dataesr/scientific-paragraphs-categorization huggingface dataset:
Indirect Comparison. The latter algorithms are either GPU-based (MD+18 and AA+19), or designed for a cluster of multicores (PS18). This makes a direct comparison challenging (furthermore, the software is not available). To obtain a meaningful comparison, we devised a cost-based approach that is detailed in Section VI-B, which takes into account the cost of the machines used and the time needed by that machines to conclude an experiment, using the results and dataset reported in the corresponding papers  [2] ,  [22] ,  [25]. Replicability: Source Code and Computing Platform. The computing platform is a machine provided by the University of Pisa with Intel(R) Xeon(R) CPU E5-2620 v3 at 2. 40GHz, 24 virtual cores, 128 Gb RAM, running Ubuntu Linux version 4. 4. 0-22-generic. The program is written in C++11, compiled with gcc-8.
1) Performance Evaluation on a Single Core In this section, we compare the time needed by FMT-dec with respect to the one used by SL+17, KM17, WG+18, and D18, using just one core in order to test the effectiveness of the underlying algorithms independently from their parallel aspects. For this reason, we ran the competitors on a restricted number of networks, which are the smaller networks in our dataset. We report in the right part of Table  2  (decomposition) our results. This table shows that KM17 is faster in 3 small graphs, all of them having less than 17 millions of edges. On the other hand, SL+17, D18 and WG+18 are never faster than the others. SL+17 clearly outperforms KM17 on enwiki-2013, but it runs out of memory when dealing with bigger ones, like arabic-2005. In this scenario, FMT-dec outperforms the competitors on the four biggest graphs, being the fastest for all the graphs having more than 20 million edges.
2) Dealing with Large Scale Networks using Multiple Cores In the right part of Table  3  (having headline decomposition), we report the experimental results to compute the truss decomposition for all the networks in our dataset using 24 parallel threads. Looking at the results, we can see that FMT-dec is always faster than the competitors on bigger graphs. In the case of gsh-2015-host for instance, FMT is more than eight times faster than KM17, which was the only competitor able to conclude the computation on this graph. Concerning the other methods, it is worth mentioning that WG+18 is not parallel and, hence, it uses just one core. Nonetheless, we also tried to run WG+18 on bigger graphs, but, due to the lack of parallelism, it ran out of the allotted 30 000 seconds. Also, D18 ran out of time for bigger graphs.
On the other hand, SL+17 appears to be fast on some medium-sized graphs, like on g500-sc23-ef16 and g500-sc25-ef16, but its higher memory usage prohibits its application on larger networks on our workstation. Indeed, it should be noted that the original paper  [28]  was able to run SL+17 on large networks due to the larger main memory available (384GB against the 128GB of our machine). Among the competitors of FMTdec, KM17 seems to be faster than the others on the majority of the graphs, even though there seems to be variability in the results, as for some graphs it is heavily outperformed by SL+17. Indeed, KM17 is the only competitor of FMT-dec able to process some of the largest graphs in our dataset. This is due to its optimized usage of the main memory, which is shown for the sake of completeness in Table  4  for the biggest graphs.
Regarding the datasets, we selected the graphs which were used by at least two algorithms among PS18, MD+18 and AA+19. We illustrate the hourly costs in Table  5 , and the millions of edges processed per USD cent in Table  6. As it can be observed, the performance of FMT is orders of magnitude higher than the other considered algorithms, except for Amazon0505 and cit-Patents, where AA+19 excels. We observe that the latter cases correspond to graphs with the lowest trussness values: Amazon0505 has trussness 11 and cit-Patents 36, while all the other networks have trussness values over 500. For the sake of completeness, we plotted in Figure  4  the performance per USD against the value of t G in the considered datasets: we can see that the performance per cost of FMT-dec and FMT-max remains consistent on all considered datasets, while the performance per cost of AA+19 gets significantly worse as t G increases.
The rationale behind this behaviour is that approaches based on matrix multiplication (such a GPU-based ones) re-compute the support of all edges in the graph simultaneously, exploiting the mass-parallelism given by the GPUs, while our combinatorial approach only processes information relative to the edges that are iteratively removed. Moreover, our CPUbased algorithm relies on hardware that is up to one order of magnitude cheaper than the hardware required by other existing algorithms (Table  5 ), leading to a more cost-effective computation. It follows that approaches such as AA+19 obtain good performance per cost on graphs with only small k-trusses (as fewer multiplications are performed), their performance degrades when denser k-trusses start to appear; on the other hand, the performance of FMT-dec and FMT-max remains consistent on all considered datasets, outperforming all the other considered algorithms by orders of magnitude as t G increases.
