dataesr/scientific-paragraphs-categorization huggingface dataset:
Smoothness of the nonlinear effects was enforced via a second derivative penalty, and parameter selection was performed using cross-validation  [22]. Data from all participants' valid minutes were used in the model fitting except for minutes, which had activity count values equal 0. The activity count=0 exclusion was motivated by a large proportion of zero values, and the need to estimate the relation for small activity count values without it being inflated by the large number of zeros. Relationships were estimated as strictly monotonic (without monotonicity having been constrained explicitly). The generalized additive model was used to provide values for 2-way mapping between activity count and each measure. All measurements were mapped into activity count, where (x) represents the activity count value estimated by mapping the x value of a measure, where measure represents monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, or activity intensity.
Evaluation To assess mapping accuracy in estimating physical activity volume statistics, total activity count (the sum of minute-level activity count values from a day) was computed for each participant, using activity count data and , and the difference was defined the estimation error. Estimation error was summarized by calculating mean percentage error (MPE), mean absolute percentage error (MAPE), median percentage error, and median absolute percentage error for each participant and aggregated across participants (mean and SD). To assess whether mapping accuracy depended on participant activity level, MPE values were plotted against the participant's average total activity count. The utility of the mapping for classifying minutes into various activity intensity classes was assessed. We used activity count cut-offs derived to (1) separate sedentary and active minutes in data collected with a sensor worn on nondominant wrist in older adults  [23] , (2) separate sedentary from light and (3) light from moderate-to-vigorous activity intensity levels in data collected with a sensor worn on a nondominant wrist in young to older adults  [24].
2), followed by activity intensity (MAPE 6. 7), mean amplitude deviation (MAPE 11. 1), and Euclidean norm minus one (MAPE 12. 5). To the best of our knowledge, we are the first to provide freely available R software (SummarizedActigraphy R package) with a unified interface for computation of the 4 open-source measures from raw accelerometry data, with complicated mathematical formulas distilled into a reader-friendly text (Multimedia Appendix 2).
Limitations First, the data were from a sample that consisted of predominantly middle-aged to older adults (Table  1 ). However, we observed that (1) the level of activity of adults in the sample ranged from sedentary to moderate and vigorous activity, (2) mapping results did not exhibit any trend based on the average level of the participant's physical activity, and (3) the variability of estimates was lower for higher activity values, which suggests that mapping could prove useful in future studies with younger (more active) populations  [25]. Second, physical activity measures were computed using raw accelerometry data collected at a frequency of 80 Hz. While this frequency matches that of physical activity data from NHANES 2011-2014  [12]  that uses the monitor-independent movement summary measure, caution should be used in adapting our harmonization mapping to raw data collected at a different frequency.
Third, data had been collected with sensors worn on the nondominant wrist only. While we expect the results to be generalizable to data from sensors worn on the dominant wrist, we presume that correlations and mapping would not be applicable to chest-or hip-worn sensors, because physical activity volume statistics (eg, total activity count) calculated from raw data collected by these devices are expected to be substantially lower than when measured at wrist. Fourth, harmonization mapping was estimated using generalized additive modeling, which does not offer an easy, closed-form formula of the transformation. While a closed-form formula could be obtained using polynomial regression models, the choice of generalized additive models allowed for thorough estimation of a relationship between activity count and other measures in a more flexible way.
We contribute two conceptual frameworks inspired by fictional superpowers that (1) characterize the common underlying visual, perceptual, and cognitive mechanisms that form the basis for many fictional abilities, and (2) describe ways in which visualization and other epistemic tools can give the people who use them a sense of objective or subjective empowerment-the increased ability to achieve goals. These frameworks are the result of three years of iterative discussions driven by our experience collecting, organizing, and scrutinizing depictions of enhanced epistemic abilities from a wide range of media including comics, film, television, and video games. During this process, we gathered a broad set of superpower examples, drawing on fan-curated superhero databases and our own exposure to superpowers in fiction.
