dataesr/scientific-paragraphs-categorization huggingface dataset:
For a given mean input time series μ[t 0 , t] we calculate r(t|μ[t 0 , t],θ) using the initial condition μ f (t 0 ) = μ(t 0 ). Likelihood maximization. We maximized the logarithm of the likelihood (loglikelihood), argmax θ log pðDjθÞ ¼ argmax θ X KÀ1 k¼1 log pðt kþ1 jt k ; μ½t k ; t kþ1 ; θÞ ð22Þ for individual neurons, using Eq. (  1 ), and similarly for networks using the logarithm of Eq. (  4 ). Optimization was performed using a simplex algorithm  99  as implemented in the Scipy package for Python. It should be noted that our method is not restricted to this algorithm; alternative, gradient-based optimization techniques, for example, may likely lead to reduced estimation times. We would further like to remark that maximizing the likelihood p(D|θ) within plausible limits for the parameter values is equivalent to maximizing the posterior probability density for the parameters given the data, p(θ|D), without prior knowledge about the parameters except for the limits (i.
66. In the model-free, CCG-based method a connection strength was estimated by the z-score of the extremum in the spike train CCG across positive lags for each pair of neurons. Note that the lag denotes the time since a presynaptic spike. z-scores were obtained using estimates from surrogate data generated by perturbing the presynaptic spike times by a temporal jitter (random values between -10 ms and 10 ms) in a large number of realizations. For each of the three methods detailed in this section we assessed detection performance in the following way. A discrimination threshold J thresh for the presence or absence of connections was applied to estimated coupling strengths Ĵi;j. Accordingly, the presence of a connection (i,j-pair) was assured by the condition j Ĵi;j j > J thresh. The true positive rate (sensitivity) was given by the number TP of connections for which the estimation satisfied j Ĵi;j j > J thresh and a true connection was present (|J i,j | > 0), divided by the number P of true connections.
(  32 ). Methods 1a and 2 are less well suited for this scenario because the adaptation variable can accumulate to substantial values, thereby opposing the assumption of weak variations of the mean input; moreover, the spike train of an adapting neuron deviates strongly from a Poisson process. Implementation and computational complexity. We have implemented our methods for parameter estimation (1, 1a, and 2) using the Python programming language and applying the libraries Scipy 101 for optimization and Numba 102 for low-level machine acceleration. The code for representative estimation examples from Results sections "Inference of background inputs" to "Inference of neuronal adaptation" is available at GitHub: https://github. com/neuromethods/inferencefor-integrate-and-fire-models. Computation times for example inference problems are summarized in Supplementary Table  1.
50. Using only the presynaptic and postsynaptic spike times, we fitted an I&F neuron where input perturbations were described using delta pulses or alpha functions (cf. Methods section "Modeling input perturbations"). For the former model we applied method 1a, for the latter method 2. Similarly to ref. 50  we defined detection time as the minimal total length of spike train data for which the model with input perturbations (J ≠ 0) yields a larger likelihood on test data compared to the respective model without input perturbations (J = 0); this was indicated by a positive log-likelihood ratio on test data from 10-fold cross-validation. In addition, we assessed detection performance on a fixed amount of data that consisted of five consecutive segments (i. e. , 200 s recording duration), where adjacent five-segment blocks shared one segment.
Coupling strength z-scores were computed using estimates from surrogate data generated by perturbing the presynaptic spike times by a temporal jitter (cf. Methods section "Network model and inference details") in a large number of realizations. In vivo ground truth data on synaptic connections. We used combined juxtacellular-extracellular recordings of neuronal ensembles from the hippocampal CA1 region in awake mice (for details see ref. 51  ). Neurons were separated into PYRs and INTs according to their spike waveform and spiking statistics. Spikes were evoked in single PYRs by short current pulses (50-100 ms) applied at intervals of variable length using juxtacellular electrodes while recording extracellular spikes of local INTs. PYR spikes which occurred during a stimulus were considered as evoked, and those which occurred at all other times were considered as spontaneous.
