluation capabilities. This avoids inadvertent results by overtrained methods or by methods trained on wrong training and test data splits. Therefore a central evaluation instance ensures representative and comparable results. Past medical imaging benchmarks focused on a wide variety of organs. Particularly assessing the heart region by (semi-)automated segmentation of arterial wall thickness  [108] , endocardial three-dimensional ultrasound  [109] , right ventricle  [110] , coronary lumen on computed tomography angiography  [107] , and the whole heart using multiple modalities  [111].
The ISBI 2017 and MICCAI 2017 Benchmark Challenges The first LiTS benchmark was organized on April 18, 2017 in Melbourne, Australia, in a workshop held as part of the IEEE ISBI 2017 conference. During Winter 2016/2017, participants were solicited through private e-mails as well as public e-mail lists, social media and the IEEE ISBI workshop announcements. Participants had to register at our online benchmarking system hosted on www. codalab. com and could download annotated training data and test data. They were asked to submit a four page summary of their algorithm after successful submission to the codalab plattform. Submissions were reviewed by the organizers and a final group of 17 participants for the ISBI and 26 participants for MICCAI were invited to contribute to the challenge and this work.
The LiTS benchmarking dataset contains 201 computed tomography images of the abdomen. 194 CT scans contain liver lesions. The data was collected from seven academic and clinical institutions arround the world, including the Technical University of Munich Germany, Ludwig Maxmilian University of Munich Germany, Radboudumc Netherlands, Polytechnique Montral and CHUM Research Center Canada, Tel Aviv University and Sheba Medical Center and The Hebrew University of Jerusalem and Hadassah University Medical Center Israel and IRCAD France. In particular the LiTS benchmark includes the public 3DIRCAD-B dataset in the training-set. The data was acquired with different scanners and scanning protocols. The examined patients suffered from primary to secondary liver tumor and metastases. The data had been manually annotated with two labels (liver and lesion) by a trained radiologists or oncologists.
The quality of the test segmentations was verified by three experienced radiologists in an blinded review. The data has been split into 131 training and 70 test cases. The test cases were published without annotation. After publishing the test images the participants had to infer their submission segmentations for the test images. To participate in the benchmark the participants had to upload their submission segmentations to the online benchmarking platform accepted. The online benchmarking platform automatically computed performance scores. Of the 17 groups that participated in the benchmark at ISBI, five methods presented their work at the IEEE ISBI 2017 workshop in Melbourne. During the plenary discussions it became apparent that using only segmentation and surface metrics for lesions are not sufficient and do not account for missed lesions.
The training data and test data for the benchmark was identical to the ISBI benchmark. In comparison to ISBI, the MICCAI benchmark evaluate besides lesion segmentation performance also liver segmentation performance and accuracy in determining the tumor burden for each subject. The workshop at the MICCAI 2017 proceeded in a similar fashion to the ISBI edition: the TOP participating teams were invited to present their methods in short 20 minute presentations. Altogether, we report four different test results from the two events: one summarizing the ISBI 2017 tumor segmentation results (Section 4. 4), the second for MICCAI 2017 results on liver segmentation (Section 4. 5. 1), tumor segmentation results (Section 4. 5. 2) and automatic tumor detection and tumor burden evaluation (Section 4. 5.
The LiTS image dataset The image data for the LiTS challenge derives from several clinical sites including Ludwig Maxmilian University of Munich, Radboud University Medical Center of Nijmegen, Polytechnique & CHUM Research Center Montral, Tel Aviv University, Sheba Medical Center, IRCAD Institute Strasbourg and Hebrew University of Jerusalem. The studied subjects suffer from diverse types of liver tumor diseases. Primary tumor disease such as HCC as well as secondary liver tumors and metastasis derived from colorectal, breast and lung cancer are present. The tumors have varying contrast enhancement such as hyper or hypo-dense contrast. The images represent a mix of pre-and post-therapy abdomen CT scans. The image data was acquired with different CT scanners and acquisition protocols. The image data is also very diverse with respect to resolution and image quality.
