dataesr/scientific-paragraphs-categorization huggingface dataset:
With this challenge, we aimed to separate the evaluation of the methods from the data generation by using datasets that were generated but not shared with researchers and as such all participants in the challenge were naive to the data. As a resource to the community, we have shared results of the challenge, including submission rankings, methods descriptions and code, and full feature sets, as well as the specific segmentation and training/test splits of the data used in the challenge, so that other researcher can continue to improve on these methods (www. synapse. org/DigitalBiomarkerChallenge). Participants in this challenge used an array of methods for feature extraction spanning unsupervised machine learning to hand-tuned signal processing. We did not, however, observe associations between types of methods employed and performance with the notable exception that the top two teams in the diagnostic biomarker challenge based on the mPower data (SC1) generated features using CNNs while top-performing teams in SC2.
1-2. 3 that used the smaller L-Dopa dataset used signal processing-derived features (though a CNN-based feature set did rank 2nd in SC2. 3). The top-performing team in SC1 significantly outperformed the submissions of all remaining teams in the subchallenge. This top-performing team was unique in its use of data augmentation, but otherwise used similar methods to the runnerup team. Consistently, deep learning has previously been successfully applied in the context of detecting Parkinsonian gait  16. However, given CNNs' relatively poorer performance in SC2, which utilized a substantially smaller dataset, we speculate that these methods may be most effective in very large datasets. This was further supported by the observation that the top SC1 model did not perform well in the oldest study subjects which correspond to the smallest age group.
If the sample size is indeed a driver of the success of CNNs, this suggests that applying these methods to most digital validation datasets will not be possible as they currently tend to include dozens to hundreds of individuals in contrast to the thousands available in the SC1 data and the typical deep learning dataset  17. Traditionally, clinical biomarkers have a well-established biological or physiological interpretation (e. g. , temperature, blood pressure, serum LDL) allowing a clinician to comprehend the relationship between the value of the marker and changes in phenotype or disease state. Ideally, this would be the case for digital biomarkers as well, however, machine learning models vary in their interpretability. In order to try to understand the features derived from machine learning models, we computed correlations between the CNN-derived features submitted by teams with signal processing-based features, which are often more physiologically interpretable.
For example, we observed differences in performance between males and females in the top submissions, as well as the relatively better performance in younger patients (57-65). This can be due to imbalances in the dataset such as more young participants or more data from male participants leading to better-fitting models in those populations, but could also be due to disease etiology and symptoms. We know from previous work that models can be affected by confounders  20, 21  and that there are gender effects of the disease  22, 23. If the latter is true, it is possible that different models and features might be necessary to capture different aspects of the disease as a function of age and gender. In the extreme, we might even consider personalized models  15. For example, it stands to reason that the standard for normal gait differs in older people relative to younger people.
Winning method sub-challenge 1: Team Yuanfang Guan and Marlena Duda The winning method by team 'Yuanfang Guan and Marlena Duda' used an end-to-end deep learning architecture to directly predict PD diagnosis utilizing the rotation rate records. Separate models were nested-trained for balance and gait data, and the predictions were pooled by average when both are available. Rotation rate x, y and z were used as three channels in the network. Each record was centered and scaled by its standard deviation, then standardized to contain 4000-time points by 0-padding. Data augmentation was key to prevent overfitting to the training dataset, and was the primary difference in performance compared to the next ranking deep learning model by 'ethz-dreamers'. The following data augmentation techniques were included to address the overfitting problem: (a) simulating people holding phones in different directions by 3D random rotation of the signal in space based on the Euler rotation formula for a standard rigid body, vertex normalized to unit = 1, (b) time-wise noise-injection (0.
