dataesr/scientific-paragraphs-categorization huggingface dataset:
Generally speaking, the lower the number of samples and the higher the ratio of features to sample size, the more a machine learning model will fit the noise in the data instead of a meaningful underlying pattern  (Jain & Chandrasekaran, 1982; Kanal & Chandrasekaran, 1971 ). In the case of our auditory spectrograms, algorithms would need to be trained/tested on a maximum of 120 samples with 4480 features each (as a benchmark: samples for each neural estimator in the main analyses were approximately 1600 and contained 1260 features each). Evidently, such disproportionate dataset is ill-suited for the same kind of estimators used on the ERPs: instability and overfitting would completely undermine the reliability (and therefore interpretability) of the outcome. but not place (top rows) or to discriminate place but not manner (bottom rows).
ACKNOWLEDGMENTS This research was supported by grants from the  Fondation NrJ ,  Fondation Bettencourt  and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 695710 ). We are grateful to  Don Tucker  and  Amy Rowland  ( EGI and University of Oregon ) for designing the 256-electrodes net; and to  Bahar Khalighinejad  for providing help with auditory spectrogram estimation. We are also thankful to  Stanislas Dehaene ,  Yair Lakretz  and  Christophe Pallier  for constructive feedback and suggestions. This paper is dedicated to  Sébastien Marti , whose mentorship was essential. His smile, kindness and friendship remain in our hearts.
Fluorescent confocal microscopy 1 mL of small-scale cultures was centrifuged for 5 min at 2000 × g followed by two washing steps with 1 mL of PBS pH 7. 4. The cells were re-suspended in 1 mL of PBS pH 7. 4, and 2 µL were deposited on an agarose pad (1% (w/v) agarose gel in milliQ water). The pictures were taken in the Light Microscopy Facility of ImaGif (I2BC, Gif-sur-Yvette, France) using a Leica confocal microscope SP8 controlled by the LAS-X software and using an oil immersion objective lens with 100 x magnification. Samples were excited with a 488 nm laser.
Acknowlegments We would like to thank  Dr Christine Jaxel  for all the support and discussions during this project, and to  Dr Eugene Diatloff  and  Dr Jing Yuan  for critically reading the manuscript. We also thank  Quentin Rochette  for his early work on this project. This work was supported by the  ANR  grants  ANR-14-CE09-0022  to GL, and  ANR-18-C811-0009-02  to JLVI, the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INSB-05 ), and the  Centre National de la Recherche Scientifique (CNRS). In memory of  Prof Ronald H. Kaback.
Creep properties are determined in design standards by measuring the creep coefficient, noted ϕ, as a function of time, t, and the age of the concrete at loading, t 0. The work aims to study the validity of the analytical models proposed in the most used international standards and to check the possibility of their extension to estimate the creep of recycled aggregates concrete (RAC). A database was built from experimental results available in bibliographic references including 121 creep curves divided into 73 curves for natural aggregates concrete (NAC) and 48 curves for RAC. The comparison between the experimental and predicted values showed a significant dispersion for NAC and RAC. For the remediation of this dispersion, a new analytical model was developed for NAC. The parameters being the conventional creep coefficient, ϕ 0 , the power of the ageing function, named α, and β h , which accounts for the relative humidity and the compressive strength in the ageing function, were identified by inverse analysis.
Other studies will be presented and analyzed thereafter, but the common point between all the works is that the authors have tried to model the creep behavior of RAC based on their results by admitting the validity of analytical models for NAC  [9, [18] [19] [20] [21] [22] [23]. In addition, some authors propose modifications with factors that are often difficult to measure  [8, 9, 23]. The first aim of the present paper is to revise the applicability of analytical models adopted in design standard for NAC and to study their possible extension to RAC using a new database built from the experimental results available in the literature. The second aim is to develop a new analytical expression for NAC which can be extended to RAC simply by considering the equivalent replacement ratio. To achieve the main objective, a new creep database including 121 curves divided into 73 curves for natural aggregates concrete (NAC) and 48 curves for recycled aggregates concrete (RAC) was built from experimental results available in bibliographic references.