Online Evaluation Platform A central element of the LiTS benchmark is its online evaluation tool. We used Co-daLab, which was developed at Stanford University and Microsoft Research. On Codalab participants can download annotated training and blinded test data, and upload their segmentations for the test cases. The system automatically evaluates the performance of the uploaded segmentation maps, and makes the overall performance available to the participant. Average scores for the different tasks of liver and lesion segmentation as well as tumor burden estimation are also reported online in a leader board. The LiTS benchmark data is publicly available at Codalab, allowing any team around the world to develop and test novel liver lesion segmentation algorithms. Ground truth segmentation files for the LiTS test data are hosted on the Codalab but their download is protected and not possible for participants.
Loss function Weighted cross entropy is used by many winning methods (e. g. Han, Zou, Li) as a loss function to mitigate the negative effects of high class imbalance present in the LiTS dataset. In addition, Li applies so called hard pixel mining learning and only uses the 10% of voxels with the highest misclassification rate in his loss calculations to force the network to focus on minimizing the error of the up until that point most difficult voxels. Other common loss functions among the best performing methods were Dice loss (e. g. Vorontsov et al) and Jaccard loss (e. g. Yuan), which are both derived from the respective overlap metrics by inverting or subtracting the Dice or Jaccard score from 1 to obtain a quantity to be minimized. This allows the network to directly optimize its parameters according to the actual final evaluation metric instead of indirectly via cross entropy and thereby also implicitly incorporate class imbalance in its learning process without the explicit introduction of class specific weights other class re-balancing techniques.
Further, we recommended to provide multiple ground truth segmentation. However modern methods e. g. Deep Convolutional Neural Networks are able to overcome operator dependent annotation or errors in the training data that are present when only a single rater labels each case. Comparing results of LiTS's to previous liver (and tumor) challenges it is adviced to provide larger training data sets because state-of-the-art methods highly benefit from larger training datasets. Moreover, we suggest that future organizers consider the desired outcome before offering normalized and preprocessed data. The advantage of offering a normalized dataset with standardized resolution and homogenized gray-level distributions or even crops helps to compare the trained models provided by participants. Furthermore, it supports potential participants to overcome initial difficulties and can lead to more contributions.
Summary and Conclusion In this paper we presented the LiTS liver tumor segmentation benchmark. We generated the larges public dataset available for liver and liver tumor segmentation tasks evaluated, analyzed and ranked a high number of state-of-the-art medical image segmentation methods. While liver tumor segmentation is difficult, currently available algorithms can reach Dice scores of over 95% for liver segmentation and over 70% for liver tumor segmentation. For analyzed methods, segmenting small liver tumor regions automatically still remain as a difficult task. This suggests that, in addition to focus further on heterogenious liver tumor structures, future improvements can be achieved by investigating on methods focusing on very local image structures.
In this paper, a Universal Underwater Software Defined Modem (UniSDM) architecture is proposed that may operate in different modes (acoustic, magnetic induction, optical and RF), in order to utilize the advantages of each mode and accordingly satisfy the requirements of many latest use cases in underwater communication systems. A detailed description of the novel UniSDM architecture is presented first. The novelty of this architecture is its flexibility, i. e. , allowing the designers to produce a device that may include any type of modes operating seamlessly and jointly by exchanging data, control and synchronization. Many challenges, including high system costs and coordination between different modes, are addressed in the paper. Moreover, numerical evaluation is conducted to assess the performance of the proposed UniSDM architecture.
Finally, MI allows reliable short-range communications depending on water conductivity with high propagation speed at 33 m/µs  [12] ,  [13]. It is well-understood that the features of different communication modes are complementary. In other words, one transmission mode can be preferred over the others depending on the transmission requirements, water characteristics, and transmission range. The contributions of this paper are: • Based on existing design principles of the software defined radios (SDRs), we propose a novel universal software defined modem (UniSDM) architecture  [1]  that integrates acoustic, MI, optical and RF communication paradigms to realize multi-mode device operations. The UniSDM architecture design can significantly improve the system performance by allowing simultaneous and joint operations between different communication modes, improves synchronization accuracy, and reduces device cost and size.
