dataesr/scientific-paragraphs-categorization huggingface dataset:
Impacts detection campaigns and global analysis Current observations are carried out by several collaborators. Although up to now none of the discovered impact flashes has been detected by running this software we recall that the software is only being used by a small percent of the amateur community and we are working towards a wider use of DeTeCt. Both versions of the DeTeCt software produce short text log files with timing of the videos that are sent to one of us (M. D. ) for statistical analysis. This analysis is done by a set of software scripts that we use to know how much time the network of collaborators are observing Jupiter in search for impacts. Currently, the software has been used by 78 observers examining more than 76 800 video files equivalent to about 87 days of observations distributed unevenly over the last few years.
A similar survey of Saturn videos with 17 observers has accumulated a total observation time equivalent to 11. 4 days with negative results in the search of impacts on Saturn so far. Updates of these statistics are reported on: http://www. astrosurf. com/planetessaf/doc/project_ detect. shtml. We expect that users of the software will be far more numerous in the near future since DeTeCt3. 1 (which is.
Perspectives and conclusions The last Mars opposition in July 2018 attracted a high interest of the amateur observers due to the favorable conditions of the opposition with Mars reaching 24. 3 arcsec in diameter. This opposition occurred at solar minimum, but the next two Mars oppositions in October 2020 and December 2022 will also result in high-quality observations of the planet due to its large apparent size (Fig. 2 ). The solar activity will be close to maximum in December 2022 with a declination of +25 °favoring observers from North latitudes where a higher number of amateur observers reside. In the case of a CME with a high probability to arrive to Mars, a quick alert can be issued to amateurs through popular sites such as the PVOL database where regular alerts on Jupiter, Saturn and Neptune meteorological features are issued.
Acknowledgements. We thank  Dave Andrews  and an anonymous referee for their review of this paper. We are grateful to  Ishi Tabe in Japan  and  C. Palotai  at  Florida Institute of Technology and Kunio Sayanagi at Hampton University  for motivating further observations of Jupiter oriented to find new impacts. We are also specially grateful to the planetary astronomy amateur community for their continuous observations of Solar System planets and in particular to users of DeTeCt collaborating with this project. Part of this work has been developed in the framework of the  Europlanet 2020 RI , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  654208. Further support was given by the  Spanish  projects  AYA2015-65041-P (MINECO/FEDER ,  UE ) and  Grupos Gobierno Vasco   IT-765-13.
However, few studies have formally tested for asymmetry in aggregate housing prices. Against this background, this paper investigates asymmetry in housing price series for the 50 US states plus the District of Columbia and 381 metropolitan statistical areas (MSAs) using monthly Freddie Mac House Price Indices spanning the period 1975:1-2015:6. The choice of this dataset is motivated by the availability of a large set of high quality, methodologically consistent series, with a wide coverage of the United States. Developments in housing prices tend to vary widely across US states and MSAs. For example, over the past decade or so, the "Sand states" (Arizona, California, Florida, Nevada) experienced dramatic boom-bust cycles, while the housing cycle was muted in large parts of the Midwest. Hence the use of disaggregated data allows a more precise assessment of the extent of asymmetry in the housing price cycle than the use of broad aggregates, which may mask specific market evolutions.
Data The measure of housing prices used in this study is the monthly Freddie Mac house price index (FMHPI) covering the period 1975:01-2015:06. The FMHPI is a repeatsales index covering transactions on one-family detached and townhome properties serving as collateral on loans purchased by Freddy Mac or Fannie Mae. The repeat-sales methodology is widely used to measure housing price changes, particularly in the United States. The most prominent examples are the Federal Housing Finance Agency (FHFA), Standard & Poor᾽s (S&P) Case-Shiller and Core-Logic house price indices. By measuring the evolution of the value of the same property between two transactions, the repeat-sales methodology allows to measure price changes holding constant property type and location. A limitation of the procedure is that significant renovation or deterioration of the property may affect price changes.
All other persons mentioned in the context of the migration event were equally noted (others). This set of names was complemented by a list of the interviewee's parents, spouses and children. Finally, we asked each interviewee to name all those persons for whom he or she had acted as a host. This dataset is completed by a genealogical and residential dataset (about 50,000 individuals including the deceased) collected during three censuses of the village of  Afagnan-Gbléta (2004 /5, 2009 /10 and 2014/15)  as part of ongoing ethnographic fieldwork (see  Hamberger 2011). Most of the remote kinship relations (such as "father's mother's brother's daughter") and mediated non-kinship relations (such as "friend's employer") were thus not (only) directly reported by interviewees but computed from data stemming from numerous different oral sources.
The anonymized dataset is available on the Kinsources repository (permanent link: https://www. kinsources. net/kidarep/dataset-275-watchi-2017. xhtml). The total snowball sample has only an auxiliary value for the analysis. Besides the bias in favor of local and circular migration trajectories owing to the geographical limits of the survey, the snowball technique introduces a systematic bias in favor of connected and mobile persons, who have a higher chance of playing a role in the itineraries of the first interviewees. All comparative data concerning the overall sample thus are likely to overestimate mobility and network cohesion. Moreover, not being composed of independent observations, the snowball sample cannot be analyzed with standard statistical techniques. Most of our analyses are therefore restricted to the random sample of 60 itineraries.
