dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Simulation-to-reality & meta-learning approaches The main idea behind meta-learning and SimToReal approaches is to find a policy that is robust to a distribution of tasks (or environments). SimToReal approaches exploit parameterized simulators in order to learn a policy that can effectively transfer on the real system. SimToReal algorithms can be categorized into ones that find policies that are robust: (1) to visual differences  [140] -  [143]  (domain randomization), and (2) to different dynamics properties  [144] -  [146]  (dynamics randomization). James et al. [141]  use a rather simple controller, sample different goal targets and visual conditions (e. g. , lighting, textures) and collect 1 million state-action trajectories of completing different goals. Once this dataset is collected, a convolutional NN, that will later serve as the policy, is trained in a supervised manner to find a mapping between image observations and the appropriate actions to take.
IX. ACKNOWLEDGEMENTS This project received funding from: the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (GA no. 637972 , project " ResiBots "); the  Helmholtz Association  through the project " Reduced Complexity Models "; the  European Commission  through the projects  H2020   AnDy  (GA no. 731540 ) and  MEMMO  (GA no. 780684 ); the  CHIST-ERA  project " HEAP "; the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  739578  complemented by the  Government of the Republic of Cyprus  through the  Directorate General for European Programmes, Coordination and Development.
Here, we present an enhanced software and platform for the TEB model to help scientists and practitioners wishing to use the TEB model in their research as a standalone software application or as a library in their own software. This includes several features such as crossplatform support for Windows, Linux, and macOS using CMake (Kitware Inc. , 2020), static and dynamic library generation for integration with other software/models, namelist-based configuration, integration with MinimalDX  (Meyer & Raustad, 2019)  and PsychroLib  (Meyer & Thevenard, 2019)  to improve the modelling of air conditioners (AC) and psychrometric calculations respectively, a thin interface used in the coupling with WRF-CMake  (Riechert & Meyer, 2019) , helper functions for Python for pre-and post-processing inputs and outputs files, and a tutorial in Jupyter Notebook to allow users to quickly become familiar with the general TEB modeling workflow.
In the new platform we implement testing at every code commit through continuous integration (CI) and automate the generation of documentation. The project is developed as a free, open source, community-driven project on GitHub (https: //github. com/teb-model/teb) to support existing and new model applications with enhanced functionality. We welcome contributions and encourage users to provide feedback, bug reports and feature requests, via GitHub's issue system at https://github. com/teb-model/teb/issues. (Masson, 2000). The number of features available in TEB has increased since its first version published in 2000. (*) Capability not currently available in the TEB software.
Acknowledgments We acknowledge the researchers who contributed to the scientific development of the TEB code: from CNRM:  Aude Lemonsu ,  Grégoire Pigeon ,  Cécile de Munck ,  Bruno Bueno ,  Marine Goret ,  Emilie Redon ; from  IFSTTAR Katia Chancibaul, Xenia Stavropulos-Laffaille ; and from  Environnement and Changement Climatique Canada (ECCC) :  Sylvie Leroyer.
5, and assumed a nonsphericity correction ε = 1. Results of the power calculations indicated we had more than 95% power with α < 0. 05. Fifty participants were recruited from the College of Education Research Participant Pool at Auburn University (USA) and ten were recruited by word-of-mouth. Participants recruited from the Research Participant Pool were offered course credit for their participation. To be included in the study, participants had to be willing to participate in a laboratory session. Participants were excluded if they: had a physical impairment making physical activity difficult; had a neurological impairment; or were color blind. A total of sixty students were recruited, but one participant's data were excluded due to an experimenter error during data collection. Participants gave written consent prior to participation.
Statistical analysis Commission errors and reaction times on all correct "go" trials from the corresponding task were analyzed using mixed effect models (MEM). The MEM approach decreases the risk of type-I error and permits a correct estimation of parameters with multiple cross-random effects, like the present study where participants are crossed with stimuli  (Boisgontier & Cheval, 2016). Here, we built MEM using the LmerTest package of the R software and specified both participants and stimuli as random factors  (Bates, Mächler, Bolker, & Walker, 2014; Kuznetsova, Brockhoff, & Christensen, 2015; R Core Team, 2017). To reduce Fig. 1. Go/no-go tasks. The experiment consisted of three go/no go tasks of 208 trials (go trials, 75% occurrence; no-go trials, 25% occurrence). In one task, participants were instructed to respond to physical activity images and to not respond to physical inactivity images (this task is depicted in the figure).
