dataesr/scientific-paragraphs-categorization huggingface dataset:
RESEARCH METHODS The study uses a quantitative approach to obtain research results and conclusions. Therefore, research tools are statistical with descriptive research methods and explanatory research. The population in this study are e-payment who use ShopeePay in making payment transactions at least three times. The sample in this study is infinite, so one way to determine the number of samples is by using the Lemeshow formula. This study obtained a number of 200 respondents by distributing questionnaires through Google Form. The data analysis technique in this study uses path analysis and Sobel test. The following is the research design and method of substructure equations I and II in this study:  = error structure 2 The largest respondents are women aged 21-25 years, are at Bachelor Degree (S1) graduates, have income > 4,000,001 and expenses of 500,000.
One of the most popular tools in constructing these electrode structures in silico is the commercial software GeoDictÒ. INNOV is a free-of-charge alternative developed by our group within the ARTISTIC project, which only requires a standard MatlabÒ license to be used  [18]. Following the GeoDictÒ-based approaches, and based on percolation theory, Bielefeld et al. studied the impact of carbon-free composite electrode formulation (AM/SE ratio), porosity, particle size and electrode thickness on the formation of ionic and electronic percolation networks (Figure  3 )  [19]. The model is built from small spherical AM particles and polyhedral SE particles, where the conduction clusters are calculated according to the percolation theory. It was found that the small AM particles enhance the effective electronic conductivity, offering a high surface area and a higher number of possibilities to connect particles with each other.
We can speculate that the existing scientific publications may already fulfil these requirements. In that sense, Kuniyoshi et al. have reported an automated machine reading system for extracting the synthesis processes of materials for ASSBs available in the scientific literature  [42]. The authors defined the representation of the synthesis processes using flow graphs and created a data corpus from the experimental sections of 243 papers. The automated machine-reading system is developed by a deep learning-based sequence tagger. The script automatically creates synthesis graphs that represent the synthesis process of ASSBs in text. However, this preliminary work faces the issue of data incompleteness in literature. This issue has recently been pointed out in a text mining study of 13000 papers about LIBs and sodium-ion batteries, calling for the standardization of data produced both from experiments and simulations  [43].
Such a digital twin also integrates a set of ML models that accelerates the physical models' parameterization, assesses experimental data and originates surrogate models with lower computational costs  [39, 60, 61]  that have been used by us for electrodes optimization and inverse design of manufacturing parameters  [62]. Regarding the latter, the ARTISTIC project high-fidelity physics-based models are being used to generate synthetic data that can be used to augment experimental data sets acquired in our LIB pilot line. The use of ML on these synthetic and/or experimental datasets allows deriving surrogate models which are embbeded in optimization algorithmic loops to predict which manufacturing parameters (e. g. slurry formulation, drying rate, calendering pressure) must be adopted in order to maximize and minimize multiple electrode properties simultaneously (e.
Acknowledgements M. A. , V. S. , and A. A. F. acknowledge the support by  Umicore Specialty Powders France  within  DESTINY COFUND PhD Program , which has received funding from  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  (grant agreement No  945357 ). A. A. F. acknowledges the European Union's  Horizon 2020 research and innovation program  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. and F. M. Z. acknowledge the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  957189  ( BIG-MAP ). A. A. F. acknowledges  Institut Universitaire de France  for the support.
2 models per week during the first 10 months of 2021 (Fig. 1C  and SI Appendix, Fig. S1 ). A total of 28 models met inclusion criteria, yielding 1,791 submission files with 556,050 specific predictions for unique combinations of forecast dates, targets (horizons forecasted), and locations. The evaluated forecasts used different data sources and made varying assumptions about future transmission patterns (SI Appendix, Table  S1 ). All evaluated models, other than CEID-Walk, the COVIDhub-baseline, the COVIDhubensemble, and PSI-Draft, used case data as inputs to their forecast models. A total of 10 models included data on COVID-19 hospitalizations, 10 models incorporated demographic data, and 9 models used mobility data. Of the 28 evaluated models, 7 made explicit assumptions that social distancing and other behavioral patterns would change over the prediction period.