Experimental Database Many databases have been proposed in the bibliographical references, but unfortunately they do not provide all the necessary information for the requalification of creep analytical models  [17, 20, 28]. For this reason, a database consisting of 73 NAC formulations and 48 RAC mixtures was established in this work based on the available studies in the literature  [8, 11, 16, 21, 22, [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]. The chosen references provide the following information: This database, despite the reduced number of experimental points compared to the other databases  [9, 20, 28] , considers not only the final creep coefficient but all the creep over time curves. For the acquisition of experimental curves, the free software PlotDigitizer (http://plotdigitizer.
Identification of Creep Parameters The creep model can be expressed by the following equation: ϕ(t, t 0 ) = ϕ 0,EXP t -t 0 β h + t -t 0 α EXP (8) The equation has three parameters which can be at the origin of the dispersion between the experimental values and those predicted according to analytical models. Those terms are, coefficient of conventional creep, ϕ 0,EXP , the power of the aging function, α EXP , and β h , which depends on the relative humidity (RH%) and the notional member size, h 0. In this work, the three parameters were calculated using an optimization approach which consists in minimizing the difference between experimental creep curves and the calculated analytical ones according to Equation  (8). The procedure was implemented in Matlab and begins by imposing initial values to parameters ϕ 0,EXP , α EXP , and β h , allowing to generate a creep curve using Equation (  8 ).
The function fminsearch of Matlab is used iteratively to estimate new values, while the square of difference between analytical and experimental curves is higher that 10 -3. The optimization was initially conducted for the NAC in order to verify the reliability of the analytical relationships, especially the EC2. The results illustrated in Figure  5  show that the average value of α EXP , the power of the aging function, is 0. 44 ± 0. 15 and not 0. 3, as given in EC2. The sensitivity of φ0,EXP was studied with the variation of material param as the conditions of the creep test. The results are presented in Figure  6  as a (a) concrete compressive strength, fcm, (b) temperature, T, (c) relative humid notional member size, h0, (e) stress level, kσ, (f) the age of concrete at loading, volume, Vpaste, and (h) sand to aggregate ratio,  +.
44 2 0 0 m 0,EC2 h_Modif 0 t -t S φ t,t = + Γ φ S + G β + t -t 5. 7 1 0. 33 ( 1 The corrective term (1 + 0. 33Γ  ) was also introduced in the expression of Equatio (  12 ) for the calculation of RAC creep coefficient according to the ACI standard. It is wor mentioning that for the qualification of the models for the RAC, certain studies were elim inated from the database. The works of Tia et al. [36]  and Ghodousi et al. [37]  have bee omitted because the authors used a percentage of cementitious materials in concrete mi tures higher than 30% of the total mass of binder, which is the allowed limit defined b European standards  [25, 39]. For other studies  [11, 16, 35] , the creep values were too f from other values. Hence, a total of 20 RAC values were used for the validation of pr posed modified relations.
The predicted curves using modified models are compared the creep data selected in the database of the present work. The correlation factors a tabulated in Table  4 , where it is rather interesting to note that they are better than valu obtained for models without modification. Some representative comparisons for RAC before and after modification are pr sented in Figure  11. The difference without proposed modifications is explained by th fact that the normative models do not consider the effect of recycled aggregates on cree  In order to account for the presence of recycled aggregates, the term (1 + α M Γ m ) was introduced in Equation (  11 ) and a value of α M = 0. 33 was found by a linear regression. The final expression for creep coefficient for recycled aggregate concrete takes the form: ϕ(t, t 0 ) = 5.
7 S S + G 2 (1 + 0. 33Γ m )ϕ 0,EC2 t -t 0 β h_Modi f + t -t 0 0. 44 (13) The corrective term (1 + 0. 33Γ m ) was also introduced in the expression of Equation (  12 ) for the calculation of RAC creep coefficient according to the ACI standard. It is worth mentioning that for the qualification of the models for the RAC, certain studies were eliminated from the database. The works of Tia et al. [36]  and Ghodousi et al. [37]  have been omitted because the authors used a percentage of cementitious materials in concrete mixtures higher than 30% of the total mass of binder, which is the allowed limit defined by European standards  [25, 39]. For other studies  [11, 16, 35] , the creep values were too far from other values. Hence, a total of 20 RAC values were used for the validation of proposed modified relations.