TDA of mPower features To construct the topological representation, we leveraged the open source R implementation of the mapper algorithm  13  (https://github. com/ paultpearson/TDAmapper). As a preprocessing step, we considered only the features (median value per subject) from the six top-performing submissions in SC1, and centered and scaled each feature to obtain a zscore. We then reduced the space to two dimensions using MDS and binned the space into 100 (10 × 10) equally sized two-dimensional regions. The size of the bins was selected so that they have 15% overlap in each axis. A pairwise dissimilarity matrix based on Pearson correlation was calculated as 1-r from the original multi-dimensional space, and used to cluster the samples in each bin individually (using hierarchical singlelinkage clustering).
ACKNOWLEDGEMENTS The  Parkinson's Disease Digital Biomarker Challenge  was funded by the  Robert Wood Johnson Foundation  and the  Michael J. Fox Foundation. Data were contributed by users of the Parkinson mPower mobile application as part of the mPower study developed by  Sage Bionetworks  and described in Synapse [https://doi. org/10. 7303/ syn4993293]. Resources and support for J. S. were provided by Elder Research, an AI and Data Science consulting agency. M. D. was supported by  NIH   NIGMS Bioinformatics Training Grant  ( 5T32GM070449-12 ). J. F. D. was supported by a postdoctoral fellowship from the  Canadian Institutes of Health Research. L. L. E. reports grants from the  European Research Council ERC  ( 677943 ),  European Union 's  Horizon 2020 research and innovation programme  ( 675395 ),  Academy of Finland  ( 296801 ,  304995 ,  310561  and  314443 ), and  Sigrid Juselius Foundation , during the conduct of the study.
P. S. is an affiliated PhD fellow at the  Max Planck ETH Center for Learning Systems. Y. G. is supported by  NIH   R35GM133346 ,  NSF # 1452656 ,  Michael J. Fox Foundation  # 17373 ,  American Parkinson Disease Association   AWD007950. Cohen Veterans Bioscience  contributed financial support to  Early Signal Foundation 's costs (U. R. , C. E. , and D. B. ).
Managing complex disaster situations is a challenging task because of the large number of actors involved and the critical nature of the events themselves. In particular, the different terminologies and technical vocabularies that are being exchanged among Emergency Responders (ERs) may lead to misunderstandings. Maintaining a shared semantics for exchanged data is a major challenge. To help to overcome these issues, we elaborate a modular suite of ontologies called POLARISCO that formalizes the complex knowledge of the ERs. Such a shared vocabulary resolves inconsistent terminologies and promotes semantic interoperability among ERs. In this work, we discuss developing POLARISCO as an extension of Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). We conclude by presenting a real use-case to check the efficiency and applicability of the proposed ontology.
the core system, which is composed of a knowledge base based on a suite of ontologies named POLARISCO (POLARISC Ontology)  [6]. These are extended by interactions with domain experts and by a set of integrated services such as the messaging service  [7] , victim evacuation service  [8] , and geospatial resources database. The remainder of this paper is organized as follows. We start with a description of the operational level of disaster response and of the different ER requirements, which illustrates the motivation of this paper. We also structure our proposed solution according to the Framework of Enterprise Interoperability (FEI)  [9]. Section 3, provides an overview of existing ontologies proposed in the field of disaster response. Section 4 details the POLARISCO development methodology and outlines the proposed ontology modules.
Rather, each ER community should maintain control over its own data but with a capability to work with other stakeholders through a set of collaborative processes through which they become linked together virtually. Ontologies have been identified as an effective means to implement semantic integration and achieve information interoperability along these lines. They offer the richest representations of machine-interpretable semantics for systems and databases  [16]. They serve as both knowledge representation and as mediation to enable heterogeneous systems interoperability  [17]. Thus, to overcome semantic heterogeneity and to foster a consistent shared understanding of information, the use of ontologies is crucial  [18]. The principal classes of the POLARISCO ontology are data, service, process, and business.
POLARISCO Objectives POLARISCO is a domain ontology built with the principal goal of making the best possible definition of the terms in the technical vocabularies of the various stakeholders. It was developed in order to establish a commonly shared conceptualization that defines classes and their relationships to support semantically interoperable information exchange among the ERs involved in the process of disaster response. It will be used to provide a common understanding of the exchanged data for improving communication and interaction among the involved stakeholders.