g. , times when a state reported a large backfill of deaths in the most recent week) (SI Appendix, SI Text). In general, other models did not show unusual errors in their forecasts originating from these anomalous data, suggesting that their approaches, including possible adjustments to recent observations, were robust to anomalies in how data were reported. Model Performance in Specific Pandemic Waves. In addition to evaluating performance in aggregate across the entire evaluation period and separate phases, we evaluated model performance during important moments during the pandemic. To assess the impact of rapidly changing trends on incident death forecasting accuracy, we ran an analysis restricted to specific locations and time periods that experienced high rates of change during four different waves of the pandemic (Fig.
Forecast performance may be affected by ground-truth data and forecast target. Ground-truth data are not static. They can be later revised as more data become available (Dataset S1). There are also instances in which data are not revised but rather left with large peaks or dips due to reporting effects, especially around holidays. Different sources for ground truth data can also have substantial differences that impact model performance. Lastly, because this evaluation focuses on incident death forecasts, it cannot speak to model performance for incident cases or hospitalizations. Deaths may serve as a lagging indicator of COVID-19, thus making it more predictable than hospitalization and case targets  (22). While the Hub has provided many insights into what has and has not been predictable in the COVID-19 pandemic, it also has left many important questions unanswered.
Methods Surveillance Data. Early in the COVID-19 pandemic, the Johns Hopkins CSSE developed a publicly available data-tracking system and dashboard that was widely used  (24). CSSE collected daily data on cumulative reported deaths due to COVID-19 at the county, state, territorial, and national levels and made these data available in a standardized format beginning in March 2020. 1. 2 0. 9 0. 9 0. 8 0. 9 1. 7 0. 9 1. 1 0. 7 1. 0 1. 0 0. 8 1. 4 0. 8 1. 0 0. 9 0. 7 1. 1 1. 0 0. 9 0. 8 1. 3 0. 7 1. 2 1. 0 0. 8 1. 2 0. 9 1. 1 1. 0 1. 4 0. 8 1. 1 1. 3 0. 9 0. 9 0. 7 1. 1 1. 0 1. 2 1. 7 0. 9 1. 0 1. 0 0. 9 2. 1 0. 9 1. 0 0. 9 0. 9 0. 9 1. 4 1. 2 1. 1 0. 9 0. 7 1. 1 1. 6 1. 3 0. 9 1. 2 1. 8 0. 9 1. 0 0. 7 0. 9 1. 0 1. 0 1. 1 0. 9 8. 4 0. 9   ). Teams are listed on the horizontal axis in order from the lowest to highest relative WIS values (Table  1 ).
V I D J H U _ C S S E -D E C O M U S C -S I _ k J a lp h a B P a g a n o -R t D r iv e n M O B S -G L E A M _ C O V I D C M U -T im e S e r ie s I H M E -S E I R U C S D _ N E U -D e e p G L E A M L A N L -G r o w t h R a t e C U -s e le c t U A -E p iC o v D A C o v id A n a ly t ic s -D E L P H I J H U _ I D D -C o v id S P C E I D -W a lk C O V I D h u b -b a s e li n e C o v id 1 9 S im -S im u la t o r J H U A P L -B u c k y R o b e r t W a lr a v e n -E S G U M ic h -R id g e T f R Incident deaths were inferred from this time series as the difference in reported cumulative deaths on successive days. Throughout the real-time forecasting exercise described in this paper, the Forecast Hub stated that forecasts of deaths would be evaluated using the CSSE data as the ground truth and encouraged teams to train their models on CSSE data.
