dataesr/scientific-paragraphs-categorization huggingface dataset:
This layer deals with the management of connected objects and the sensor network. It includes Sensors Controllers, Smart Lamp Controllers and Voice Command Controllers. This layer allows bidirectional communication between the connected objects and the middleware. The controller layer is implemented logically in our architecture by the access protocol management module. Interoperability, subscription, notification, heterogeneity, and command execution are operations performed in the middleware layer. This layer offers mostly an abstraction to handle connected objects. It is just composed of few components: OpenHab and MQTT servers. It is deployed in a server running on a Raspberry Pi. Clients connect to an application server for data access. To ensure security, the Rest API is the only API able to connect to the Rapsberry Pi for data exploitation.
Design of Mobile Applications. Regarding the design process, we assessed expectations of older drivers relative to mobile application interfaces with interviews. In these, we asked participants about pictogram and color for three types of interfaces. We implemented the most preferred interfaces in a mobile application (app. ) named "AR-Driving Assistant". The mobile app has been divided into two sub-app. The first one presents information to the driver. This sub-app was built with the Unity software and was configured to run on an Android phone. It projects the information to the driver through the HUD interface. The objective was to provide driving notifications with pictograms and colors. These choices are important when designing technology interfaces  [19]. Figure  2  presents the three selected interfaces according to expectations collected.
The second sub-app allows control of notifications sent to the driver and collects data. The objective was to control the first sub-application by collecting data during driving for later analysis (i. e. speed, breaking, position, etc. ). The application consists of a scrolling view containing several buttons including a dialog box to configure and start sensor monitoring (Fig. 3 ). This application was developed with the Android Studio software and the application code is available on GitHub (https://github. com/limvi-licef/AR-driving-assistant).
Experiments We evaluate the user-embedding model using a private dataset collected from a survey of the user app usage. We collected the app usage data of the users over a period of 2 months. The User set is composed of both the male and female users within the age group from 16 to 80. App usage data consists of the app ids (For example -com. whatsapp) of the apps used by the users within this period in the csv format. This dataset is used to evaluate the user embedding using various tasks. The app usage data is preprocessed using Spark to create the one hot encodings of the user app usage vector.
In addition to that, established research institutes and organisations are added to the consortium to develop the concept further and further. Due to literature research and experience, possible partners were detected. Afterwards, the team decided to start the working phase by founding a matrix based organisation, composed of several working groups (WG) (see Fig. 1 ). The figure shows WGs with domain specific focus and groups with cross-sectoral topics. The WG coordinators need to collect relevant data of their WG and interconnect with other WGs. The created ForeSight platform concept should be evaluated and verified in laboratories and real world environments. Therefore, possible user stories and use cases need to be identified and implemented. There are several user story catalogues but usually there is no direct combination with AI, which is one of the core elements of this platform approach.
Discussion When designing a platform, one of the first questions which arises is who wants to operate such a platform. The ForeSight consortium consists of several companies, which are experienced in running platforms. A flexible service-based approach for running the platform seems to outperform a monolithic approach, as first interviews showed. Numerous use cases from the ML area require high quality data in order to train the ML models, e. g. neural networks. Such data is often not available and must be generated, which usually takes an enormous amount of time. It will be hard to consider such soft facts in the AI method finder function to answer the service-based apps. In the past, several critical factors for smart home platforms were identified, i. e. IT security, privacy and economic beneficial business models for all stakeholders.
use a transfer learning technique to train our network model with sleep posture data for 56 subjects (source dataset) and use it for sleep stage classification (target data). The sleep data was obtained from 5 subjects and it was collected in the Boone Hospital Center (BHC) in Columbia, MO, USA under the University of Missouri IRB approval, project number 2008526. The main contribution of this work is developing a new deep model architecture that utilizes CNNs and LSTMs to classify sleep stage data. The CNNs are trained to learn filters that extract time-invariant features from the BCG signals while the LSTMs are trained to encode temporal information such as sleep stage transition rules.