Abstract. The North Atlantic is one of the major ocean sinks for natural and anthropogenic atmospheric CO 2. Given the variability of the circulation, convective processes or warming-cooling recognized in the high latitudes in this region, a better understanding of the CO 2 sink temporal variability and associated acidification needs a close inspection of seasonal, interannual to multidecadal observations. In this study, we investigate the evolution of CO 2 uptake and ocean acidification in the North Atlantic Subpolar Gyre (50-64 • N) using repeated observations collected over the last 3 decades in the framework of the long-term monitoring program SURATLANT (SURveillance de l'ATLANTique). Over the full period  pH decreases (-0. 0017 yr -1 ) and fugacity of CO 2 (f CO 2 ) increases (+1. 70 µatm yr -1 ).
Data collection and measurements This study is based on observations collected in the framework of the long-term monitoring program SURATLANT (SURveillance de l'ATLANTique) initiated in 1993. The main objective of this program is to monitor hydrological and biogeochemical properties in surface waters of the NASPG, notably sea surface salinity (SSS), to improve the understanding of the role of salinity in the variability and predictability of climate as well as the water cycle  (Reverdin et al. , 2018a; Holliday et al. , 2020). To this end, two to four cruises per year are conducted between Reykjavík (Iceland) and Newfoundland (Canada) on board merchant ships. Seawater samples are collected every 3 to 4 h from a pumping system (at approximately 5 m deep) in order to measure salinity, as well as alkalinity (measured since 2001), DIC, silicate, nitrate and phosphate concentrations.
The accuracy is on the order of ±3 µmol kg -1 for both DIC and TA based on Certified Reference Material (CRM) analysis and occasional intercomparisons as explained by  Reverdin et al. (2018b). Samples for the analysis of nutrient concentrations are frozen just after sampling. Spring and summer samples are filtered before analysis. The measurements are carried out by the MFRI team in Reykjavík according to the standard colorimetric method described by  Ólafsson et al. (2010)  with an accuracy of ±0. 2 µmol L -1 for nitrate and silicate and ±0. 03 µmol L -1 for phosphate. Details of the sampling and analyses for all properties are provided in  Reverdin et al. (2018b). The SURATLANT dataset is freely available and is accessible at http://www. seanoe. org/data/ 00434/54517/ (last access: 11 March 2019).
Calculations of the carbonate system parameters and contributions Carbonate system parameters such as pH, the fugacity of CO 2 (f CO 2 ) and the saturation states for the calcium carbonate minerals calcite ( Ca ) and aragonite ( Ar ) are calculated using SST, SSS, TA, DIC, silicate and phosphate data described by  Reverdin et al. (2018b). For the last two, we used monthly climatological values derived from the SURAT-LANT data over the period 1993-2017 because nutrient interannual variability does not have a significant impact on the carbonate system parameter calculations (and because nutrients were missing for some cruises). The calculation program used is CO 2 SYS originally developed by  Lewis et al. (1998)  available in  MATLAB (van Heuven et al. , 2011 ) that now includes error propagation  (Orr et al.
The trend analysis and contribution calculations (Eq. 2) were first performed for each box but results of trends and drivers for boxes C, D and E were found to be quasi-identical. Therefore, we present and discuss the results by combining the data collected north of 54 • N (i. e. , boxes C, D and E). The trends in box B are evaluated separately because properties and their trends (SST, SSS, DIC, TA) differ significantly from the other three boxes. We separate seasons, and the trends for winter (January to March) and summer (June to August) are analyzed separately as they correspond to seasonal extrema of surface carbonate properties (described below in Sect. 3. 1) and these seasons were more regularly sampled along the SURATLANT line (Table  1 ). Finally, we choose February and July as a reference for winter and summer, respectively.
When February or July data were missing for a specific year, the data collected in January or March (for winter) and in June or August (for summer) were used after correcting the observed seasonal anomalies from the climatological cycles described in Sect. 3. 1. We tested this reconstruction for February and July data using SST and SSS data based on the binned products from  Reverdin et al. (2018a)  and averaging the bins in the same boxes (B and CDE) used for discrete sampling (Fig. S1  and Table  S1  in the Supplement). As expected, the differences for SST and SSS are most pronounced for summer and box B (Fig. S1a, c ) due to higher variability observed during this season, both in time and at the small scale. For winter the reconstruction based on discrete sampling appears more robust when compared to the binned products, especially for box CDE (Fig.