In a second task, participants were instructed to respond to physical inactivity images and to not respond to physical activity images. In a third task, the stimuli depicting physical activity and physical inactivity were replaced by stimuli including an animal versus not including an animal (control task). Participants were either asked to respond to images depicting an animal and to not respond to images not depicting an animal, or to do the reverse. The order of tasks was randomized for each participant. The random inter-stimulus interval (ITI) varied between 1200 and 1400 ms. Stimuli were presented for 500 ms. convergence issues, each model was first optimized using the default BOBYQA optimizer  (Powell, 2009) , the Nelder-Mead optimizer  (Nelder & Mead, 1965) , the nlimb optimizer from the optimx package  (Nash & Varadhan, 2011) , and then the L-BFGS-B optimizer (see  Frossard & Renaud, 2019 , for similar procedure).
g. , availability of travel funds) early in the process. What are the institutional policies regarding remote positions? Does the remote position require cross-border consideration (i. e. , visas and immigration, which need to be discussed with the hosting institution early)? How often will the postdoc visit? Who pays the costs of visits to campus? Are there temporary housing options available for the postdoc? Asking and answering these questions early will ensure that any legal or institutional issues are addressed and that plans are made to ensure that the postdoc will be effective. If a PI and a potential postdoc are writing a proposal together (e. g. , grant or fellowship), openly discuss the costs and benefits of remote postdoc work and budget in travel and housing costs if possible. Just as with an on-site postdoc, it's important to establish expectations for mentoring styles and communication (See Rule 3).
Team-focused collaboration software (we all use Slack) is useful for managing intralab communications in a way that creates a sense of community and facilitates remote equivalents to the kinds of interactions that happen between local lab members. These platforms allow organizing discussions by topic, which allows easy partitioning of questions and discussion by topic and makes it easy for team members to read and engage in discussions on their schedules, which is useful when working in different time zones. For project management and long-term planning, we recommend using shared project management space or shared documents (e. g. , project boards on GitHub or checklists on Google Drive) so PIs and postdocs can make sure they agree about priorities, tasks, and progress. It is also important to revisit your communication plan at set intervals (e.
Rule 4: Invest in and use video conferencing As remote postdocs and PIs work to create a solid, functional, and dynamic communication plan (Rule 3), investing in the hardware, software, and practice of video conferencing will both support that communication and open the whole lab to dynamic, inclusive meetings (Rule 5). Leveraging modern technology to conduct regular real-time meetings will allow remote postdocs to build relationships with their lab, facilitate brainstorming across the whole lab community, and obtain the advising and support they need to be professionally successful. Text-based communication (e. g. , emails and collaboration platforms such as Slack) has many advantages, but it can be problematic in many contexts because it is easy to misconstrue tone due to the lack of visual and auditory cues.
Rule 5: Normalize remote interactions and cultivate digital spaces for the entire lab Digital collaboration on writing, coding, and discussion is increasingly central to productive research. While digital collaboration tools are essential to remote collaboration, they are also useful for local collaboration. Even when collaborators work within the same institution, they may have different schedules, obligations, or frequent travel requirements that can prevent them from coming into the lab. Digital spaces allow lab members to interact seamlessly regardless of remote or local status. For example, this manuscript was written entirely through a remote collaboration using email, Slack, and Google Drive. It is vital to normalize remote interactions and collaboration tools throughout the lab, not just for remote postdocs.
Integrating digital spaces into the everyday lab practices reduces the differences between local and remote members and lowers the barriers for interaction. Electronic lab or research notebooks, wikis (we have used GitHub wikis and Open Science Framework), project management software (we have used GitHub and Trello), online office software (we have used Google Docs, Sheets, and Slides), and cloud storage (we have used Google Drive, Zenodo, and figshare) improve institutional memory, create an archive of activities, help with project management, and ensure long-term, secure data storage, therefore providing many advantages in addition to supporting remote work. Remote participation can be useful for local individuals as well, allowing for work to continue when traveling or working from home (see Fig  2  for a real-world example), some examples include: the use of institutional virtual private networks (VPNs), remote access to computing clusters, and interlibrary-loan scanning services.
This work was partially supported by the  Gordon and Betty Moore Foundation's DataDriven Discovery Initiative  through grant  GBMF4563  to EPW (https://www. moore. org/programs/science). KRB was supported by  National Science Foundation NRT-IGE  grant # 1545458  (awarded to M. Rubega) (https://www. nsf. gov/funding/pgm_ summ. jsp?pims_id=505473). CMM, SBB, KEI, and AKT are supported by the  David H. Smith Conservation Research Fellowship , funded by the  Cedar Tree Foundation  and administered by the  Society for Conservation Biology  (http:// cedartreefound. org/smith-fellows-program). AKT is also supported by a  Mitacs Accelerate Fellowship  ( IT12751 ) co-sponsored by the  Pacific Salmon Foundation and Genome British Columbia  (https://.