Acknowledgements We thank  Dimitra Maoutsa  for her support on the GLM implementation. This work was supported by  Deutsche Forschungsgemeinschaft  in the framework of Collaborative Research Center 910, the  Programme Emergences of the City of Paris ,  ANR  project  MORSE  ( ANR-16-CE37-0016 ), and the program " Ecoles Universitaires de Recherche " launched by the  French Government  and implemented by the  ANR , with the reference  ANR-17-EURE-0017. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper.
These statistical results substantiate a report by  Shiokawa et al. (2008)  who studied one event of northeast propagating MSTIDs during nighttime. MSTID motion seen in optical data during this event were related to the descent of the ionospheric F region identified by a concurrently operating ionosonde. HOK observed some irregularly-propagating GS progressions that could have been identified as MSTIDs, but details were not investigated. Oinats et al. (2015)  indicate that the majority of nighttime MSTIDs occur during late evening to pre-midnight hours (16-24 LT) and preferentially at equinox (see their Fig. 2 ). This finding is in disagreement with  Duly et al. (2013). They found propagation to the southwest (az = 210°), similar to daytime MSTIDs over Japan, and pre-midnight MSTIDs occurrence consistent with the wind filtering effect (Fig.
These mixed findings question the importance of auroral zone sources for generating nighttime mid-latitude MSTIDs, similar to what has been found on the dayside. Finally, it is noted that  Ichihara et al. (2013)  found preferential occurrence of northeast propagating MSTIDs near dusk (see their Fig. 4 ) suggesting the solar terminator may also play an important role (e. g. ,  Afraimovich et al. 2009). This hypothesis, however, has not been investigated with SuperDARN data yet.
Frissell et al. (2011)  presented observations of an individual Pi2 pulsation using data from BKS in a special high-time resolution mode which was implemented to support the Time History of Events and Macroscale Interactions during Substorms (THEMIS) mission. In this mode, each radar operated a scan with 3-s integration times along each beam, but with a special beam, the camping beam, being sampled after every other beam. Thus, the full scan would take 2 min while there was a 6-s time resolution on the camping beam which in this case for BKS was beam 7. The BKS observations of the Pi2 wave were between 54 o and 58 o N MLAT, close to the latitude of the Remus magnetometer station which observed temporal variations similar to those in the Doppler velocity of the ionospheric scatter detected by the radar.
TIG observed frequencies inside the plasmapause (14 and 28 mHz) which are different from those outside of it (14 mHz only), while inside the plasmapause HOK only observed the lower spectral frequency and UNW measured the lower spectral frequency at lower latitudes and higher spectral frequency further poleward. The estimations of Poynting flux at THEMIS indicate that the waves were propagating earthward and duskward. The authors concluded that the observed wave was a cavity mode resonance which had been stimulated by compressional waves which had propagated duskward from the source region in the midnight sector. 2017 ) provided a careful and thorough analysis of multiple data sets from THEMIS, ground magnetometer, and FHE, FHW, and BKS. Shi et al. (2017)  attempted here to separate a plasmaspheric virtual resonance (PVR) where wave energy is primarily confined to the plasmasphere but a small portion escapes beyond the plasmapause, from a plasmaspheric cavity resonance (PCR) where wave energy should be confined to the plasmasphere.
Magnetometer data at Ottawa revealed simultaneous magnetic pulsations with the same period (Fig. 35  from  Greenwald et al. 2008). The top panel of Fig. 35  is a contour plot of the spectral power from a wavelet analysis of the Ottawa X (geographic northsouth) component data which shows well-defined peaks at 20 s at 0605 UT and between 25 and 30 s over 0608-0609 UT. The middle panel shows the high time resolution SuperDARN data which were available only for parts of the analyzed interval due to the operational mode. The most important segment lies between 0608 and 0609 UT, and it is zoomed on in the bottom panel. During this segment, two oscillation cycles with periods of 13-14 s were also seen by the Ottawa magnetometers, although in magnetic records the main spectral a b c Fig. 35  A comparison of magnetometer data from Ottawa with WAL LOS velocity.