A. R. V. I. S. We expanded the list of abilities by immersing ourselves in online pop-culture encyclopedias, including the Fandom Superpower Wiki 1 (a community-curated database of superpowers that as of early 2021 included over 16,000 entries) as well as community-generated superpower taxonomies  [21, 75]. We also revisited an extensive cross-section of original source material from movies and comics, focusing on specific characters whose abilities emphasize perception and knowledge generation. From this process, we distilled a list of 26 distinct perceptual and cognitive superhuman abilities from fiction, many of which are exhibited by a variety of characters. As we gathered each example, we looked at (a) which aspects of human abilities it amplifies, (b) in what contexts and situations characters use the ability, (c) how characters control the ability, and (d) how the ability is visually portrayed in print and other media.
ACKNOWLEDGMENTS We thank all of the SEVEN workshop participants and colleagues who contributed to the genesis of this work. This research was funded in part by  Inria-Calgary  associated team SEVEN, the  Canada Research Chairs Program , the  Natural Sciences and Engineering Research Council of Canada (NSERC) ,  Alberta Innovates Technology Futures (AITF) ,  SMART Technologies, ULC , and the  ANR  grant  ANR-19-CE33-0012.
Motivation: Tracking SARS-CoV-2 variants through genomic sequencing has been an important part of the global response to the pandemic. As well as whole-genome sequencing of clinical samples, this surveillance effort has been aided by amplicon sequencing of wastewater samples, which proved effective in real case studies. Because of its relevance to public healthcare decisions, testing and benchmarking wastewater sequencing analysis methods is also crucial, which necessitates a simulator. Although metagenomic simulators exist, none are fit for the purpose of simulating the metagenomes produced through amplicon sequencing of wastewater. Results: Our new simulation tool, SWAMPy (Simulating SARS-CoV-2 Wastewater Amplicon Metagenomes with Python), is intended to provide realistic simulated SARS-CoV-2 wastewater sequencing datasets with which other programs that rely on this type of data can be evaluated and improved.
Evaluating the effectiveness of new methods on in vivo or in vitro samples is often difficult or impossible, for example because of the lack of availability of a wide range of real or synthetic samples and the costs of repeated experiments  (7). However, simulated datasets can provide an efficient way of benchmarking new methods' performances  (8). There is a specific set of features characteristic to data coming from wastewater amplicon sequencing. For example, it has been shown that there is a high variation in amplification across different amplicons of a given primer set, resulting in a variation in read depth across the genome  (1, 2). Moreover, wastewater data is expected to represent a mixture of different SARS-CoV-2 variants since the biological matter in the sample comes from multiple people, and will carry RNA degradation signatures resulting from the environmental exposure of the viral RNAs in sewage as well as PCR, sequencing library layout-specific and sequencing device-specific errors  (9) (10) (11).
Each study performed its own simulations for its specific use case, omitting important characteristics of real life data such as PCR errors or only partially accounting for characteristics like variable amplicon abundance by replicating one abundance distribution seen in in vitro experiments. Our simulator, SWAMPy (Simulating SARS-CoV-2 Wastewater Amplicon Metagenomes with Python), is intended to produce a realistic set of reads that might be generated through multiplex PCR of a wastewater sample, and sequenced by an Illumina sequencer. We model the following scenario: 1. Different viral genomes coming from a human population contaminate wastewater systems, creating a mixture of virus variants which is then captured in a wastewater sample. At this stage, viral RNAs are exposed to RNA degradation and there is a variation in variant abundance in the mixture.
Methods The overall workflow of SWAMPy can be seen in Fig. 1. The four basic steps of our software pipeline are detailed as follows: 1. Create an initial amplicon population   16 ) to align the primers (forward and reverse complement) to each virus genome to detect primer binding positions on the source genomes. Next, we slice the source genomes from the primer binding positions to obtain individual amplicons of each source genome, including the primer sequences. During the alignment step, some primers may not align well with the viral genome, and in those cases, the corresponding amplicon is not produced. While this is a strict penalty for primer misalignment, it approximates the observation that some amplicons can be dropped due to mutations in the target regions  (17).
D. Simulate read sequencing using ART. To create a set of simulated paired-end Illumina reads from each amplicon, each with a given read count, we use the program ART  (14). We use ART's paired-end amplicon mode, as well as the noALN and maskN flags. These settings create 150bp paired-end reads, and faithfully transcribe any "N" characters appearing within the amplicons. We use a set of default error rates and quality score profiles tuned for the Illumina MiSeq V3 sequencer, though the ART package has options available for other platforms and read lengths. A full list of the flags used is in the supplementary material. Finally, we use a custom script based on ubiquitous bash utilities to concatenate all of the FASTQ read files, and shuffle their order to avoid potential biases in case any downstream application software can be influenced by read ordering.