The confusion matrix Ventilation and the Indoor Environment. Water and Air Quality Bureau Healthy Environments and Consumer Safety Branch, March 2018. http://publications. gc. ca/ collections/collection_2018/sc-hc/H144-54-1-2018-eng. pdf 20. Mshali, H. , Lemlouma, T. , Magoni, D. : Context-aware adaptive framework for e-health monitoring. In: IEEE International Conference on Data Science and Data Intensive Systems, Sydney, Australia, pp. 276-283, December 2015 21. Outdoor air quality dataset. https://catalogue. data. gov. bc. ca/dataset/air-quality-monitoringverified-hourly-data 22. GAMS Indoor Air Quality Dataset. https://github. com/twairball/gams-dataset Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons.
Acknowledgment. This work was supported by the European project  City4Age  that received funding from the  Horizon 2020 research and innovation program  under grant agreement number  689731. We would like to thank all the participants who accepted to be part of this experimentation and all our collaborating geriatricians and professional and informal caregivers. Acknowledgement. The authors would like to thank the professors  Jacques Bergeron ,  University of Montreal , and  Charles Gouin Vallerand, TELUQ University , to share the data used in this study. Acknowledgments. We would like to show our gratitude to all staff of  Boone Hospital Center Sleep Center at Columbia, Missouri , with a special thanks to  Mr. Custer  for his patient and helpful advice.
Acknowledgements. This work is supported by the German Academic Exchange Service (DAAD) (Transformation Partnership: Theralytics Project). Acknowledgment. This work is supported by the  German Academic Exchange Service (DAAD)  (Transformation Partnership:  Theralytics  Project). Acknowledgments. The research was supported by a grant of the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (grant number:  HI18C0552 ). Acknowledgments. We would like to acknowledge  St. Joseph 's Health Care, the  Canadian Mental Health Association and London Middlesex Community Housing  for facilitating the research environment. We would like to thank the participants and HCPs for their voluntary participation.
Also, we appreciate the commitment of the research assistants for data collection and auditing to make sure the quality of research is assured. Finally, we would also like to acknowledge the two granting agencies -Canadian Mortgage & Housing Corporation, and  Canadian Institutes for Health Research  -for making the funding available. Acknowledgement. The work is part of the project  PULSE ,  H2020 -727816 , funded by the  European Union. Acknowledgments. The authors want to thank all the partners and participants involved in the project. A special thanks to the Côte Saint-Luc city administrators. Acknowledgments. This work was supported by  MITACS. We thank all the participants from the user study and staff who wrote and provided helpful comments.
Summary Recent experiments measuring the temperature and polarization of the Cosmic Microwave Background, like WMAP  (Bennett et al. , 2013)  and Planck  (Planck Collaboration, 2018) , produce all-sky maps at higher and higher resolution. Handling those datasets efficiently and studying their statistical properties requires a discretization scheme on the sphere. The Hierarchical Equal Area isoLatitude Pixelization, HEALPix  (Górski et al. , 2005) , scheme (see Fig. 1 ) has proven to be an excellent mathematical framework to store mapdomain data and efficiently compute their Spherical Harmonics Transform, whose Angular Power Spectrum is one the most powerful tools to understand the early Universe. The HEALPix team provides FORTRAN, C++, IDL/GDL and Java implementations of the framework with simulation, analysis and visualization functionalities, format definitions for FITS files, and extensive documentation of each routine and facility.
healpy is a wrapper to the multi-threaded HEALPix C++ library in Python, it implements a user-friendly interface for all HEALPix functionalities, most importantly a fast nearestneighbor search and the decomposition into Spherical Harmonics coefficients. It also adds utilities to read/write maps, Spherical Harmonics coefficients, and Power Spectrum values as FITS files based on astropy. io. fits. Finally it provides extensive plotting functionality, i. e. Mollweide, Gnomonic and Cartographic projections based on Matplotlib. We also release a conda package on conda-forge and wheels on PyPI for Linux and MacOS. These packages also bundle the HEALPix C++ library. Moreover, healpy is distributed as part of the HEALPix release package; within the installation script, a single C++ dynamic library is installed in the target system and used both by the C++ binaries and by healpy.