The predicted curves using modified models are compared to the creep data selected in the database of the present work. The correlation factors are tabulated in Table  4 , where it is rather interesting to note that they are better than values obtained for models without modification. Some representative comparisons for RAC before and after modification are presented in Figure  11. The difference without proposed modifications is explained by the fact that the normative models do not consider the effect of recycled aggregates on creep in addition to their insufficiency already mentioned for the NAC. By considering all modifications, the calculated values agreed well with experimental data on the development of creep over time. This improvement is attributed to the modification of β h and the power of the ageing function, as well as to the introduction of the term S S + G for NAC together with the introduction of 1 + 0.
Conclusions This study presented an analysis of data available in the literature on the ability of design standards to predict the creep behavior of natural and recycled aggregate concretes. The comparison between experimental and predicted values shows a significant dispersion in terms of creep coefficient. However, EC2 was the most suitable for estimating creep for NAC and RAC as compared to both ACI and MC2010 models and it was then chosen for a possible modification. Based on the general expression of the creep function, the conventional creep coefficient, named ϕ 0,EXP , the power of the aging function, α EXP , as well as β h were identified by an optimization approach using the database of NAC. It was found that the value of α EXP , the power of the aging function, is 0. 44 ± 0. 15 and not 0.
The TAMMBER code (available at github. com/tomswinburne/tammber) employs a variabletemperature accelerated dynamics (TAD) method  [32] , accumulating sampling information in parallel and producing (τ i , {τ ji , k ji }) over a range of possible T H down to the lowest application temperature T L. The acceleration temperature Details on how sampling data at variable T H is collated can be found in Ref. [10]. The current implementation calculates rates using harmonic TST, though future work will relax this assumption using the PAFI method  [7] k u i = ∞ k o i kπ(k|τ i , {τ ji , k ji })dk -k o i , (1) as illustrated in Figure 1  [10]. Estimators for k u i have been proposed in pioneering earlier work to address this issue  [17] [18] [19]. However, the Bayesian approach described here has the key property of monotonic decay with computational effort, even under the discovery of new transitions.
The chosen observable is the 'committor' probability C A B , which gives the probability of a trajectory reaching A before returning to B, a key quantity when calculating reaction rates  [8]. For large networks at low temperature, observables such as C A B can be so small that typical linear algebra routines fail due to floating point error. This has motivated the development of specialized renormalization methods  [44] , specifically designed to allow the exact evaluation of branching probabilities and mean first passage times. The underlying algorithms have recently been implemented, along with a variety of related methods in a Python package (PyGT module, pygt. readthedocs. io). These techniques allow the production of a smaller network involving only the particular states of a given pathway, with renormalized transition rates such that branching probabilities are exact.
In addition to making the uncontrolled assumption that thermal vibrations are harmonic oscilations, unlikely to hold at elevated temperature, the diagonalization scales cubically with the number of atoms in the system. This complicates application to the large systems routinely found when investigating linear or areal defects in materials  [46] , motivating the development of linear-scaling methods  [47, 48]  to evaluate harmonic TST prefactors by approximation of Hessian spectral densities. The PAFI code is a linear-scaling method to evaluate ∆F with no assumption on the nature of thermal vibrations. Starting from a converged NEB calculation, constrained sampling is performed on hyperplanes perpendicular to the MEP  [10] , as shown in figure  4. This constraint is available as part of the open-source LAMMPS package  [49] , designed to be used with the PAFI code (available at github.
The following list of five representative examples describes the range of active stations and how they vary substantially in terms of research objectives and instrumentation. This list is obviously incomplete. MÜGGELSEE MONITORING STATION -IGB BERLIN: This automated research and monitoring station (www. igb-berlin. de/en/muggelsee-monitoring-station) has operated since the 1970s on the small, shallow Müggelsee (Germany). It collects meteorological, physical and biological (plankton) data in real time in order to investigate the ecological impacts of climate and environmental changes on aquatic communities and biodiversity. This station serves both national and international observational programs  (Schmidt et al. , 2018). AQUAPROBE -EAWAG: This automated lake monitoring station (www. aquascope.