Formalization After defining the modules and the related Glossaries in this phase, the proposed taxonomy is transformed into a formal model by establishing relations among classes to ensure a complete taxonomical hierarchy for the POLARISCO as a whole. To connect the different classes, we use a hybrid approach, based on a top-down alignment to BFO and CCO, and a bottom-up alignment to define classes that are gathered during the knowledge acquisition step. That is, we approach in two ways by generalizing high-level classes to lower levels and by abstracting the low-level data to the higher-level class. In virtue of extending BFO and CCO to define POLARISCO modules, we reused generic relations imported from other external ontologies. In particular, CCO reuses the Relations Ontology (RO)  [54]  which is a collection of OWL2 relations intended to be shared among various ontologies.
Another ontology called RO-Bridge has been developed by adding domain and range constraints to the relations defined in RO used to relate BFO classes. The RO-Bridge relations that are reused in POLARISCO are presented in Table  3. Furthermore, we identified the need to define other relations specific to POLARISCO to relate the classes of the different modules (Table  4 ). In this section we present the various classes and relationships of the PCC module. According to the US Department of Homeland Security National Response Framework, a disaster is defined as any event, natural or manmade, that results in extraordinary levels of mass casualties, damage, or disruption severely affecting the population, infrastructure, environment, economy, national morale, and/or government functions  [55]. That is, a disaster is an event characterized by boundaries in time (it has a beginning and an ending).
Messages Module The message module is related to the PCC module and subsequently to the separate stakeholder modules. To define the message module, we reused classes from the PS/EM Communication Ontology  [23] , whose authors employed the EDXL-RM (Emergency Data Exchange Language-Resource Messaging) standards in their work. Figure  15  shows a partial view of the proposed module. The reused classes are marked with the prefix EDXL. We defined an edxl: message as a cco: information bearing artifact. We defined three different types of message that could be exchanged among ERs: informative message (which includes information message, alert message, report message, and so on); request message and response message. In addition, a message is characterized by a state (treated, untreated or ongoing), a confidentiality (public, private or limited), and a degree of criticality of the information to be exchanged (extreme, moderate or secondary).
Implementation The proposed formalization models are encoded in the ontology implementation language OWL and implemented using Protégé. To implement the proposed ontology, we, first, imported BFO and CCO to build the PCC and message modules by using the "owl: import" feature of OWL2. Second, we imported PCC to construct the stakeholder modules. The different modules were then merged together and integrated into one POLARISCO ontology. Table  5  presents the classes and relations of this global ontology.
Evaluation The process of ontology evaluation is designed to ensure that an ontology is built correctly. In other words, it answers the question "are we producing the ontology in the right way?"  [56]. To do so, the consistency of POLARISCO modules was checked using the OWL 2 HermiT reasoner. The aim is to ensure that the constructed ontology is consistent, that its classes are satisfiable, and thus that the inferred model can in principle reflect the intended semantics desired by the ontologist. To check if the ontology responds to our specifications, we translate the CQs into SPARQL (Simple Protocol and RDF Query Language) in order to query the ontology. Figures  16  and 17  present the results obtained. CQ1: "What types of means are needed to respond to a forest fire?" CQ2: "Who is competent to search and rescue the drowned Person?".
Ontology Querying All the presented data about the November 13, 2015 terrorist attacks in Paris were translated into ontology instances to allow query POLARISCO. To this end, we used the SPARQL Query editor that is integrated into Protégé. Examples of queries made include: 1. When and where did the terrorist attacks occur? 2. Who were the command members of each involved unit? 3. What were the used means of transport used by firefighters and healthcare units in the Paris terrorist attacks? 4. How many vehicles and operational firefighters were engaged in the Paris terrorist attacks? Query 1: When and where occurred the terrorist attacks? As shown in Figure  20 , the temporal and the spatial region of the multi-site terrorist attacks are identified. Concretely, we extracted the date of the attacks, the structures where the attacks took place (theatre, stadium, bars, restaurants), and their geopolitical location (city or town).