Results The source code of our python implementation of SWAMPy, together with the program documentation and exemplar files is available under the GPL-v3 license at https:// github. com/goldman-gp-ebi/SWAMPy. E. SWAMPy Implementation. SWAMPy takes as input a multi-FASTA file containing the SARS-CoV-2 variant genomes that will be present in the simulated wastewater sample, as well as a file that contains the relative abundances of these variants in the mixture. For ease of use, other input files (primer-set-specific sequence files, and primer-set-specific amplicon distribution files) were wrapped with a single --primer-set parameter which loads the corresponding input files for the specified primer set. As of December 2022, there are three supported primer sets: ARTIC V1, ARTIC V4  (12)  and Nimagen V2  (21).
There are many command line parameters that allows fine control of the program such as the parameter c that reflects the quality of the wastewater sample as described in Section B, target number of simulated reads, and error rates, VAF and lengths of high frequency errors as described in Section C. 1. The full list of command line interface arguments and their explanations are available on the GitHub wiki page: https://github. com/goldman-gp-ebi/ SWAMPy/wiki/CLI-arguments. An example SWAMPy run takes 300 seconds to complete and reaches 700MB of max memory when run with default parameters (three SARS-CoV-2 variants and default error rates and 100. 000 total read counts) on a single thread of an Intel Xeon Gold 6336Y 2. 40GHz CPU.
ACKNOWLEDGEMENTS The authors thank  Joshua Quick ; members of the  JBC-led Wastewater Genomics collaboration , in particular  Terry Burke ,  Hubert Denise ,  Steve Paterson ,  Christopher Quince  and  Sébastien Raguideau ; SWAMPy program testers  Charlotte West ,  Lukas Weilguny ,  Sevim Seda Çoko glu ,  Kıvılcım Başak Vural ,  Melih Yıldız  and  Gözde Ata  g; and  Mehmet Somel  who supported this project.
Since its last  31  vulcanian eruption in 1880-90, La Fossa has recurrently been in a state of unrest, motivating extensive geochemical work and leading to the acquisition of an uniquely robust gas dataset  23. A common conclusion of these studies  19, 21, 23  is that intense fumarolic activity is sustained by mixing between CO 2 -N 2 -Herich magmatic gases and hydrothermal steam deriving from evaporation of seawater-fed hot and saline fluids. Periods of elevated unrest occurred in the 1920s (ref. 32 ) and in 1988-93 (ref. 19 ), this latter phase being thought  33  to have started in late 1977. Lower intensity periods of unrest were detected in  1996, 1998, and 2004-2009 (refs. 23,34,35  ). These unrest periods have been characterised by volcanic gases becoming increasingly magmatic in composition  19, 21, 23, 36  (with CO 2 -N 2 -He concentrations increasing relative to H 2 O), by visible escalations in outgassing flux  37, 38  (Fig.
By comparing observations with results of a degassing model, initialised at various P-T-X conditions relevant to Vulcano Island volcanism, we propose that degassing of mafic magma-either due to ascent and decompression at depth or through the release of previously accumulated gas at the roof of a ~5 km deep reservoir underneath La Fossa-triggered the 2021 unrest. Results and discussion SO 2 emissions. We quantify the SO 2 flux from La Fossa cone during the 2021 unrest from ground-based remote dual UV Camera measurements (Supplementary Fig. 1 ; see Methods). Although discontinuous, our UV Camera data (Supplementary Table  1 ) record an escalation in SO 2 release during fall 2021 (Fig. 2a, b ). Our measurements do not constrain the exact onset of the degassing unrest (fixed at September 13 based on geodetic evidence  43  ) but instead bracket the transition period from background to unrest regimes.