Long-term trend and anthropogenic CO 2 When describing the SURATLANT dataset for 1993-2017,  Reverdin et al. (2018b)  evaluated the long-term trend of properties over a broad region (50-63 • N). Using data over 24 years (and all seasons) they estimate an increase in DIC of +0. 77 (±0. 11) µmol kg -1 yr -1 , a f CO 2 trend of +1. 95 (±0. 12) µatm yr -1 and a pH decrease of -0. 0021      2  (first lines). The DIC increasing rate ranges between +0. 5 and +0. 9 µmol kg -1 yr -1 depending on the season and region. For f CO 2 we evaluate trends ranging between +1. 5 and +1. 7 µatm yr -1 and for pH between -0. 0016 and -0. 0019 yr -1. Not surprisingly, because the same original data are used, the summer and winter trends for each box (B, CDE) are consistent with preliminary results from  Reverdin et al.
05 ± 0 -0. 006 ± 0 0. 3 ± 0. 1 0. 6 ± 0. 1 1. 0 ± 0. 1 1. 3 ± 0. 1 -0. 0009 ± 0. 0004 0. 9 ± 0. 3 -0. To gain insight into the DIC trends and separate natural versus anthropogenic contributions to the f CO 2 and pH trends, we evaluate the anthropogenic DIC (hereafter denoted C-ant) in the NASPG region. Here we use the TrOCA method (Tracer Combining Oxygen, Inorganic Carbon and Total Alkalinity;  Touratier et al. , 2007)  applied to data available in the GLODAPv2 database  (Key et al. , 2015; Olsen et al. , 2016). In the NASPG, repeated observations since 1997 were mostly conducted during summer (13 cruises in June-September). Because indirect methods such as TrOCA are not suitable to evaluate C-ant concentrations in surface waters (due to biological activity and gas exchange), we calculate C-ant in the layer 150-200 m only.
5b, e ). In contrast to 1993-1997, the winter oceanic CO 2 source increases during 2002-2007. It is near equilibrium in 2002 and a source in 2004-2007 (Fig. 4h ), a result previously confirmed with independent sea surface f CO 2 observations  (Metzl et al. , 2010). At the end of this period, in winter 2007, oceanic f CO 2 was higher than 400 µatm and pH was low, near 8. 04. The last period, 2008-2015, was not investigated in previous studies  (Corbière et al. , 2007; Metzl et al. , 2010). 2018a ) and updated data to 2018, available at https://doi. org/10. 6096/ SSS-BIN-NASG (last access: 25 March 2020). The mean trends for each period selected for the carbonate data analysis (when available) are represented as black lines  (1: 1993-1997; 2: 2001-2007; 3: 2008-2017). The NAO index (in blue, Hurrell data available at https://climatedataguide.
ucar. edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-station-based, last access: 30 September 2019) and AMO index (in red, Friedman ERSST V5 data available at https://www. ncdc. noaa. gov/data-access/marineocean-data/ extended-reconstructed-sea-surface-temperature-ersst-v5, last access: 20 November 2019) are represented (c). During 2008-2015, the DIC concentrations in winter continue to rise (+1. 0 to +1. 3 µmol kg -1 yr -1 , Table  2 ) but at a slower pace compared to previous years and still higher than the anthropogenic DIC trend. Contrary to 2001-2007, we do not observe large changes in TA. In the northern region (box CDE) the sea surface cools (-0. 05 • C yr -1 ), which is not found in the southern region, inducing different f CO 2 and pH trends in the southern and northern sectors.
, 2018b) , and no such signal is identified for winter cruises in 2008-2015. In addition, during OVIDE cruises conducted in summer in 2006-2014 in the North Atlantic (e. g. ,  García-Ibáñez et al. , 2016) , we performed regular TA and DIC intercomparisons with the ICM/CSIC group in Vigo (F. Pérez) and certified our results to within around ±4 µmol kg -1 for both TA and DIC. Results based on the surface TA samples measured at LOCEAN for the OVIDE cruises in summer in  2006 -2018 (data in Metzl et al. , 2018) )  also suggest that sTA increases in the NASPG at a rate of around +1. 5 µmol kg -1 yr -1. To support these results, we have evaluated sTA trends in the NASPG from independent data available in the most recent GLODAPv2. 2019 version  (Olsen et al. , 2019). For this we selected the data in the layer 0-20 m for all cruises conducted in June-August in 1997-2014.