Here we utilized data generated as part of the European Medical Information Framework for AD Multimodal Biomarker Discovery (EMIF-AD) previously reported in full in Kim et al. [7]. As discussed in that paper, metabolite levels were measured using liquid chromatography-mass spectroscopy (LC-MS) to cover ca. Eight hundred metabolites and these metabolites related to CSF biomarkers of AD commonly used in clinical research including trials, and increasingly in clinical practice, as part of the diagnostic work up. Here we explore the potential of different Machine Learning (ML) algorithms to identify those individuals with AD from dataset and to compare the effectiveness of bloodbased metabolites as an indicator of clinical diagnosis to that of CSF markers. In this study we employed two state-of-the-art ML algorithms-Deep Learning (DL) and Extreme Gradient Boosting (XGBoost)-and compared these to the more commonly utilized Random Forest (RF) algorithm.
Further studies will assess the performance of ratios/combinations of CSF markers and metabolites, life-style factors and disorders commonly found in the elderly, together with testing the specificity for this specific panel in other neurodegenerative (e. g. , PD, FTD), neurological (e. g. , stroke) and psychiatric (e. g. , depression) disorders associated with aging. The intent of this paper was to compare the performance of different ML algorithms to identify people with AD from cognitively unimpaired individuals. Here we show first that all three approaches used demonstrate good discriminatory power, second that XGBoost is somewhat more effective in this particular dataset than RF and DL and third, that this accuracy for clinical diagnosis is broadly similar to that achieved by CSF markers of AD pathology.
The lack of a replication and validation dataset limits the interpretation of this finding, but nonetheless, the strong prediction of diagnostic category from a blood-based metabolite biomarker set is further evidence of the potential of such approaches to complement other biomarkers in identification of people with likely AD. composed of financial 2 Interpretation: The results in here show that with state-of-the-art ML algorithms, blood metabolites have the potential to match the CSF markers of AD pathology on identifying people with AD from cognitively unimpaired individuals. All the ML algorithms employed showed good discriminatory power. 3 Future directions: Results of this study should be replicated and validated using an independent dataset. Further studies will also aim to assess the performance of ratios/combinations of CSF markers and metabolites, life-style factors and disorders commonly found in the elderly, together with testing the specificity for this specific panel in other neurodegenerative (e.
contribution from the European Union's Seventh Framework Program (FP7/2007-2013) and EFPIA companies' in-kind contribution. The DE-SCRIPA study was funded by the European Commission within the fifth framework program (QLRT-2001-2455). The EDAR study was funded by the European Commission within the fifth framework program (contract no. 37670). The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government (allocation 17. 0. 1. 08. 12. 0000. 2. 454. 01. 41142. 001. H). Kristel Sleegers is supported by the Research Fund of the University of Antwerp. Daniel Stamate is supported by the Alzheimer's Research UK (ARUK-PRRF2017-012). Those which looked at blood metabolomics data were small and used clinical diagnosis as an endpoint. Subsequently, we explored the potential of state-of-the art ML algorithms including Deep Learning and Extreme Gradient Boosting to test the performance of blood metabolite levels to clinical diagnosis and compared to CSF biomarkers.
Acknowledgments The authors thank the individuals and families who took part in this research. The authors would also like to thank all people involved in data and sample collection and/or logistics across the different centers, and in particular  Marije Benedictus ,  Wiesje van de Flier ,  Charlotte Teunissen ,  Ellen De Roeck ,  Naomi De Roeck ,  Ellis Niemantsverdriet ,  Charisse Somers ,  Babette Reijs ,  Andrea Izagirre Otaegi, Mirian ,  Ecay Torres ,  Sindre Rolstad ,  Eva Bringman ,  Domile Tautvydaite ,  Barbara Moullet ,  Charlotte Evenepoel ,  Isabelle Cleynen ,  Bea Bosch ,  Daniel Alcolea Rodriguez ,  Moira Marizzoni ,  Alberto Redolfi  and  Paolo Bosco. Funding: The present study was conducted as part of the  EMIF-AD  project, which has received support from the  Innovative Medicines Initiative Joint Undertaking  under EMIF grant agreement no.