2c ) essentially reflects increasing SO 2 in the fumarolic emissions, consistent with the SO 2 flux increase observed (Fig. 2a ). In addition to a low CO 2 /S T signature, the October-November 2021 emissions are also particularly H 2 Opoor, with measured molar H 2 O/CO 2 ratios (1. 2-4. 1) being the lowest on record (Fig. 4a ). Multi-GAS data from December 2021 exhibit more variable composition than in previous months (Fig. 4b ), together with a gradual progression toward higher CO 2 / S T ratios (11. 6-70; Supplementary  Overall, the October-December 2021 results identify a compositional field that overlap the magmatic gas compositional end-member identified by directly sampled 1988-91 fumaroles  19  (Fig. 4a ). An especially SO 2 -rich volcanic gas in fall 2021. Our gas observations provide evidence for a particularly SO 2 -rich fumarolic composition in fall 2021.
6b ) that is not reflected by the MI dataset (Fig. 7b ). These results suggest that redox conditions between NNO (refs. 48, 63 ) and NNO-1 (Fig. 7b ), rather than NNO + 1, prevail in the magmatic system. The results of the B3/B6 model runs are thus considered for illustrative purposes only in the following discussion (Figs. 4, 8 ; Supplementary Fig. 4 ). The L1, T1 and R1 model runs (Fig. 6b ) match well the low H 2 O-S end of the MI populations (Fig. 7b ). The theoretical magmatic gas compositions coexisting in equilibrium with the modelled melts-for all melt compositions explored-evolve from being CO 2 -rich at initial pressures (300 to 50-40 MPa, depending on the runs) to H 2 O-and S-rich at 0. 1 MPa (Fig. 4b , Supplementary Fig. 4 ), reflecting the pressuredependent solubilities of the major volatile species in silicate melts  64, 65.
4a ). However, the positive relationship between H 2 O/S T and CO 2 /S T defined by the October-December 2021 array (Fig. 8 ) also implies some degree of mixing between magmatic and hydrous (and S-poor) hydrothermal fluids  19, 21, 23, 36. We estimate the composition of the magmatic gas end-member by averaging the most H 2 O-poor (i. e. , those with the smallest hydrothermal component, if any) plume samples in our dataset. This end-member magmatic gas (white star labelled M in Fig. 8 ) contains ~68. 4 mol. % H 2 O, ~28. 7 mol. % CO 2 , and ~2. 9 mol. % S T , and is less hydrous than estimated previously for the 1988-93 unrest (see purple star labelled M in Fig. 8 ; ref. 19  ). Identifying the composition of the hydrothermal component is more challenging, as previous work has found this component to vary in composition over time  48.
Methods UV Camera. We use the dual UV Camera technique  [91] [92] [93]  to quantify the SO 2 flux release from the La Fossa fumarolic system. Measurements were initially conducted during campaigns (Supplementary Fig. 1a ) with a portable UV Camera unit similar to the one described in ref. 94. The instrument was powered by a 12 V battery and commanded via a Toughbook PC using the Vulcamera software  95. We acquired data for several consecutive hours per day (at 0. 5 Hz rate) and the mean daily recorded SO 2 flux is listed in Supplementary Table  1  (data illustrated in Fig. 2a ). On November 23, a permanent UV Camera system was field deployed in the Vulcanello area, ~2 km away from La Fossa (see Supplementary Fig. 1a  for location). This fully autonomous UV camera system, similar to those in operation at nearby Stromboli  96  , is powered by 12 V battery kit and solar panels, and is commanded by an embedded mini-PC (Jetway NF531M) using a set of ad-hoc designed algorithms  93  that allow the system to acquire 5 h per day and store data in external hard-drives.
The fumarolic field was found more compositionally heterogeneous in these occasions (Fig. 3  and Supplementary Fig. 2 ), as indicated by the ratio results for the individual fumarolic vents (GPS coordinates listed in Supplementary Table  2 ). Our Multi-GAS dataset is also complemented by a series of measurements carried out during routine monitoring surveys in 2009 to June 2021. These unpublished results are also listed in Supplementary Table  2. Interpretation of our Multi-GAS dataset is assisted by integration and comparison (Figs. 2, 4, 5, 8) with results of direct sampling of the fumaroles from the literature (Supplementary Fig. 3 ). The Multi-GAS records in-plume gas concentrations using a sensor kit consisting of (i) one Gascard nondispersive infrared (NDIR) spectrometer from Edinburgh Sensors (for CO 2 ) (ii) two specific electrochemical sensors (both from City Technology) for SO 2 and H 2 S, and (iii) one KVM3/5 Galltec-Mela sensor for temperature (T) and relative humidity (Rh), which were converted into in-plume H 2 O concentrations 98 using the Arden Buck equation.