ch/) on Greifensee (Switzerland) provides continuous (24 h), high-frequency measurements on phytoplankton and zooplankton at 3 m depth along with environmental monitoring data (multiparameter profiles, weather station, and automated nutrient sampling). Data streamed in real-time are used to investigate plankton biodiversity and dynamics. Plankton monitoring consists of scanning flow cytometry (www. cytobuoy. com) and observations by the newly developed Aquascope, an underwater high-resolution, dual-magnification imaging microscope  (Orenstein et al. , 2020; Pomati et al. , 2011). ERKEN LABORATORY -UPPSALA UNIVERSITY: This program has a long history of monitoring physical, chemical and biological conditions on Lake Erken and more recently, on Lake Mälaren and surrounding rivers. Monitoring has been performed manually since 1940 and with automated high-frequency sensor technology since 1988.
Financial resources were provided by the five LéXPLORE partner institutions, (a) the Swiss Federal Institute of Technology, Lausanne (EPFL; www. epfl. ch/research/domains/limnc/), (b) the Swiss Federal Institute of Aquatic Science and Technology (www. eawag. ch/), (c) the University of Geneva (www. unige. ch/forel/en/), (d) the University of Lausanne (www. unil. ch/idyst/), and (e) CARRTEL INRAE-USMB (www6. lyon-grenoble. inrae. fr/carrtel/). Box 2 summarizes LéXPLORE's technical specifications. Besides the physical platform on the lake, data management is a crucial component of LéXPLORE (Box 3). There are two types of data: (a) monitoring data (core dataset), which are freely available for download at www. datalakeseawag. ch and (b) project-specific data curated by project leaders. The monitoring data include continuous meteorological parameters and currents, as well as temperature, oxygen and photosynthetic-active-radiation (PAR) and multiparameter profiles (Appendix S1; www.
lexplore. info/available-dataset/). Ongoing projects use project-specific equipment and instrumentation, some of which are listed in the Appendix S1. We encourage research teams to make all data publicly available when projects end, although final decisions and responsibility for data access lie with project leaders and funding agencies. Since the beginning of operations (February 2019), 30 projects have applied to use LéXPLORE, which are listed in Appendix S2. In addition to being technically and scientifically feasible, all projects must comply with LéXPLORE operational and safety policies (www. lexplore. info/current-projects/).
BOX 3 DATALAKES-A data management platform for lakes The LéXPLORE platform with its various monitoring and scientific instruments generates vast quantities of data. Efficient access to and utilization of LéXPLORE data requires dedicated curation. The web-based open access data platform Datalakes (www. datalakes-eawag. ch/) is designed for these needs. Datalakes serves as a sensor-to-frontend platform for data on any Swiss lake. The software performs continuous acquisition, storage, curation, indexing, patching, visualization, and extraction of lake-related environmental data and products. Datalakes offers a modern, user-friendly online interface with easy-to-use visualization and extraction functions. The platform also seeks to maximize reproducibility and data integrity. Access does not require registration and provides raw data (labeled as Level 0) up to commonly used products integrating information from multiple sensors.
As an example, the interface categorizes surface net heat flux (www. datalakes-eawag. ch/datadetail/452) as Level 2 data. Both the web portal and GIT repository offer access to processing scripts with the intention that the scientific community will adapt, update, and optimize them. To avoid ill-defined reproducibility, changes in processing scripts and methods, Datalakes tracks workflow and performs version control using a Renku platform (www. datascience. ch/renku/). Finally, Datalakes is not confined to LéXPLORE data but rather combines different sources of information. This promotes data visualization and extraction of one-dimensional and threedimensional hydrodynamic models using remote sensing data from Meteolakes (www. meteolakes. ch/; Gaudard et al. , 2019; Baracchini et al. , 2020).