A recent WHO consultation was held to gather evidence to support a new NTD roadmap for post-2020 targets and milestones. Transmission dynamics models can help address evidence gaps by assessing, in silico, the feasibility and potential impact of different intervention strategies to achieve proposed 2021-2030 targets. Dixon et al. 29  recently conducted a systematic review and analysis of available TS transmission models, identifying four mathematical/computational/statistical models (among them three transmission dynamics models), and a conceptual 'logical' framework  30. Models included a decisiontree  31  , Reed-Frost 32 , individual-based  33  , and population-based  34  frameworks. (Other models have been published since this review  35, 36. ) On-going development of cystiSim  33  and EPICYST  34  within a collaborative umbrella, has led to the establishment of 'CystiTeam', a partnership between the groups that developed these two models (based at the University of Copenhagen/Scientific Institute of Public Health, Brussels, and Imperial College London, respectively) and groups of epidemiologists, veterinarians, clinicians, one-health experts and program stakeholders.
Acknowledgments Members of the  CystiTeam Group for Epidemiology and Modelling of Taenia solium Taeniasis/Cysticercosis  (CystiTeam: A coalition of Taenia solium taeniasis/cysticercosis field and quantitative epidemiologists, clinicians, veterinarians, one-health experts and program stakeholders recently formed to address collaboratively questions regarding the population biology, transmission dynamics, epidemiology and control of TS through mathematical modelling approaches):.
This work was supported by the  Bill and Melinda Gates Foundation  through funding to the  NTD Modelling Consortium Grant information : [ OPP1184344  to MW and MGB] and past funding to  UCB, MVJ, VS, ASW , and WEH [1017886]. MAD is funded by the  Medical Research Council Doctoral Training Programme (MRC DTP ). MAD, PW and  MGB acknowledge joint Centre  funding from the  UK Medical Research Council and Department for International Development  [ MR/R015600/1 ]. VS and ASW acknowledge joint funding from the  German Federal Ministry of Education and Research (BMBF)  [ CYSTINET-Africa 01KA1618  and  SOLID 01KA1617 ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
FULL PAPER through singular value decomposition and global analysis performed using the software Glotaran. [60] mputational details. All the quantum chemical calculations for TBP in gas phase and solvents have been carried out using Density Functional Theory (DFT) and Time Dependent DFT (TDDFT) as implemented in the Gaussian 09 package. [59] The 6-31+g(d) basis set was adopted for all calculations and a long-range Coulomb attenuated functional CAM-B3LYP  [61]  was used both for geometry optimization and for the calculation of vertical excited states. Calculation were run in gas phase and in dichloromethane (DCM), cyclohexane and toluene, in the polarizable continuum model (PCM)  [62]  approach (non-equilibrium, state-specific) as implemented in Gaussian 09 package. The optimized ground state (S0) geometries have been confirmed to be in local minimum energy structures by analyzing the vibrational frequencies in the ground state.
Acknowledgements The authors gratefully acknowledge financial support from the  Italian Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) : grant  RBFR10Y5VW  ( FIRB Futuro in Ricerca 2010 ) and grant " Dipartimenti di Eccellenza " ( DM 11/05/2017 n. 262 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  812872  (TADFlife). This research benefits from the  HPC (High Performance Computing)  facility of the  University of Parma, Italy. We thank  Cédric Rouxel  and  Cynthia Vandermeulen  for assistance in the synthesis of TBP.