Like data from other public-health systems, the CSSE data occasionally exhibited irregularities due to reporting anomalies. CSSE made attempts to redistribute large "backlogs" of data to previous dates in instances in which the true dates of deaths, or dates when the deaths would have been reported, were known. However, in some cases, these anomalous observations were left in the final dataset (SI Appendix, SI Text). All updates were made available in a public GitHub repository (https://github. com/CSSEGISandData/COVID-19/ tree/master/csse_covid_19_data#data-modification-records). Weekly incidence values were defined and aggregated based on daily totals from Sunday through Saturday, according to the standard definition of epidemiological weeks (EW) used by the CDC  (25). Forecast Format. Research teams from around the world developed forecasting models and submitted their predictions to the COVID-19 Forecast Hub, a central repository that collected forecasts of the COVID-19 pandemic in the United States beginning in April 2020.
For delta phase eligibility, a model required submissions for at least 16 out of 26 submission weeks between EW17-2021 and EW42-2021. These phases were determined based on the waves of deaths at the national level during pandemic (Fig. 1B ). Each phase includes a period of increasing and decreasing incident deaths, although forecasts for the spring phase did not begin early enough to capture the increase in many locations. Forecasts were scored using CSSE data available as of November 16, 2021. We did not evaluate forecasts on data first published in the 2 wk prior to this date due to possible revisions to the data. Disaggregated Forecast Evaluation by Pandemic Wave. In a post hoc secondary analysis, we evaluated forecasts made in selected locations during selected pandemic waves. We used the following criteria in selecting locations and waves to represent this analysis (Fig.
4  and Dataset S2). • We selected states that had unusually severe waves or whose waves "led" the overall wave. Locations for which data for weekly deaths during the wave had been substantially revised after the initial report were excluded from consideration. • We picked an initial date near the start of the first increase at the start of the wave and a last date at the end of the steep decline of the wave. To compare the forecasts during the waves, we plotted 1-and 4-wk-ahead forecasts and calculated 95% PIs coverage rates of forecasts made for the given location both during the wave of interest over all weeks. Coverage rates were computed for models that were included in the overall analysis (see eligibility in Methods section: Forecast model eligibility and evaluation period) and, for inclusion in the coverage calculations for each wave, the model additionally had to have made forecasts for at least 3 wk in the selected wave.
The national-level forecasts were not included in the aggregated scores because the large magnitude of scores at the national level strongly influences the averages. However, in analyses for which scores were stratified by location, we included forecasts for all US states, including territories and at the national level. This evaluation used the CSSE COVID-19 surveillance data as ground truth when assessing forecast performance. We did not score observations when ground-truth data showed negative values for weekly incident deaths (due to changes in reporting practices from state/local health agencies [e. g. , removing "probable" COVID-19 deaths from cumulative counts]). This occurred 11 times. Forecast Models. For the primary evaluation, we compared 28 models that submitted eligible forecasts for at least 47 of the 79 wk considered in the overall model eligibility period (Fig.
1 ). Teams that submitted to the COVID-19 Forecast Hub used a wide variety of modeling approaches and input data (SI Appendix, Tables  S1  and S2 ). Two of the evaluated models are from the COVID-19 Forecast Hub itself: a baseline model and an ensemble model. The COVIDhub-baseline model was designed to be a neutral model to provide a simple reference point of comparison for all models. This baseline model forecasted a predictive median incidence equal to the number of reported deaths in the most recent week (y t ), with uncertainty around the median based on changes in weekly incidence that were observed in the past of the time series (details in SI Appendix, SI Text). The COVIDhub-ensemble model combined forecasts from all models that submitted a full set of 23 quantiles for 1-through 4-wk-ahead forecasts for incident deaths.
In all of these situations, modeling teams attested (via annotation on the public data repository) to the fact that they were correcting inadvertent errors in the code that produced the forecast and that the forecast used as input only data that would have been available before the original submission due date. In these limited instances, we evaluated the most recently submitted forecasts. Evaluation Methodology. We evaluated aggregate forecast skill using a range of scores that assessed both point and probabilistic accuracy. These scores were aggregated over time and locations for near-term forecasts (4 wk or less Point forecast error was assessed using the MAE defined for a set of observations y 1:N and each model's designated point predictions ŷ 1:N as MAE ¼ 1 N ∑ N i¼1 j y i À ŷ i j. To assess probabilistic forecast accuracy, we used two scores that are easily computable from the quantile representation for forecasts described in the Methods section, Forecast Format.