METHOD We started curating a list of relevant papers, in November 2017, by doing a search query using the terms "engagement+humanrobot interaction" in Scopus and Google Scholar, two popular citation databases. Thereafter, to consolidate a full list of any newly published articles, the search was repeated every 6 months up until December 2019. The curated list of papers went through the following inclusion criteria: • Is the paper covering the topic of engagement in human-agent interaction, including detecting it and generating behavior to manifest engagement? This criterion entails that papers can include robotic agents or virtual agents. • Is the paper over four pages in length? With this criterion, the inclusion of abstracts or poster publications is eliminated. • Is the content of the paper not overlapping significantly with another paper from the same author(s)? If the paper does overlap, then the most elaborated paper is selected for inclusion.
Evaluation Methodology To understand how evaluations in the curated list of papers investigated the users' perception in their work, we extracted the type of evaluation data collected in each of the papers. After an initial review of all the papers, it was apparent that 23 papers did not include an evaluation study and were found to be focused on technical, modeling or conceptual contributions. For the remaining papers, we found thirteen types of evaluation tools, both objective and subjective, to measure the users' perception in the presented works. The data types reported include questionnaires, RGB video recordings, depth camera recordings, time/temporal performances, post study interviews, observations, physiological sensor data, tracking sensor technologies (motion, eye, and laser tracking), speech and dialogue recording, and contextual and application records (such FIGURE 5 | Overview of all publications in this survey according to application type when it was specified (140 in total specified, 29 unspecified, or could not be identified).
Lab or Real World If we want to go toward more long-term interactions and away from very context dependent interactions, then it is important to gather data and build models of how interactions happen in real-world situations. Questions then arise including how to capture changes of engagement over the course of an interaction, how group size effects engagement and what implications this has on model building. To capture more of these conversational dynamics,  Oertel et al. (2013)  recorded five participants over the course of two days. All interactions were recorded with audio and video and also motion captured. Instead of providing participants with a task or guiding the interactions, they were simply asked to talk to one another. While this data led to several research papers on investigating engagement, it was also limited in that still several multi-modal cues needed to be annotated manually.
Leite et al. (2014)  presented an empathic model that is designed to allow for a long-term interaction. In their work, they present a study with 16 elementary school children (8-9 years) interacting with a robot in a chess game scenario. The aim of their study was to investigate children's perception of social presence, engagement, and support toward the robot over time. Their study extended over five weeks in which each participant had one session per week (average of 20 min per session). The authors collected data from open ended interviews, questionnaires, video recordings, affect sensors, and game play logs. Their analysis revealed that in long term child-robot interactions, a robot endowed with an empathetic model was perceived to support children in a similar manner to the support received from their peers.
Furthermore, children found the emotion-based support to be their preferred supportive behavior, echoing the results presented by  Ahmad et al. (2017). Díaz et al. (2011)  aimed to build a robot capable of maintaining the engagement of children. The authors presented a study to investigate the social bonds associated with childrobot interaction in order to design for long-term interaction for hospitalized children. In their study, they had two phases, the first was a field study using four robots at a school with 49 children (11-12 years). The second phase had 7 children from the 49 to contribute in a lab study with one of the robots 2 months later. Data collected from both phases include video, questionnaires, observations, interviews, and interaction choices. Their analysis reveals that appearance and performance aspects are important design considerations for long term interaction, in particular, they shape the expectations of children toward interactive behaviors and social processes.
Review of remote health initiatives 1. China: China, the first country to encounter the disease, was also the first to develop innovative remote monitoring methods to assist clinical staff and public health experts. Perhaps the most obvious remote monitoring technology most citizens possess, is the smartphone, allowing everything from telemedicine consults and symptom checking from apps and websites to approximate heart rate monitoring through the camera, and location tracking to identify disease hotspots or contact-tracing. The Chinese government teamed up with Tencent and Alibaba to develop color-coded health rating systems, which are added to the existing apps of WeChat developed by Tencent (Shenzhen, China) and AliPay developed by Alibaba Group Holding Limited (Hangzhou, China). Citizens in hundreds of cities have been asked to download the software that transmits their location to several authorities, including the local police.
Both phones exchange anonymized temporary IDs (generated by encrypting the User ID with a private key that is held by MOH) together with information about the nearby phone's model, Bluetooth signal strength, and time. The temporary ID can only be decrypted by MOH, with MOH's privately held key. The temporary ID is renewed frequently, making it difficult for anyone (other than the MOH) to identify or link the temporary IDs to any given user. All this information is stored locally on the user's phone, and not sent to MOH. If MOH needs to share a user's proximity data for contact tracing, they seek the user's consent by directly calling the user and providing the user with a code to match with a corresponding verification code on the user's TraceTogether app. The app does not track an individual's location or contacts and data is stored locally on the phone for only 21 days and is not accessed unless the user is identified as having been close enough to another user of the app that was identified as COVID-19 positive at some (unspecified) window around the time of proximity.