The most recent configuration of the Multi-GAS, including details on sensor's accuracy and repeatability, calibration ranges and gas standards, is described in ref. 100  ). Our Multi-GAS version, assembled at UniPa, uses a Campbell CR6 datalogger to log simultaneously at 1 Hz the signals outputted from all sensors. We calculate ratios between SO 2 and other volatiles in the fumarolic plumes of individual fumaroles (listed in Supplementary Table  2 ) by post-processing the acquired dataset using a routine processing protocol implemented in the Ratiocalc software  99. To this aim, sub-sets of data obtained at individual fumaroles are initially selected, and gas ratios are then derived from the gradients of the best-fit regression lines in gas (either H 2 O, CO 2 or H 2 S) vs. SO 2 scatter plots.
7, (ii) the SO 2 concentration threshold of 20 ppm is reached. The obtained GPS-localised CO 2 /SO 2 ratios are interpolated with multi-level B-splines  102  to obtain the contour map of Fig. 3. This figure explores the spatial heterogeneity of the fumarolic field at finer spatial resolution, and is thus complementary to the individual fumarole results listed in Supplementary Table  2. Melt inclusions. The pre-eruptive volatile content of La Fossa magmas is inferred from information derived from analysis of silicate melt inclusions and by the application of mineral-melt equilibria. We rely on an extensive dataset (reported in Supplementary Table  3 ) assembled from collection of published major and volatile elements melt inclusion analyses  31, 61, 62, [103] [104] [105] [106]. The compilation is complemented with unpublished and new results obtained in this work (data all listed in Supplementary Table  3 ).
Silicate melt inclusions (MIs) are hosted in phenocrysts picked from pumiceous lapilli of pyroclastic deposits, which guarantee quenching of the trapped melt upon eruption. The sampled eruptions span the last 1000 years of activity of La Fossa and Vulcanello (Vulcanello 1, Palizzi eruptive period, Lower Pietre Cotte, 1888-90 eruption; ref. 30  ; Eruptive Epoch 8 in ref. 28  ). The dataset covers the entire basaltto-rhyolite range (Fig. 7a ). Since only few basaltic melt inclusions have been found in recent La Fossa products (in Fo90 olivine and diopside antecrysts in 1888-90 eruption  62  ), the melt inclusions of La Sommata (about 42 ky; Eruptive Epoch 5 in ref. 28  ) are taken as proxy for the basalts feeding the La Fossa plumbing system, based on the similar composition and the petrogenetic reconstructions  62.
Acknowledgements This work received funding from the  Italian Ministero Istruzione Università e Ricerca (Miur , Grant N. 2017LMNLAW) and from the  NSFGEO-NERC  grant Sulfur cycling in subduction zones (subcontract between  Lamont-Doherty Earth Observatory  and  DiS-TeM , co-Pi A. A. ). Financial support from  BHP  is acknowledged for help with the acquisition and processing of gas composition and flux data collected in June 2021 only. The authors wish to thank  A. Mazot ,  J. Stix  and  Y. Taran  for useful comments on an earlier version of the manuscript.
Data availability All data produced in this work are listed in Supplementary Tables  1 2 3 4. The volcanic SO 2 flux dataset and the volcanic gas composition dataset are publicly available in the EarthChem data repository (https://www. earthchem. org/ecl/) as Supplementary Data 1, 2 (ref. 114 ). Major elements and volatile composition of melt inclusions from Vulcano Island are publicly available in the EarthChem data repository (https://www. earthchem. org/ecl/) as Supplementary Data 3 (ref. 115 ).
Code availability The CO 2 -H 2 O-SO 2 -H 2 S-melt saturation and degassing code is publicly available for download at https://github. com/charlesll//chosetto (https://doi. org/10. 5281/zenodo. 5554941) (ref. 116 ). The CHOSETTO. exe program (for Windows®) is available together with instructions to run it and relevant information on licensing and credits to be given.