dataesr/scientific-paragraphs-categorization huggingface dataset:
The patients had to enter meal composition details into the software, which subsequently analysed the meals and proposed corrections taking into account the patients' individual profiles, dietary habits and tastes. A more detailed description of the device has already been published  [21]. The monitoring interface linking TMG participants with investigators was designed as a secure web platform receiving all data from connected objects. TMG subjects sent these data to the platform on a weekly basis and had free access to this information and to their monitoring summary. The frequency of data consultation by investigators was left to their discretion. However, according to the protocol, they were sent an email every month to encourage them to carry out the telemonitoring. A summary of all data recorded over the last 30 days was available for rapid interpretation of results in order to facilitate follow-up and the telemonitoring process.
Statistical Analysis An intention-to-treat effectiveness analysis was carried out. Since the analysis was performed on complete data (no imputation for missing data), the intention-to-treat population comprised all randomised participants having at least one HbA 1c value collected during follow-up (primary endpoint) and having no missing data regarding the confounding factors defined a priori. The proportion of subjects randomised but not analysed was \ 10%. Concerning the primary efficacy endpoint, we compared the two groups using a linear mixed-effects model for repeated measures (MMRM), modelling all HbA 1c measurements performed during the 1-year intervention period. To ensure the comparability of the two groups (TMG versus CG) and to enhance statistical power, the model was adjusted for the confounding factors defined a priori (baseline HbA 1c , age, gender, diabetes complications, obesity, duration of diabetes and insulin treatment).
The search for an interaction between the time of measurement and the intervention group was carried out as well as a centre effect. Additional analyses taking patient compliance (number of connections) into account were also performed. For this purpose, we considered the number of connections to data synthesis and to Nutri-Educ software, respectively. These variables were categorised into two groups based on the median value. We therefore classified the most assiduous users into TMGs? group (number of connections to the synthesis above the median) and TMGn? group (number of connections to the Nutri-Educ software above the median). Changes in body weight, BMI and waist circumference during follow-up were compared using the same procedure. The analyses were also stratified by gender to assess a potential change in terms of effect.
Patient Disposition and Baseline Characteristics Two hundred eighty-two T2D patients were enrolled in the study according to baseline HbA 1c level stratification, i. e. , HbA 1c \ 7. 5% (58 mmol/mol) (n = 131) or HbA 1c C 7. 5% (n = 151). They were then randomly assigned to two groups of 141 participants for either telemonitoring (TMG) or standard (CG) management. Nineteen participants were lost to followup (13 in the TMG and six in the CG) (Fig. 1 ). The analysis set comprised 263 individuals (128 in the TMG and 135 in the CG) who had at least one HbA 1c measurement during the follow-up period. Multivariate analyses were based on 256 participants (126 in the TMG and 130 in the CG) with no missing data among the variables entered in the model (data and confounding factors defined a priori).
Frequency of Device Use in Telemonitored Patients In the TMG, the mean number of connections to the device by patient was 104 ± 78 (median value: 86) over the 12-month follow-up period, which corresponds to a frequency of about twice a week. Users could access the different device functions at each connection. Mean data synthesis (TMGs) and Nutri-Educ software (TMGn) access figures were 44 ± 49 (median value: 29) and 48 ± 61 (median value: 31), respectively, demonstrating almost weekly use. On average, TMG patients sent 14 ± 13 messages (median value: 11) to the investigators, i. e. , about one message per month. The mean messaging frequency from the investigators to the participants was 5 ± 5 (median value: 3).
4% were completely satisfied or rather satisfied with device use and telemonitoring data synthesis. Very high levels of satisfaction (very satisfied or rather satisfied) were also recorded regarding the assistance provided by the device in terms of  improving their knowledge on diabetes (87. 6%) and nutrition (84. 3%). Device-generated assistance was deemed positive by 88. 2% of subjects for the daily management of diabetes, by 81% for ensuring a balanced diet and by 85. 5% for the practice of physical activity. Fifty-five percentage of the investigators completed the satisfaction questionnaire; 85% of them reported having completely integrated the web application functions and over 80% found it easy to very easy to use, in terms of both patient records and telemonitoring synthesis reports.