The platform can either be dedicated software (e. g. Clinics 23  and Curon 24  ) or common communication platforms. It is up to healthcare providers and patients to decide if they utilize these services. It is required that all services used need to be secure and privacy-protecting. The adoption of mobile clinic had been slow thanks to several restrictions in the 2018 guideline: (1) only limited numbers of diseases were covered (e. g. , hypertension and diabetes); (2) "6 months rule" whereby patients must visit a doctor regularly for 6 months before they transition into telemedicine, and (3) "30 minutes rule" whereby an online healthcare provider must be located within 30 minutes from their patients in case of emergency. Although these rules were intended to promote informed decision making and prevent misdiagnosis, under treatment, misconduct, and security breaches, they hampered the rapid spread of mobile clinics.
The Weizmann Institute of Science in Rehovot has developed a method for monitoring, identifying and predicting the zones of COVID-19 spread  (Rossman et al 2020). To achieve that, volunteers are asked to complete a questionnaire relating to virus-induced symptoms, once a day, with the aim of identifying clusters of infection. This initiative has had tens of thousands of volunteers to date and is expected to support policymaking 25 Behar J, Rosenberg A, Weiser-Bitoun I, Shemla O, Alexandra A, Konyukhov E and Yaniv Y 2020 Heart Rate Variability analysis of human and animal electrophysiological data Online: https://physiozoo. com/ Retrieved 08 July 2020 intending at slowing down the spread of the virus. Perhaps most notably, on 22 March 2020, the Israeli Ministry of Health unveiled a new location tracing app, called "The Shield" ("HaMagen") 26.
The app takes GPS location data from the user's phone and compares it with the information available within the Health Ministry servers regarding the history of the location of confirmed cases in the 14 days prior to their diagnosis. The extracted information informs users if they have crossed paths with someone who has been infected with COVID-19. The user is then given the option of reporting their exposure to the Health Ministry by filling out a form. The Health Ministry has provided public assurances that involvement is strictly voluntary and secured. The crossreferencing of the user GPS history with the ones of COVID-19 positive patients happens on the user phone and is not sent to any third party. The source code of the application has been made available to the public for transparency.
Originally aimed at identifying sepsis, the wearable device is comprised of a chest patch that measures respiratory rate, heart rate and temperature every two minutes; the collected data is then analyzed by an automated algorithm, which alerts the medical team in cases of deterioration. NHSX, the technology and research arm of the NHS, developed a proximity tracing phone app with researchers from the University of Oxford and developers from tech companies like VMWare 30. Like many other solutions, the app uses the phone's Bluetooth Low Energy network to detect and inform users about potential contact with other infected users, without identifying or disclosing personal information to other individuals. The concept is similar to that of the contact tracing app called TraceTogether, rolled out in Singapore.
All medical data are protected and framed according to the General Data Protection Regulation (GDPR) of the European Union. Other specialized FrenchTech startups provide data protection solutions so that no French doctor needs to use non-secure tools like WhatsApp, FaceTime, Skype, or Zoom. The confinement significantly increased the usage of telemedicine, with 1 million teleconsultations recorded by Caisse Nationale de l'Assurance Maladie (CNAM) or the week of 30 March 2020 alone. During a press release from the national health insurance on the 31 March 2020, teleconsultations were now constituting more than 11% of all consultations, compared to less than 1% before the crisis. On 7 April 2020, the CNAM listed 30,000 doctors had adopted teleconsultation regularly. More than 44% of general practitioners having experimented using teleconsultation.
On 15 June 2020, the app Immuni 35  created by the startup Bendin Spoons S. p. A. but managed by the Government for contact tracing was made available and active throughout Italy. Privacy issues have been taken into account (as little personal data as possible is recorded) and it is based on Bluetooth Low Energy and not on GPS location tracing. The app was made available on 15 June 2020, and after two weeks, only 4 million Italian citizens had downloaded the app, not enough yet for reliable contact tracing. After lockdown temperature checks will be performed before entering public places. The post-COVID-19 era will likely see the healthcare system slip back to where they were before, except, for some remote services that may hopefully remain. 10. Germany: While Germany has pursued remote health care projects for decades  (Stroetmann and Erkert 1999) , current practices barely integrate them  (Brauns and Loos 2015).