The semantic transformation step is based on the semantic relationships among the stakeholder modules defined in POLARISCO. More accurately, we propose an algorithm that analyzes each class and interprets it using the equivalent class relation, its properties or its subclasses. The messaging service and its algorithms are beyond the scope of this paper, but Figure  25  provides an example of information exchange between firefighters and healthcare units. Once the firefighters' unit of the VSAV (Vehicle of Succor and Assistane to Victims) is in the field, they figure out that backup they need from healthcare units to handle a large number of victims in a critical situation. We start with an example of the resource-request type message sent by the firefighters in order to demonstrate how a communication act can be improved across ERs using PROMES.
In the formalization step, the conceptual model was transformed into a formal model by defining the relationships, axioms and equivalent classes. In the implementation step, the different modules were implemented in OWL and integrated to yield POLARISCO as end result. Concerning the evaluation and validation step, POLARISCO was queried to check if it responds to the defined CQs. The Paris terrorist attacks use case was a good example to show the utility of the proposed ontology since it highlighted the communication challenges among ERs during the multi-site response. Finally, POLARISCO is published, and it will be updated in reflection of changes that occur the disaster response domain to ensure its continued reliability. The resulted ontology respects all the stated requirements. First, it shares a common upper layer of well-defined classes by reusing BFO and CCO.
Acknowledgments: This research was conducted as part of the  POLARISC (Plateforme OpérationnelLe d'Actualisation du Renseignement Interservices pour la Sécurité Civile ) project. That was funded by the regional operational program FEDER/FSE «Midi-Pyrénées et Garonne 2014-2020» as part of the call for projects "Easynov2016".
At the same time, data collected by the gliders were incorporated in near-real-time into a data assimilative ocean circulation model to test whether the model forecast could be improved for the LoVe region, where geomorphological challenges are brought about by a complex coastline, the presence of fjords, and strong seasonal freshwater input. A data management e-platform was developed to ensure data storage and visualization for researchers and managers. In this paper, we present the project concept and review some of the initial results achieved after a six-month deployment along the LoVe shelf-slope. These findings, initially presented at the OCEAN 2019 conference (Marseille, 17-20 June)  [21]  are expanded here and demonstrate the cost-efficient, long-term, low invasive, and autonomous monitoring and research capabilities of gliders in collecting high-quality environmental and biological data simultaneously.
GLIDER Deployment and Data Collection The three vehicles were deployed along LoVe from March to September 2018, a period of high biological activity (see below). The vehicles were piloted by Maritime Robotics (Wave Glider), Offshore Sensing (Sailbuoy), and Cyprus SubSea Consulting and Services (Seaglider ® ). Initial deployment of the vehicles occurred from the R/V Håkon Mosby, offshore Bodø, Norway. The vehicles were retrieved twice during the survey period to repair a technical problem with the memory storage, and at the same time, offload the acoustic data. A well-known challenge with autonomous acoustic surveys is the identification of the acoustic targets based only their acoustic signal. To identify targets, additional sampling might be performed by trawl, net, or occasionally optical sampling  [15, 37, 38].
Sea Mammal Vocalizations and Anthropogenic Noise To passively scan for marine mammal vocalizations and anthropogenic sound, two of the three automated vehicles were used. The Seaglider ® was equipped with an Observer/AMAR G4 from JASCO  [11, 50, 51]  and recorded continuously down to 100 m depth. The Wave Glider was equipped with a hydrophone SoundTrap from OceanInstruments. To date, only the Seaglider ® data were analyzed. To determine the different types of biological and anthropogenic sounds that occur in the LoVe area, the recordings were inspected visually and aurally using automatic detection and visualization software (i. e. , PAMguard  [52]  and Raven  [53] ). Ship noise consists of broadband noise depending on the size and speed of the vessels, but most of the energy is at low frequencies (<1 kHz).