Importantly, the authors reported that 46% of the observed waves had no ground magnetic signature. The methodology presented in this paper was then used as the primary analysis method in three subsequent papers using mid-latitude SuperDARN radars, two of which look at Pc3-4 waves and one of which looks at Pc5 waves, which are discussed below. Ponomarenko et al. (2005)  investigated Pc3-4 waves in sea scatter from TIG. These data were complemented by ground magnetometer data from Macquarie Island. The waves were observed between 8 and 12 MLT, and the authors concluded that the observed waves were driven by the interaction of upstream waves with the magnetosphere. Such waves are believed to be generated at the Earth's foreshock by the ion-cyclotron instability in reflected proton beams. These observations suggest that the Pc3/4 energy propagates into and through the magnetosphere in the isotropic fast mode and couples to field-line guided mode Alfven waves at harmonics of the local resonant frequency.
If these features are interpreted in terms of a poloidal FLR, as done by the authors, then the values of the plasma density at the equatorial plane required to produce the 1. 6 mHz eigen oscillation would be an order of magnitude larger than those normally observed at these MLAT. Furthermore, the characteristic "kink" in the latitudinal phase progression is effectively absent from the data. In order to explain these discrepancies, the authors invoked (1) a Fig. 37  A histogram of significant frequencies in the TIG data set. A three-point smoothing is applied to the data and is shown by the curve with the slightly darker black line superimposed on the histogram. The vertical lines identify peaks of the smoothed curve showing certain frequencies at 1. 3, 1. 6, 2. 1, 2. 9, and 3. 3 mHz which appear to have higher spectral power than the neighboring frequencies.
MHD waves: Future work It is clear from the work that has been done on MHD waves with the mid-latitude SuperDARN radars that there is significant potential for further study. There are several future directions, both with existing data sets and radars, as well as potential new developments of radar technology, as well as links to current and future space missions. Two areas where the current data sets and radars could be used are identified before moving to new developments. The work that has been currently done on Pi2 pulsations has yet to result in any firm consensus regarding the modes that are present at mid-latitudes. This situation perhaps is due to both the nature of the waves which are relatively short lived and occur during the nighttime when the presence of ground scatter is perhaps limited.
Also, it is feasible that calibrated elevation angle data could be inverted to provide ionospheric plasma density suitable for interhemispheric comparison. 4. Ionosphere-neutral interactions: The contributions of mid-latitude SuperDARN to the study of phenomena associated with ion-neutral interactions have been predominantly through observations TIDs (large scale and medium scale). With mid-latitude SuperDARN, it is possible to study the characteristics of TIDs in the sub-auroral and mid-latitude regions as a function of latitude, LT, and geomagnetic activity, and several important characteristics have been clarified (e. g. , the relationship to the intensity of the polar vortex). It has been demonstrated that the potential scope for study of ion-neutral coupling extends to the ionospheric disturbances that are due to big earthquakes, tsunamis, volcanic eruptions, and tornadoes and to the time scales of flywheel effects and disturbance dynamo effects.
Technical developments There are several prospects for further technical development in terms of (1) hardware, (2) software, and (3) algorithms. 1. Hardware: The BKP, which started operation in 2014, is the first fully digital SuperDARN radar and is much more sensitive than the pre-existing radars and much less susceptible to instrumental noise. The same digital radar system developed for BKP is now also running at the FIR site. Similarly, the University of Alaska, Fairbanks group has developed an "imaging" capability for the SuperDARN radar at KOD  (Parris and Bristow 2009). Likewise, University of Saskatchewan engineers are currently working on designing a digital "imaging" radar which would replace consecutive sampling of the radar beams by simultaneous multi-directional measurements. This "imaging" will be achieved through illuminating the whole radar's FOV by widening the main antenna lobe during transmission and recording raw data at each antenna so that the beam forming and data analysis are performed afterwards in software.
The five directives comprising the DRIVE initiative are (1) diversify observing platforms with microsatellites and mid-scale ground-based assets, (2) realize scientific potential by sufficiently funding operations and data analysis, (3) integrate observing platforms and strengthen ties between agency disciplines, (4) venture forward with science centers and instrument and technology development, and (5) educate, empower, and inspire the next generation of space researchers. Much of the success of SuperDARN, to date, can be attributed to the fact that it was founded on the very same collaborative principles that underpin these five directives and, as such, it is uniquely positioned to play a key role in the development of the DRIVE initiative as it moves forward. In Japan, the government has selected master plan projects and drawn up a roadmap of scientific research every few years, in order to identify future science directions.