We found a significant difference of sTA trends during this period: -0. 52 µmol kg -1 yr -1 in 1997-2006 against +0. 54 µmol kg -1 yr -1 in 2006-2014. Although the periods are not exactly the same and sTA trends from GLODAPv2. 2019 are smaller than those deduced from the SURATLANT time series, the changing sTA trends from negative to positive in recent years are also features in that dataset. We are thus confident with TA data over time and need to find a process that explains why the sTA trend was negative in summer in 2001-2007 (-1. 9 µmol kg -1 yr -1 ) and positive in summer in 2008-2017 (+2. 3 µmol kg -1 yr -1 ). The variability of calcification through the production of calcifying species (e. g. , coccolithophores, foraminifera) is a possible mechanism that would impact TA trends as suggested by  Wakita et al.
, 2011 Häkkinen et al. , , 2013)) , suggesting that more water of subtropical origin penetrates the NASPG. This would have decreased the DIC surface concentrations (and increase TA as well), but for 2001-2007 we observed the opposite, including in winter (Table  2 ). We thus eliminate the effect of advection to interpret the DIC-nat increase in the NASPG that impacts pH trend significantly. As mixed-layer depths are not deeper than 30-40 m in summer and present no significant interannual changes (according to the dataset Armor which presents the same variations as the data of the product GLOBAL-REANALYSIS-PHY-001 030 distributed on http://marine. copernicus. eu/, last access: 28 October 2019), the variation in the vertical mixing during this season is not a likely candidate to explain the changes in DIC-nat.
Conclusion and perspectives Based on sea surface observations of DIC and TA collected in the NASPG over 1993-2017, we have analyzed the variability and trends of the carbonate system properties including f CO 2 , pH, Ar and Ca calculated from the DIC-TA pairs. This study extends to summer, and for pH and Ar there is previous work based on winter data  (Corbière et al. , 2007; Metzl et al. , 2010). It also extends the analysis for the last decade, 2008-2017, after the AMO reached a maximum and then decline and when the NAO was highly variable. In the last decade we observed a continuous surface DIC increase in winter in the northern and southern NASPG. In February 2015 when the NAO was in a positive state, DIC reached a maximum concentration (DIC > 2150 µmol kg -1 , i. e. , more than +20 µmol kg -1 higher than in the 1990s).
4g, h ). Our results added to those from  Macovei et al. (2019)  suggest the ocean CO 2 sink increased in the North Atlantic (north of 40 • N) at least since 2007 and until 2017, contributing to the increase in the global ocean CO 2 sink  (Friedlingstein et al. , 2019). At the NA basin scale this is consistent with results deduced from f CO 2 reconstructed databased methods  (Denvil-Sommer et al. , 2019; Gregor et al. , 2019)  but opposed to the ocean CO 2 sink variability generated by current ESMs, suggesting uncertainties for predicting the evolution of the NA CO 2 sink in the future  (Lebehot et al. , 2019). For pH the long-term trend in the NASPG of -0. 0017 yr -1 is in the range of what is observed in other oceanic regions but we note some differences. This trend is equal to the trend estimated at BATS station but is lower than in the Irminger Sea and higher than in the Iceland Sea  (Bates et al.
, 2016)  or for CLIVAR GO-SHIP cruises assembled in GLODAP  (Olsen et al. , 2019). In addition to ship-based observations, the analysis of data from BGC Argo floats equipped with pH sensors (together with temperature and salinity sensors, from which TA, DIC and f CO 2 can be estimated (e. g. ,  Williams et al. , 2017)  will help to better constrain spatial, seasonal and interannual variability. Such data synthesis would also help to validate ocean and Earth system models that at present do not correctly represent the temporal change in marine biogeochemistry as demonstrated by  Lebehot et al. (2019)  for the North Atlantic. Data availability. The dataset is freely available and is accessible at http://www. seanoe. org/data/00434/54517/ (http://doi. org/10. 17882/ 54517 (last access: 10 March 2020),  Reverdin et al.