Typically, the sound of a ship passing by can last for tens of minutes. Automatic detectors and manual spectrogram analysis identified biological sounds as well as weather events, vessel passages, and seismic activities. Ambient noise was defined as all sound recorded, except sound associated with a specified signal and the self-noise produced by the vehicle (e. g. , pitch/roll maneuvers, apogee pump activity, etc. ). Prior to data analysis, all self-noise was detected and removed from the dataset using template matching  [11].
4km [57]. This allowed for the introduction of a large number of rivers (more than 7000 large and small rivers) and positioned them correctly on the coastline. Here, we adopted a 3D version of the Finite-Volume Community Ocean Model (FVCOM  [58] ) and imposed a variable spatial resolution from 30 m along the coast and straits to 2. 4 km at the boundaries. Hence, by imposing high-quality boundary conditions, an enhanced picture of the ambient hydrodynamical processes was achieved. In order to assess the skill of NorShelf-2. 4km compared with MET Norway's high-resolution NorKyst-800m forecast model, which does not employ data assimilation, we compared T-S diagrams.
GLIDER Data Management Platform To manage the data collected by the gliders, a data management platform "GLIDER" was developed by the Norwegian Research Centre (NORCE), Kongsberg Digital (KDI), and the Norwegian Institute for Water Research (NIVA). The platform was designed to perform quality control through a range of tests, ensuring high-quality data access to researchers and end-users. The data management platform provides continuous tracking of autonomous measurement platforms during surveys with real-time access to time series of measured data (e. g. , temperature, salinity, conductivity, and fluorescence). The gliders send time-series data via satellite links (Figure  3 ). The solution for storing the time series is based on the Kognifai digital ecosystem provided by KDI. The solution consists of an asset database in addition to the time-series database.
The metadata for the gliders, instruments, and sensors are stored in the asset database  [59]. The GLIDER web portal implements interactive visual analysis and covisualization of large multidimensional datasets for exploration of the collected data, giving scientists access to the data stored in these databases. The portal was implemented by adapting Enlighten-web, a web application developed by NORCE, and supports interactive visual analysis and covisualization of large multidimensional datasets. Predefined plots were prepared to follow the data acquisition in real-time and to see a quick overview of the data. Users can easily create and store their own plots for further analysis. Brushing and linking  [60]  functionality is provided for exploring complex datasets, to discover correlations and interesting properties hidden in the data (Figure  4 ).
Enlighten-web offers interactive performance in brushing and linking on large datasets by utilizing the graphic processing unit on the user's computer. Several filters can then be combined using Boolean "AND" operators. searchers and end-users. The data management platform provides continuous tracking of autonomous measurement platforms during surveys with real-time access to time series of measured data (e. g. , temperature, salinity, conductivity, and fluorescence). The gliders send time-series data via satellite links (Figure  3 ). The solution for storing the time series is based on the Kognifai digital ecosystem provided by KDI. The solution consists of an asset database in addition to the time-series database. The metadata for the gliders, instruments, and sensors are stored in the asset database  [59].
The GLIDER web portal implements interactive visual analysis and covisualization of large multidimensional datasets for exploration of the collected data, giving scientists access to the data stored in these databases. The portal was implemented by adapting Enlighten-web, a web application developed by NORCE, and supports interactive visual analysis and covisualization of large multidimensional datasets. Predefined plots were prepared to follow the data acquisition in real-time and to see a quick overview of the data. Users can easily create and store their own plots for further analysis. Brushing and linking  [60]  functionality is provided for exploring complex datasets, to discover correlations and interesting properties hidden in the data (Figure  4 ). Enlighten-web offers interactive performance in brushing and linking on large datasets by utilizing the graphic processing unit on the user's computer.