The same procedure was used to compute a relative MAE. Data Availability. The forecasts from models used in this paper are available from the COVID-19 Forecast Hub GitHub repository (https://github. com/ reichlab/covid19-forecast-hub)  (4, 34)  and the Zoltar forecast archive (https:// zoltardata. com/project/44) (35). These are both publicly accessible. The code used to generate all figures and tables in the manuscript is available in a public repository (https://github. com/reichlab/covid19-forecast-evals). All analyses were conducted using the R language for statistical computing (version 4. 0. 2) (36). We followed the EPIFORGE 2020 guidelines for reporting results from epidemiological forecasting studies (SI Appendix, Table  S5 )  (37).
The time to reach the platform was recorded with a video camera that was fixed to the ceiling of the room and connected to a computerized tracking system (Videotrack, Viewpoint, Lyon, France) located in an adjacent room. Four weeks after learning, rats were submitted to a retention test in the water maze. During the test, rats were put in the water maze for 60 s in the absence of the platform. Performances were assessed using several parameters: the amount of time spent in and number of entries (annulus crossing) into each zone. Zones were defined as a 15 cm radius (corresponding to 2. 8% of pool surface  (Moser et al. , 1998) ) around the center of the original platform location (Target zone; T) and the three equivalent areas in each of the other quadrants (other zones; O). The discrimination ratio for comparing data in the target zone and data in the other zones was calculated as follows: ((T-O)/(T + O)).
Infected newborn granule cells were identified using GFP with a fluorescence/infrared light (pE-2 CoolLED excitation system, UK). Neuron action potential firing was monitored in whole-cell currentclamp recording configuration. Patch electrodes were pulled (micropipette puller P-97, Sutter instrument, USA) from borosilicate glass (O. D. 1. 5 mm, I. D. 0. 86 mm, Sutter Instrument) to a resistance of 2-4 mΩ. The pipette internal solution contained [in mM: 125 potassium gluconate, KCl, 10 Hepes, 0. 6 EGTA, 2 MgCl2, 7 Phosphocreatine, 3 adenosine-5 ′triphosphate (magnesium salt), 0. 3 guanosine-5 ′ -triphosphate (sodium salt) (pH adjusted to 7. 25 with KOH; osmolarity 300 mOsm/l adjusted with D-Mannitol)] as well as biocytin 0. 4% (a liquid junction potential of -14. 8 mV was corrected for in the data and statistics).
In order to test the effect of CNO on IP firing, neurons were clamped with positive currents until reaching low excitable potential (AP frequency<2 Hz). It should be noted that the current injected was no different between control GFP-RV and Gs-GFP-RV infected cells (t 10 =1. 078; p = 0,31). CNO (10 μM in aCSF) was fast perfused close to the recording cell for 30 s then immediately washed out. Electrophysiological data were recorded using a Multiclamp 700B amplifier (Molecular devices, UK), low-pass filtered at 4 kHz and digitized at 10 Hz (current clamp) or 4 Hz (voltage clamp) (Digidata 1440 A, Molecular devices, UK). Signals were analyzed offline (Clampfit software, pClamp 10, Molecular devices, UK). For statistical analysis, "Vehicle" data were collected during the last 60 s before CNO perfusion, then "CNO" data were collected after 45 s of CNO treatment.
5b-f; S4a-d). Furthermore, latency, mean speed and thigmotaxis revealed no difference between groups (Fig. 5g-i ). We estimated the number of neurons transduced by the Gs-GFP-RV and we found, as expected, that more neurons were targeted compared to what was found in previous experiments with adult-born neurons. 7588 ± 1255 cells were GFP positive and distributed along the DG septo-temporal axis (Fig. 5j ). These results indicate that the beneficial memory effects of chemogenetic stimulation do not depend on the number of DG cells transduced. Altogether these data reveal that chemogenetic stimulation of adultborn neurons generated before learning, and not neonatally-born granule neurons, enhances remote memory quality which fades with the passage of time. We next investigated whether chemogenetic stimulation of adultborn neurons could enhance long-term memory in another hippocampal-dependent task, i.