Acknowledgements This review paper originated from approximately one-week long workshops in January 2017 and January 2018, during which we discussed the content of the paper, its structure and future directions of the mid-latitude Super-DARN network. These workshops were carried out by the  joint research program  of the  Institute for Space-Earth Environmental Research, Nagoya University , and supported by funding agencies described in the 'funding' section. Finally, we would like to thank everyone who contributed to the completion of the review paper.
Rivers are the fluvial conveyor belts routing sediment across the landscape. While there are proper techniques for continuous estimates of the flux of suspended solids, constraining bedload flux is much more challenging, typically involving extensive measurement infrastructure or labor-intensive manual measurements. Seismometers are potentially valuable alternatives to in-stream devices, delivering continuous data with high temporal resolution on the average behavior of a reach. Two models exist to predict the seismic spectra generated by river turbulence and bedload flux. However, these models require estimating a large number of parameters and the spectra usually overlap significantly, which hinders straightforward inversion. We provide three functions contained in the R package "eseis" that allow generic modeling of hydraulic and bedload transport dynamics from seismic data using these models.
Computational Environment The R package "eseis"  (Dietze, 2018)  is a free and open source toolbox for handling the work flow of generic environmental seismology. With the latest developer version (0. 5. 0), it contains models to predict the seismic spectrum due to turbulent channel flow  (Gimbert et al. , 2014)  and impacting bedload particles  (Tsai et al. , 2012). Both models can be explored in an interactive graphical user interface (GUI; Figure  2 ). Three additional functions, denoted by the prefix fmi, are devoted to the approach of fluvial model inversion presented in this study. Data preparation, processing, analysis, and visualization steps were performed with R v. 3. 5. 3 (RCoreTeam, 2015). The R functions, data sets, and utilized scripts as commented markdown files are available in the supporting information (https://osf.
Data Processing Flood water depth and bedload flux time series were recorded at 1-min resolution. The bedload flux time series starts when at least 4 kg of sediment has been collected in the slot samplers during an event, which represents the sensitivity threshold of the sensors. We used the median of the values measured by the three bedload samplers to generate a representative bedload flux per unit stream width. The recorded seismic files were converted to hourly SAC files  (IRIS, 2017)  and organized in the consistent structure as used by the functions of the eseis package. For the relevant part of the flood (05:40 to 11:00 UTC; cf. Figure  1b ) we calculated a spectrogram from the vertical component of the seismic time series using the method of  Welch (1967)  with 10-s-long, nonoverlapping windows, created by averaging 5-s-long and 80% overlapping subwindows (Figure  1c ).
Several parameters describe the seismic ground characteristics due to the site properties. This set of parameters (material quality factor q 0 and its increase with frequency e 0 , Rayleigh wave phase velocity at the reference frequency v 0 and its variation coefficient p 0 , and the Greens function  displacement amplitude coefficients n 0 ) can be constrained by performing an active seismic survey (e. g. ,  Foti et al. , 2018). However, when that is not possible (as is the case in this study), they must be estimated. In a first step we make use of the interactive GUI provided with the R package eseis (Figure  2 ). This application allows changing all relevant model parameters and instantaneously visualizes the updated model outputs, together with an optionally provided empirical spectrum.
The quality of the match was subsequently quantified and optimized by minimizing the root-mean-square error. From this set of combinations optimized to first order we started changing the seismic parameters toward lower and higher values, respectively, until the match of empirical and model spectra obviously diverged. We defined these parameter ranges as the limits for the subsequent step of parameter range optimization. In a second step we performed the inversion of the example flood data set in an extended Monte Carlo experiment. Since the lower and upper Greens function parameters n 0 did not have significant impact on the model spectra shape when changing them between 0. 4 and 0. 8 and 0. 5 and 0. 9, respectively, we set them arbitrarily to 0. 5 and 0. 8. We created 10 5 random parameter combinations of the most sensitive parameters (q 0 , v 0 , p 0 , and e 0 ) and the target parameters (h w and q s ), exploring the range of the former set of parameters to identify the most likely values throughout the event (i.