A reduction in waist circumference is also correlated with device use but no gender effect is apparent. Such positive effects on body weight and fat distribution may have been associated with improved insulin sensitivity and a reduction in cardiovascular risk factors, but these parameters were not documented in our study  [37]. In T2D, blood glucose monitoring must be linked to lifestyle interventions in order to improve metabolic control  [32]. Therefore, in our telemonitoring and coaching approach, we included not only a glucose meter but also other connected objects and tele-education modules targeting diet and physical activity. It is important to note that the Nutri-Educ software included in our device has already demonstrated its effectiveness in improving dietary balance, weight and glycaemic control in individuals with diabetes or obesity  [23, 24].
This study was submitted to the South-West and French Overseas Territories' Ethics Committee and received a favourable opinion from the EC on 27/05/2013. This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. Data Availability. The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ACKNOWLEDGEMENTS We are very grateful to all patients who participated in this study. We also wish to extend our grateful thanks to the clinical teams at the  University Hospital Centres of Grenoble, Lyon, Nantes and Nı ˆmes, the Hospital Centres of Cahors, Corbeil-Essonne, Lannemezan and Tarbes , the  Ambroise-Pare(Toulouse), Claude Bernard (Albi) and Pasteur (Toulouse) clinics  and our two colleagues working in private practices. Funding. This study and the journal's  Rapid Service Fee  has been funded by the  Ministry of Health  [call for projects for research programmes on the performance of the healthcare system (PREPS 2012 no. 12-018-0078 )] and the  Midi-Pyrenees Regional Council [Call for Projects for Research on Clinical Transfer and Standardization in Bio -health 2012, year 1 (no.
For training, synaptic values are updated by probing the neuron states after  (21)  or during  (33)  the nudging phase through a learning rule that has been shown theoretically and numerically to match the updates of Back-Propagation Through Time, the state-of-the-art algorithm for such recurrent neural networks  (24). It has been shown recently that Equilibrium Propagation also reaches accuracy within 1% of BPTT with convolutional architectures on the CIFAR-10 dataset  (25). The original version of Equilibrium Propagation uses a rate-based formulation where dynamical neurons evolve smoothly in time. For a network of leaky integrate and fire neurons described by an Hopfield-like energy function () = 1 2 ⁄ ∑   , where  are the membrane potentials of neurons and  their activation function, the Equilibrium Propagation learning rule is: ∆  ~ (    )  -(    )  , where the product     is measured at equilibrium, at the end of the nudge phase and the free phase  (21).
Inference speed and energy As EqSpike is derived from a rate-based algorithm, it is interesting for neuromorphic applications to quantify the number of spikes needed to achieve inference, and the time needed to reach high accuracy. Operation with fewer spikes is more desirable, as it reduces both execution time and energy consumption. Fig. 3  shows the inference results as a function of the execution time multiplied by the maximum frequency of neurons ( ×   ). The orange line is the mean accuracy result over the whole test dataset, obtained by computing the spike rate of output neurons (as done for Fig. 2  and Table  1 ), through averaging in a time window   of 100 simulation time steps (  = 50/  ) and considering that the neuron with highest frequency encodes the output. This method computes the rate accurately at the expense of having to wait the time   and letting output neurons spike multiple times.
Spiking neural networks also offer the possibility to accelerate the computation and reduce the energy consumption by determining the output class from the first output neuron to spike. The blue line in Fig. 3  is the accuracy as a function of time, averaged over all images in the test dataset, obtained by considering that the first output neuron to spike encodes the output. The red, vertical dotted line in Fig. 3  indicates the average time of the first spike at the output over all presented images, corresponding to t  3. 5/. At t  10/  , the accuracy of single-spike inference (blue curve) reaches 95. 11% ± 0. 78%, within 1. 4% of the precise rate computation (orange curve). This result shows that even though the algorithm is originally rate-based, a single spike at the output suffices in most cases to determine the correct class with good precision, a feature which is highly attractive for energy-efficient inference on neuromorphic chips.