• Inter-mode cooperative operation is difficult, since it requires precise synchronization (e. g. symbol and carrier phase synchronizations). Note that an integrated acoustic and MI software defined modem architecture was recently studied in  [23]. They present a proof-of-concept of a cooperative MIMO system whose nodes are implemented by using off-the-shelf SDRs. The paper has the following shortcomings: 1) Only two modes (acoustic and MI) are considered. 2) The implementation does not consider the reconfigurability, flexibility or the possibility to provide additional modes. 3) They only implement cooperative 2 × 1 MISO acoustic data transmission supported by synchronization of time and frequency in MI mode.
III. THE UNIVERSAL SOFTWARE DEFINED MODEM ARCHITECTURE We propose the UniSDM architecture that enables joint/simultaneous operations of multiple modes, including cooperative communications, utilizing two or more modes at the same time. The proposed UniSDM has a universal physical layer (PHY) that combines functions of the separate modes, and provides synchronization and signal processing. Hence, it is capable of supporting any physical front-end or several front-ends simultaneously. The advantages of the UniSDM are: • Increased throughput and decreased latency. The modems can efficiently use any mode that is most appropriate for the current channel state and data communication requirements for different use cases. • Sound pollution reduction. Switching to optics/MI/RF decreases the quantity of acoustic transmissions, thus decreasing the acoustic interference and improving ecological sustainability.
C. The Mode Cooperation To establish communications between two nodes, the UniSDM device shall decide which mode or modes it is going to use. To do that, it does channel probings by different modes, and accordingly establishes a link. Once one or more links are established, the links' channel state information (CSI) is collected at the BBU-C to select one of the modes to perform data transmission based on a given criteria (discussed below). In short, the UniSDM operation is realized as follows: 1) Probe the available modes and establish communication links. 2) Request the transmission, and select a particular mode according to a particular criteria given below. 3) Send the data (payload).
For example, a lowest-delay route can include multiple non-acoustic hops, thus the number of hops can no longer be used as a good routing metric. Thus, future research on the multi-mode underwater communications, such as media access and routing protocol design, would have to count for these features. Along with the universal access to the communication modes and technologies that use them, the software defined architecture would facilitate additional applications, such as navigation and monitoring. These problems can also be addressed in the multi-modal paradigm, e. g. the navigation may combine radio navigation (including the use of global navigation satellite systems) and underwater acoustic navigation (including long baseline and ultra-short baseline positioning) approaches. The UniSDM can be used as well to monitor the aerial RF channel and to track underwater acoustic channel at the same time.
Results: This study presents the first set of harmonized promotional and educational materials and tools (ie, educational cards, educational and promotional videos, extended brochures, flyers, posters, and puzzles) for vaccine clinical research (eg, COVID-19 vaccines). These tools inform the public about possible benefits and disadvantages of trial participation and build confidence among participants about the safety and efficacy of COVID-19 vaccines and the health care system. This material has been translated into several languages and is intended to be freely and easily accessible to facilitate dissemination among VACCELERATE network participant countries and the European and global scientific, industrial, and public community.
VACCELERATE Posters The developed posters (see Multimedia Appendix 1) are A0-sized (84. 1×118. 9 cm) and depict human characters (ie, medical staff, minorities, and older individuals, among others) and a QR code including a URL for the VACCELERATE Volunteer Registry website  [30] , as well as catchy slogans such as "Be the missing piece" and "Together we can tackle the COVID-19 pandemic. " Finally, the posters have been translated into 7 languages, namely English, French, German, Greek, Hebrew, Italian, and Spanish, in collaboration with individual partners and national coordinators of the VACCELERATE Consortium. The promotion of the VACCELERATE Volunteer Registry for participating in COVID-19 vaccine trials could be achieved by affixing these prototype posters to a wall at sites or in buildings with high visibility (eg, hospitals, universities, malls, and public buildings).
VACCELERATE Extended Brochure (Booklet) The extended VACCELERATE brochure (see Multimedia Appendix 1) comprises valuable information regarding the VACCELERATE Volunteer Registry, such as "What is the VACCELERATE Network," the mission and objectives, the current promotion activities, the importance of registration, and a description of the registration process. The brochure also contains key information for the volunteers, such as the possibility to withdraw their registration at any time. Finally, the extended brochure also includes a QR code that directs the interested reader to the VACCELERATE Volunteer Registry website  [30]  after scanning it with a smart device.