Muscle biopsy Muscular adaptations will be studied on Vastus Lateralis samples taken during a muscular biopsy using the modified Bergström technique. 34 Some of the fresh tissue will be used to study mitochondrial functions on permeabilised muscle fibres by respirometry (Oxygraph-2k G series, Oroboros Instruments, Innsbruck, Austria) and fluorimetry (Oxygraph-2k-Fluo LED2-Module, Oroboros Instruments, Innsbruck, Austria). The rest of the biopsy will be sampled and frozen for further analysis (ie, histology, mRNA expression, biochemistry). There will be no data bank setup and the samples will only be conserved transiently for the duration of the analyses provided for in the study protocol. At the end of the study, if any samples remain, these will be destroyed. The micro-biopsy performed in the Vastus Lateralis muscle requires a 24 hours recovery and will have no impact on the exercise programme.
Management of the study The principal investigator and the trained clinical research team will collect data. Data will be recorded for each patient in a dedicated notebook at each assessment point, and any deviation from the protocol or adverse event will be noted. Study data will also be collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at the University Hospital of Clermont-Ferrand. 35 REDCap is a secure, web-based application designed to support data capture for research studies, providing: ► An intuitive interface for validated data entry. ► Audit trails for tracking data manipulation and export procedures. ► Automated export procedures for seamless data downloads to common statistical packages. ► Procedures for importing data from external sources.
Randomisation procedure A permuted-block randomisation (ie, random block sizes) will be conducted using a computer-generated random allocation (Stata statistical software, V. 13, StataCorp LP, College Station, Texas, United States of America), with a 1:1 ratio allocation, ensuring complete randomness of the assignment of a patient to each randomised group. Stratification will be performed according to sex and pathology (ischaemic vs no ischaemic heart failure). To ensure that there is no bias in their selection and allocation, patients will be randomised after it is clear that they meet the inclusion criteria, that they have provided written consent and that an anonymity number has been assigned to them.
Statistical analysis All analyses will be conducted using Stata software (V. 13). A two-sided p value less than 0. 05 will be considered to indicate statistical significance. The continuous variables will be expressed as mean and SD or as medians and IQR, according to statistical distribution. The assumption of normality will be assessed using the Shapiro-Wilk test. The categorical parameters will be expressed as the number of patients and associated percentages. The patients will be described and compared at inclusion between randomised groups according to the following variables: compliance with the eligibility criteria, epidemiological, clinical and biological characteristics and treatments. Student's t-test or Mann-Whitney U test, if t-test's assumptions are not met (normality studied using Shapiro-Wilk test and homoscedasticity verified by Fisher-Snedecor test), will be applied to compare the primary endpoint between randomised groups.
We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at z ∼ 0 with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete sample of ∼ 12, 000 galaxies in the simulation using the radiative transfer code SKIRT and include PSF and noise to match the SDSS r-band properties. The images are then processed through the exact same neural network used to estimate SDSS morphologies to classify simulated galaxies in four morphological classes (E, S0/a, Sab, Scd). The CNN model finds that ∼ 95% of the simulated galaxies fall in one the four main classes with high confidence. The mass-size relations of the simulated galaxies divided by morphological type also reproduce well the slope and the normalization of observed relations which confirms the realism of optical morphologies in the TNG suite.
The IllustrisTNG simulation The IllustrisTNG Project  (Nelson et al. 2018b; Marinacci et al. 2018; Naiman et al. 2018; Springel et al. 2018; Pillepich et al. 2018a ) is a suite of magneto-hydrodynamic cosmological simulations performed with the moving-mesh code AREPO  (Springel 2010; Pakmor et al. 2011 Pakmor et al. , 2016)). See  Weinberger et al. (2017) ;  Pillepich et al. (2018a)  for a description of the TNG simulation model which is an improved version of the original Illustris simulation  (Vogelsberger et al. 2014a,b; Genel et al. 2014; Sijacki et al. 2015). The Il-lustrisTNG model was especially designed to match some key observables: (i) the global star formation rate density at z = 0 -8, (ii) the galaxy mass function at z = 0, (iii) the stellar-to-halo mass relation at z = 0, (iv) the black hole-tostellar mass relation at z = 0, (v) the halo gas fraction at z = 0, and (vi) galaxy sizes at z = 0.