3 ). For EqSpike, the recognition rate saturates at t 10/. The corresponding measured number of SynOps is about 150,000 in average. This is much less than expected if all neurons spiked. This is also a bit less but comparable to the number of SynOps needed at inference for Event-Driven Random BackPropagation  (13). Considering 10 pJ/SynOps, each EqSpike inference could potentially consume 1. 5 µJ. This means that testing the 10,000 images of the whole MNIST dataset could be achieved with a neuromorphic chip while consuming only 15 mJ, in other words, three orders of magnitude less than with a GPU  (45). In our current EqSpike implementation, the input layer is the one leading to most spikes and SynOps: with only 16% of illuminated pixels in average in MNIST, the input layer emits 87. 5% of all spikes and 98.
Training speed and energy Training with EqSpike requires performing the free phase and then the nudging phase, during which synaptic weights are updated. A way to speed-up the training, and reduce the total number of SynOps, is to perform the nudging phase only on poorly classified examples, and skip the updates when the accuracy is satisfactory. We apply this strategy inspired from (32) using the criteria that the nudging phase is performed only when the difference between the target rate and the actual rate (  ̂-  ) at the ouput is above 1%. Fig. 4a  shows the number of presented examples per epoch as a function of epoch number. In the last 20 epochs only approximately 15% of the training dataset still require a nudging phase. For MNIST, we thus perform the free phase on all the dataset (3×10 6 images for the 50 epochs), and the nudging phase on 489,000 images.
Given the durations of each phase, we can estimate the training time to Ttraining  2. 74×10 8 /. For electronic neurons with a firing rate of 100 kHz  (26) , this leads to Ttraining  45 min, and for electronic neurons with a firing rate of 10 MHz  (43)  to Ttraining  30s. As our networks feature a fully parallel nature, these training times would be the same for much wider networks, and increased by a factor of about eight only with four hidden layers  (25). As EqSpike is derived from a rate-based approach, it is interesting to compare the actual spiking rates of neurons in the network during training to their maximum frequency. For neuromorphic applications, low overall rates are indeed desirable. Fig  4b  shows the average number of spikes emitted by each neuron for an image presentation in the training dataset, as a function of the epoch.
5b. According to the Eq-Prop learning rule, in a network with unidirectional synapses,    ∝ ̇   = ̇    , a positive weight update should be applied. Due to the acceleration of the post-neuron, there are less post-neuron spikes before the pre-neuron spike than after. Therefore, tpost -tpre is positive in average, yielding a positive weight update through STDP. We have investigated if STDP-like weight updates did emerge during learning in our simulations. For this purpose, we monitored weight variations in synapses that connect input neurons and the hidden layer neurons. These synapses are unidirectional as input neurons are clamped to the input (see Fig. 1 ): their frequency does not vary. We used 100 images during the first epoch for the MNIST dataset. The curve in Fig. 5c  shows the average weight updates in the first layer as a function of the time difference between post-synaptic neuron spikes and the average time of pre-synaptic neuron spikes in a window of 200 time steps before the post-synaptic neuron spike.
g. , identifies and flags bad data), extracts photometry for each target star in the TESS field of view, and searches the resulting light curves for exoplanet transit signatures. The initial TESS light curve was produced using simple aperture photometry (SAP,  Morris et al. 2020 ) and then the instrumental systematic effects were removed using the Presearch Data Conditioning (PDC) pipeline module  (Smith et al. 2012 , Stumpe et al. 2014). The light curves are searched with an adaptive, wavelet-based matched filter  (Jenkins 2002 , Jenkins et al. 2020a)  , and then fitted to limb-darkened transit models  (Li et al. 2019)  , and subjected to diagnostic tests to make or break the planetary hypothesis  (Twicken et al. 2018). Subsequently, the TESS science office reviewed the transit signature identified in the SPOC processing of Sector 9, promoting it to a planetary candidate as TESS Object of Interest (TOI) TOI-674.