;  Lynch et al. , 2015; Straile et al. , 2007)  and Arctic charr (Salvelinus alpinus;  Caudron et al. , 2014). However, these species now face additional threats from increasing temperatures, modified riverine flows and changing wind patterns during critical development phases  (Kelly et al. , 2020; Mari et al. , 2021; Nõges et al. , 2018). Core datasets (Box 3; Appendix S1) and plankton studies (Section 3. 5) from LéXPLORE will support investigations on the temporal coupling between the emergence of fish larvae and the phenology of plankton, or the risk of so-called match-mismatch phenomena  (Cushing, 1990). This synchronism plays a key role in fish recruitment and therefore the size of the fishery. Recruitment depends strongly on short-term weather phenomena such as sudden and pronounced upwelling events  (Baracchini et al.
, 2020). For example, spring storms can lower the surface temperature by more than 10 C in less than 24 h. This can kill perch larvae (Perca fluviatilis) which lack the ability to take refuge in warmer zones. High-frequency water column data will help constrain key parameters and impacts of these events. Toxic algal blooms  (Platt et al. , 2003) , microplastics  (Parker et al. , 2021) , and new organic pollutants  (Corsolini et al. , 2005)  all impact fish communities as well. In addition to addressing these, the LéXPLORE platform can provide samples for genomic studies such as analysis of e-DNA samples  (Sales et al. , 2021)  from different depths or remote hydroacoustic surveys using broadband sounders (Benoit-Bird & Waluk, 2020; Appendix S1). These datasets can help natural resource specialists and scientists manage fish communities and invasive species by assaying larval emergence phenology or species diversity, distribution in the water column, and schooling behavior as a function of biotic or abiotic dynamics.
ACKNOWLEDGMENT We appreciate the contributions by  Mridul K. Thomas  ( U of Geneva ),  Bieito Fern andez Castro (U of Southampton) ,  Daniel Odermatt  (Eawag) and especially by  Camille Minaudo  ( EPFL ) for providing Figure 3. The build-up of the scientific-technical instrumentation would have not been possible without the work of  Aurélien Ballu ,  Guillaume Cunillera ,  Christian Dinkel ,  Roxane Fillion ,  Michael Plüss ,  Philippe Quétin ,  James Runnalls , and  Viet Tran-Khac. The technical team received support by  Hannah Chmiel ,  Fabio dos Santos Correia ,  Nicolas Escoffier , and  Pascal Perolo. The EPFL teams of the technical platform PLTE of ENAC and of the DSPS provided guidance in technical and safety matters. Finally, a big thank to  Andreas Kindlimann  ( Naval Architecture GmbH ) for the design and realization of LéXPLORE, and to  Barbara Tirone  for her amazing assistance during the construction phase.
Some recent examples of sensor-based technologies that track mental health include phone apps, such as Northwestern University's Center for Behavioral Intervention Technologies' Purple  [24, 25]  and Intellicare  [26, 27]  platforms, Harvard University's Beiwe platform  [28, 29] , and apps such as mPower  [30, 31]  built on top of Apple's ResearchKit for iOS  [32]  and ResearchStack for Android  [33]. The Beth Israel Deaconess Medical Center maintains an extensive database of apps that claim to respond to mental health needs  [34]  and provides a protocol for their evaluation  [35, 36]. There is an unmet need for a free, open, configurable platform to translate pencil-and-paper assessment instruments, cognitive tasks, and therapies into attractive, engaging, and effective digital, mobile, and remote tools that exceed current standards of privacy, security, and accessibility.
Criteria for Reviewing Customizable, Mobile, and Experience Sampling Products In this review, we did not consider mobile apps limited to specific assessments, cognitive tasks, or therapies, but rather examined platforms that enable the creation and distribution of such apps. We wanted to find products that have an administrator interface for creating and scheduling recurring, customized questionnaires, where users receive and respond to scheduled notifications on a mobile device. The detailed outline of the protocol we followed has been provided in Multimedia Appendix 1, which is briefly summarized in the paper. Over the last 3 years, clinical and research collaborators and colleagues have helped us gather information about products with desired characteristics. To extend this search, we conducted 3 queries in Google's search engine (without quotation marks or Boolean operators):  (1)  digital electronic data capture systems, to broadly identify any electronic tools for capturing data; (2) mobile phone software sensor data collection, to identify mobile data collection software that may involve sensors; and (3) alternative to Qualtrics, to identify alternatives to one of the most prominent products that enables web-based customization of surveys (although Qualtrics itself does not currently have a mobile app).