Model Validation In order to infer the ability of the model approach to estimate water depth and bedload flux, we created several synthetic data sets, inverted them, and compared the resulting model time series of the target parameters to the input data (Figure  3 ). All except the varied parameters are the same as denoted in Table  1. Synthetic Data Set 1 imposes a constant water depth of 0. 5 m. The bedload is injected after 2 hr of the modeled time period, resulting in an instantaneous rise to 5 kg/sm, which is held constant for another 2 hr Model quality is assessed by the absolute difference between synthetic spectra and the best fit modeled reference spectra. This error can be studied both in time and frequency space. Another measure of model quality is the error (residual) between water depth or bedload flux and the respective model estimates.
Characteristics of the Flood The flood hydrograph (data available under https://osf. io/5uzpw/) shows a rapid rise of water depth although the actual onset of the event is not shown here because we define the event by the onset of the bedload sampler records, that is, at 05. 40 UTC. After the flood's double peak occurred (0. 84 and 0. 83 m), water depth dropped logarithmically for at least 13 hr (Figure  1b ). The three bedload samplers monitored a maximum average value of 4. 29 kg/sm. The highest bedload fluxes were recorded within the first 2 min. Thereafter, values declined progressively to almost zero around 05:55 UTC, when two further smaller bedload waves (peak flux 1. 08 kg/sm) emerged for 30 min. Bedload transport ceased at 07:10 UTC. With the onset of the flood, the seismic spectrogram shows a broadband (10-90 Hz) increase in seismic power up to -100 dB, which progressively grades into the background for about 1 hr.
Model Validation With Synthetic Flood Time Series The ability of the model to reconstruct the synthetic time series of target parameters (which were used to generate noise-free spectrograms that were inverted) provides the accuracy baseline for the actual inversion of the empirical data set. Synthetic Data Set 1 (Figure  3a ) yielded absolute differences between best fit model and input spectrogram of less than 0. 5 dB and target parameter errors of 0. 02 ± 0. 04 m (water depth) and -0. 03 ± 0. 06 kg/sm (bedload flux). The modeled time series resemble the onset of changes and are only slightly affected by changes in the corresponding parameter. Synthetic Data Set 2 (Figure  3b ) has only minor spectral differences (less than 0. 26 dB) and model errors (0. 02 ± 0. 04 m and -0. 06 ± 0. 13 kg/sm, respectively).
The concurrent changes in water depth and bedload flux are captured well. However, during the second half of the synthetic event the model produced increasingly larger deviations. Synthetic Data Set 3 (Figure  3c ) has the largest spectral differences (up to 1. 75 dB) but yielded the smallest target parameter errors (-0. 01 ± 0. 03 m and -0. 001 ± 0. 03 kg/sm, respectively). These errors mainly appear toward the end of the synthetic data set, when the continuously declining water depth curve is represented by stepwise model results. The synthetic data set produced by the real world time series of water depth and bedload flux (Figure  1b ) produced spectral differences of up to 0. 47 dB and target parameter errors for water depth and bedload flux of -0. 04 ± 0. 03 m and -0. 001 ± 0. 02 kg/sm, respectively.
We interpret these pulses as the effects of persons working at the observatory for data collection and station maintenance reasons. These activities included documented walking and operating at close proximity to the seismic sensor and a car idling at the bank. A further set of seismic signals, the temporally constant, narrowband horizontal lines in the spectrogram (Figure  1c ) around 22 and 44, 30 and 60 Hz, are interpreted as the signature of measurement devices operating at the observatory. When excluding this period of signal contamination, the temporal variation of the seismic signal-derived bedload flux shows three important components of the average-channel bedload flux: (i) a first very large wave in bedload flux up to 5 kg/sm, which drastically recedes within minutes of the arrival of the flood bore, (ii) a second multiple-rise peaking at about 1 kg/sm, and (iii) and a third, smaller rise (0.