Funding This work was supported by INSERM (to DNA) and the ANR (to ST ANR-16-CE37-0018-01; to DC ANR-13-BSV4-0006-01) and by the Université de Bordeaux. ML was supported by a MESR (Ministère de l ′ Enseignement Supérieur et de la Recherche) fellowship and by the ANR (ANR-16-CE37-0018-01). NM was supported by the ANR (ANR-16-CE37-0018-01) and by the Fondation Fyssen. PM was supported by the FRC (to DNA). This work benefited from the support of the Biochemistry and Biophysics Facility of the Bordeaux Neurocampus, funded by the LabEX BRAIN ANR-10-LABX-43 and the Animal Housing facility funded by INSERM and LabEX BRAIN ANR-10-LABX-43. The confocal analysis was done in the Bordeaux Imaging Center (BIC), a service unit of the CNRS-INSERM and Bordeaux University, member of the national infrastructure France BioImaging supported by the French National Fig.
Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces.
Together with other rhythms, such as the low-gamma band, it participates in the coordination of bottomup and top-down information flows  [42] [43] [44]. These frequencyspecific neural signals could be of particular importance for intended speech decoding, as focal articulatory signals indexed by BHA are expected to be notably weaker during imagined speech. In this study, we set out to use ECoG data sampled in patients with epilepsy to explore the range of representation level(s) and neural features that could potentially be usable in future imagined speech decoding BCIs. Rather than adopting a purely neuroengineering perspective involving large datasets and automatized feature selection procedures, we used a hypothesis-driven approach assuming a role of low-frequency neural oscillations and their cross-frequency coupling in speech processing, within both perceptual and motor representation spaces.
To enable a fair comparison of overt and imagined speech in our study, we checked that the three current datasets were free of acoustic contamination (Supplementary Fig. 22 ). A further technical advantage of silent speech is the absence of movement artefacts. In the three presented studies, the task instructions explicitly stated that participants should not articulate. On-line and off-line (audio/video recordings) monitoring of the patients, confirmed that they did not silently mouth or whisper words (see Methods section), even though we could not control for some degree of silent mouthing. Note that although we could have expected less residual motion after the instruction "imagine hearing" (study 1) than "imagine speaking" (studies 2 and 3), our results did not confirm any such bias (Supplementary Fig.
No monetary compensation was given to the participants. Studies and data acquisition. Three distinct experiments were performed, one in each study center. In all three studies, participants were closely monitored by the experimenter to prevent any residual movements. The absence of motion was further checked post-hoc by visual inspection of video recordings. Participants also confirmed after the experiment that they followed instructions (i. e. using imagine hearing or imagine speaking strategy). Study 1: word repetition. The first study was a word repetition paradigm (Fig. 1a ). This data appeared first in  18. The participant first heard one of six words presented through a loudspeaker (average length: 800 ms ± 20). A first cross was then displayed on the screen (1500 ms after trial onset) for 1000 ms, indicating that the participant had to imagine hearing the word.
Reference and ground were chosen by selecting ECoG contacts away from epileptic foci and regions of interest. Data acquisition and synchronization with task stimuli were performed with the BCI2000 software  70. The participant's voice was also acquired through a dynamic microphone (Samson R21s) that was rated for voice recordings (bandwidth 80-12000 Hz, sensitivity 2. 24 mV/Pa) placed 10 cm away from the patient's face. Another dedicated 16-channel g. USBamp amplifier was used to acquire and digitize the microphone signal to guarantee synchronization with ECoG data. Finally, the participants' compliance with the imagined task was verified with an eye-tracker (Tobii T60, Tobii Sweden). Study 2: rhythmic word repetition. The second study was also a word repetition paradigm (Fig. 1b ). The participant first read one of twelve words presented on a laptop screen for 2000 ms.