Ground-based photometry After the discovery of the transits made by the TESS team, several follow-up ground-based observations were scheduled to confirm that the transits occur on TOI-674 and rule-out some false positive scenarios (e. g. , eclipsing binaries unresolved by TESS). The data were acquired under the TESS Follow-up Observing Program (TFOP) and uploaded to the Exoplanet Followup Observing Program for TESS (ExoFOP-TESS 2  ).
High-resolution imaging TESS has a pixel scale of 21 arcsec/pixel, hence it is possible that TOI-674 can present some level of flux contamination produced by nearby faint stars not detected by seeing limited photometric observations. Contamination in the flux of the transit host star can led to a wrong estimation of transit depth, thus leading to an incorrect absolute planet radius. TESS Data Validation Report  (Twicken et al. 2018 ) performs a difference image centroiding analysis, in the case of TOI-674 the report determined that the location of the source of the transit signature was within 1 arcsec of the target star. Nevertheless, we searched for previously undetected faint nearby companion stars using adaptive optics and speckle imaging using Gemini Telescope. Article number, page 3 of 22 A&A proofs: manuscript no.
Transit light curve and radial velocity model The light curves were modeled with the help of the transit modeling package PyTransit 3  (Parviainen 2015) version 2. 0. We used the PyTransit model implementation of the  Mandel & Agol (2002)  analytic transit model with a quadratic limb darkening law. Due to the large number of data points, the Spitzer and ground based light curves were detrended before performing the joint fit to speed up the fitting process. For the Spitzer time series we performed Pixel Level Decorrelation (PLD) following  Deming et al. (2015)  to remove the instrumental noise known to affect Spitzer data. We computed the normalized pixel intensities for each of the 3 × 3 Spitzer pixels Pt i = P t i N i=1 P t i (1) where P t i is the i-th pixel inside the aperture at a time t of the time series.
For the TESS time series, besides using an analytical model for the transit we fitted the stellar variability present in the time series using Gaussian Processes (GPs; e. g. ,  Rasmussen & Williams 2010 , Gibson et al. 2012 , Ambikasaran et al. 2015). The TESS GPs were computed using the python package Celerite (Foreman-Mackey et al. 2017); we chose a Matern 3/2 kernel: k i j TESS = c 2 1        1 + √ 3|t i -t j | τ 1        exp        - √ 3|t i -t j | τ 1        (3) where |t it j | is the time between points in the series, c 1 is the amplitude of the variability, and τ 1 is a characteristic time-scale. The constants c 1 and τ 1 were set as free parameters. The radial velocity data was modeled using RadVel 5  (Fulton et al. 2018). The free parameters in the radial velocity fit were the planet induced radial velocity semi-amplitude (K RV ), the host star systemic velocity (γ 0 ), the instrumental radial velocity jitter (σ RV jitter ), while the orbital period and central transit time were also set free but taken to be global parameters in common with the fits of the light curves.
Using this parametrization we are sampling values of e ∈ [0, 1] and ω ∈ [0, 2π] (after imposing e < 1). Table  C. 1 presents the prior functions and limits used in both global fits. For the circular orbit case we used an uninformative prior (uniform) for the stellar density, with ρ ∈ [5, 15] g cm -3. For the fit allowing a non-zero eccentricity, we imposed a more restrictive range of values for the stellar density, using a normal 5 https://github. com/California-Planet-Search/radvel 6 https://github. com/hpparvi/PyDE prior with the density and 2-σ uncertainty found by our procedure used to obtain the stellar parameters (see Section 3. 1).