Acknowledgements. The  SURATLANT  project is supported by the  French institute INSU (Institut National des Sciences de l'Univers) , the  Lamont-Doherty Earth Observatory (LDEO) , the  National Oceanic and Atmospheric Administration (NOAA) -Atlantic Oceanographic and Meteorological Laboratory (AOML)  and the  Climate Program Office (CPO). We thank the  EIMSKIP Company  and the MFRI team, both based in Reykjavík (Iceland), for their cooperation in seawater sampling and analysis. We also thank the numerous scientific volunteers who worked at sea as well as www. biogeosciences. net/17/2553/2020/Biogeosciences, 17, 2553-2577, 2020.
clinicaltrials. gov/ct2/show/ NCT00912041). This clinical trial is part of the ongoing effort to develop brain-computer interfaces (BCIs) that allow people with tetraplegia to control computer cursors, tablet computers, robotic arms, and other devices with their neural activity  (Aflalo et al. , 2015; Ajiboye et al. , 2017; Bouton et al. , 2016; Collinger et al. , 2013; Gilja et al. , 2015; Hochberg et al. , 2006 Hochberg et al. , , 2012;; Jarosiewicz et al. , 2015; Nuyujukian et al. , 2018; Truccolo et al. , 2008). Intermixed with sessions dedicated to other neuroprosthetics research, each participant performed five research sessions testing for memory replay. The BCI paradigm confers a unique advantage for studying memory replay because the recorded neural activity patterns that are tested for replay are known to have been directly and causally responsible for the performance of the learned motor sequence.
EXPERIMENTAL MODEL AND SUBJECT DETAILS Permission for this study was granted by the U. S. Food and Drug Administration (Investigational Device Exemption #G090003) and the Institutional Review Boards of Partners Healthcare/Massachusetts General Hospital, Providence VA Medical Center, and Brown University. The two participants (identified as T9 and T10, Table  S1 ) had two 96channel intracortical silicon microelectrode arrays (Blackrock Microsystems, Salt Lake City, Utah, US) placed neurosurgically as part of the BrainGate2 pilot clinical trial (https:// www. clinicaltrials. gov/ct2/show/NCT00912041). Both participants gave informed consent to the study and publications resulting from the research, including consent to publish photographs and audiovisual recordings of them. Both participants were proficient at controlling motor imagery-based brain computer interfaces (BCI), and took part in 1-3 sessions (3-4 hours each) per week, of which the current study was one of several concurrent studies.
METHOD DETAILS Signal acquisition-Neural activity detected by the 96 recording channels of each microelectrode array was transmitted via a cable attached to a percutaneous connector during each recording session. Signals were analog filtered (4th order Butterworth with corners at 0. 3 Hz and 7. 5 kHz) and digitized at 30 ksps by two 128-channel NeuroPort Neural Signal Processors (Blackrock Microsystems, Salt Lake City, Utah, US). The signals from both systems were fed to custom software written in Simulink (The MathWorks, Inc. ) for saving and for further processing and decoding. Signal pre-processing-For pre-processing, signals were downsampled to 15 kHz and then common-average referenced  (Ludwig et al. , 2009)  to reduce any electrical artifacts common to all channels. Namely, the mean signal across the 60 channels with the lowest noise distribution on each array, identified from a single 30 s "reference" recording at the start of the first session, was computed and subtracted in real time from all channels' signals on that array.
, 2016; Perge et al. , 2013)  that might otherwise spuriously affect correlations across time. For the sequenced single-target replay analysis, the neural activity corresponding to each individual target was extracted from the repeated sequence trials in each session. Then, for each of the 4 targets, the mean duration of all trajectories to that target was obtained, and all of the neural activity sequences corresponding to that target were temporally resampled to that mean duration using MATLAB's resample function. This allowed all neural activity patterns corresponding to that target's trajectories to be averaged together, resulting in one neural activity template for each of the 4 targets. Thus, each average target's template consisted of a TxF matrix, where T is the mean number of time bins across all trajectories to that target and F = 40 is the set of neural features.
5x its original duration (time dilation factors = [0. 05, 0. 075, and 0. 1 to 2. 5 in increments of 0. 1]) using MATLAB's resample function and repeated the above analysis for all time dilation factors. We also verified that the results were not sensitive to the CC centile threshold we chose when identifying CC peaks by repeating the analysis using a sweep of different centile thresholds  (75, 90, 95, 99 and 99. 9). Results for each session are shown in Figure  S3. To test for replay across sessions, we first computed the mean RI of the repeated sequence trials and the mean RI of the control sequence trials in each session. We then tested whether the 10 repeated-trial mean RIs (1 for each session) were significantly higher than their respective control-trial mean RIs using a paired 1-tailed t test (see Figures  2B-2D  and 3 ).