Mass, radius and composition We find that TOI-674b has a radius of 5. 25 ± 0. 17 R ⊕ and a mass of 23. 6 ± 3. 3 M ⊕. Combining these measurements we find that TOI-674b has a mean density of ρ p = 0. 91 ± 0. 15 g cm -3. We compared the mass and radius of TOI-674b with known exoplanets taken from the TEPcat database  (Southworth 2011)  and the composition models of  Zeng et al. (2019). The planet models consider an isothermal atmosphere and are truncated at 1 milli-bar pressure level (defining the radius of the planet). Figure  8  shows the mass-radius diagram for known transiting exoplanets with masses measured with a precision better than 30% and the rocky planet models of  Zeng et al. (2019)  with an equilibrium temperature of 700 K, i. e. , the equilibrium temperature closest to the likely temperature of TOI-674b.
Conclusions We report the discovery and confirm the planetary status of TOI-674b, a super-Neptune transiting exoplanet orbiting around a nearby M dwarf. NASA's TESS observations led to the initial detection of the transits. Follow-up photometric observations made with several facilities and radial velocity measurements taken with the HARPS spectrograph made possible confirmation of the planetary nature of the transiting object and establishment of its mass and radius. From a HARPS mean stellar spectrum we estimate that the spectral type of the host star is M2V and that the star has an effective an effective temperature of T eff = 3514 ± 57 K, and a stellar mass and radius of M = 0. 420 ± 0. 010 M and R = 0. 420 ± 0. 013 R respectively. We analysed the data from Sector 9 and 10 two minute cadence time series observations from TESS (PDCSAP curves) plus single transit follow-up observations from Spitzer Space Telescope (4.
This planet is a new addition to the so-called Neptunian Desert. The Transmission Spectroscopy Metric (TSM) of TOI-674b is ∼ 240, one of the highest TSM for Neptunian Desert planets. Thus, TOI-674b is a promising candidate for atmospheric characterisation using JWST. Article number, page 12 of 22  We searched for evidence of another planets in the system by measuring the transit timing variations using the data from TESS, Spitzer, and ground-based follow-up observations. We find no significant deviations from a linear ephemeris for the central time of the transits in a time baseline of ∼ 200 days. TOI-674 is expected to be observed again by TESS during its extended campaign. The observations will be done from 7 March 2021 until 2 April 2021 (corresponding to Sector 36); these new TESS observations will help to refine TOI-674b's orbital parameters and in the search for additional planets in the system.
Acknowledgements. N. A. -D. acknowledges the support of  FONDECYT 3180063. D. D. acknowledges support from the  TESS Guest Investigator Program  grant  80NSSC19K1727  and  NASA   Exoplanet Research Program  grant  18-2XRP18_2-0136. X. D. , T. F. , and G. G. acknowledge funding in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes. G. M. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 895525. This paper includes data collected by the TESS mission, which are publicly available from the  Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by  NASA's Science Mission directorate.
This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalização by these grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020; PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113 ;  PTDC/FISAST/28953/2017 & POCI-01-0145-FEDER-028953. This work made use of tpfplotter by  J. Lillo-Box  (publicly available in www. github. com/jlillo/tpfplotter),which also made use of the python packages astropy, lightkurve, matplotlib and numpy. This work makes use of observations from the  Las Cumbres Observatory Global Telescope Network. This work made use of corner. pyby  Daniel Foreman-Mackey  ( Foreman-Mackey 2016 ).
, 2009; Ko ¨hler et al. , 2019). Alternatively, multipole parameters can be transferred from databanks (either constructed from theoretical calculations or averaged over experimental multipole refinements) and fixed during the refinement of positions and ADPs, which leads to more stable refinements of H-atom parameters  (Dittrich et al. , 2005; Dadda et al. , 2012; Ba ˛k et al. , 2011; Jha et al. , 2020). In Hirshfeld atom refinement (HAR)  (Jayatilaka & Dittrich, 2008; Capelli et al. , 2014) , H-atom positions and sometimes also ADPs can be refined freely, and the results agree favourably with those from neutron diffraction  (Woin ´ska et al. , 2016; Fugel et al. , 2018; Sanjuan-Szklarz et al. , 2020). HAR has also been tested for strong hydrogen bonds  (Woin ´ska et al. , 2014). A drawback of the HAR method is its reduced speed in comparison with MM methods, since it relies on the repeated calculation of molecular wavefunctions.