Software Tools All analyses used in this paper have been made using the open source software Puck version 2. 3. 50  (Hamberger et al. 2014 ), which can be downloaded at http:// www. kintip. net (source code at http://sourceforge. net/projects/tip-puck/). Initially developed for the study of kinship networks, Puck contains, from its 2. 2 version onwards, a package (org. tip. puck. sequences) for the study of longitudinal data. The networks of Figs. 3, 6, 7, and 8 have been visualized with the software Pajek (http:// mrvar. fdv. uni-lj. si/pajek/) from files produced by Puck. The detailed procedures for reproducing the analyses of the paper are described in the document accompanying the Watchi dataset on the Kinsources repository (see above).
Sequence Networks After having examined in a synchronic manner the way personal networks have been shaped by (and have shaped) people's itineraries, let us now turn to the question how these itineraries run through the various positions of these networks. We shall construct the corresponding relational sequence networks by classifying mobility events according to the type of relation or relational chain that links ego to his or her host. To facilitate comparison, we have used a very coarse classificatory grid, which, for example, lumps together all relatives other than the immediate parents into a single category, 13 and generally does not distinguish between different instances of the same relational type (such as "landlord"), with the only exception of spouses. Even so, except for the three individuals who have never left their natal social 13 The software Puck allows for any kind of precision for the classification of kinship chains.
Acknowledgements The paper is part of a larger research project conducted with Karin Sohler since 2010. Preliminary results were the subject of joint presentations at the 1st European conference of Social Network Analysis in Barcelona in 2014 and at the 3rd meeting Réseaux-Histoire in Paris in 2015. Though Karin did not coauthor the present paper, its argument owes a fundamental debt to our collaborative work. I am also grateful to  Véronique Hertrich ,  Ismaël Moya ,  Sorana Toma ,  Claire Lemercier  and  Karen Middleton , as well as to the anonymous reviewers of the LaCOSA II conference and for this volume, for their valuable and encouraging comments on earlier versions of this paper. Fieldwork has been supported by the  Laboratoire d'Anthropologie Sociale , the  EHESS  research fund and the  French Cooperation in Togo.
The 2010 fieldwork was carried out in cooperation with  Ibitola Tchitou  and  Kokou Vignikin  from the  Unité de Recherche Démographique of Lomé University. This work would have been impossible without the competence and dedication of my field collaborators  Komi Malou Kakanou  and  Toussaint Yakobi  and the active cooperation and help of the interviewees, to whom I present my heartfelt thanks. The  sequence-network module of Puck has been developed with Christian Momon as part of the project  " Kinsources " supported by the  French National Research Agency ANR  (research grant  ANR-12-CORP-0008 ).
Data Analysis The data analysis was conducted using the lme4 package  (Bates et al. , 2015)  of R. In a mixed logit regression  (Jaeger, 2008) , we modeled the selection of w 1 -type-objects (coded as 0 or 1) compared to w 2 -type-objects during the critical test trials. All responses on critical trials were included in the analysis since participants always picked either w 1 -or w 2 -type-objects, and never a distractor object. The model included two categorical predictors with their interaction: Group (Competition vs. No-Competition) and Condition ({w} vs. {w, w 1 } vs. {w, w 1 , w 2 }) as well as a random intercept and random slopes for Condition for participants. The resulting R syntax for the model was: w 1 -type-objects-selection ~Condition * Group + (1 + Condition | Participant).
ACKNOWLEDGMENTS We would like to thank  Mélissa Berthet ,  Guillaume Dezecache ,  Emmanuel Dupoux ,  Joël Fagot ,  Andreas Haida ,  Nathan Klinedinst ,  Takashi Morita ,  Philippe Schlenker ,  Benjamin Spector , and  Shane Steinert-Threlkeld  for discussion (all errors remain ours). The research leading to these results has received funding from the  European Research Council  under the.
The center bin was excluded for this fit, and the phase of the sine wave was restricted so that its peak was aligned at the center bin (see STAR Methods for details). Shown is the average amplitude of this sine wave across participants, separately for each condition. SEM across participants is shown as error bars (after removal of between-participant variation). See Figures S2E and S2F for corresponding plots for other behavioral measures. bins in each participant (including extracting the 1% voxels with strongest modulation for each permutation). The application of our voxel selection procedure in the surrogate distribution can provide us with a range of values for tACS-induced modulation of BOLD responses that would have been produced in a dataset in which no tACS effect is present (shown in Figure  S3A ).
Electrodes were kept in place with adhesive, conductive ten20 paste (Weaver and Company, Aurora, CO, USA). Current intensity was set to 1. 7 mA (peak-to-peak). After each run, participants were asked to rate the perceived side effects of the stimulation between 0 (no side effects) and 10 (very strong side effects). On average, stimulation runs were rated as giving numerically higher side effects (1. 49 ± 1. 57, mean ± SD) than sham runs (1. 21 ± 1. 37), but the two stimulation conditions did not differ significantly (t(16) = 0. 99, p = 0. 33; paired t test). fMRI Data Acquisition and Pre-processing MRI data were acquired on a 3-Tesla Siemens Prisma scanner using a 64-channel head coil. A T1-weighted structural scan was acquired for each subject using a three-dimensional MPRAGE sequence (TR: 2250 ms, TE: 3.
e. , 2 3 8 = 16 conditions for each scanning run). Six realignment parameters were included in each run to account for movement-related effects and four regressors were used to remove the mean signal from each of the runs. An AR(1) correction for serial autocorrelation was applied and a high-pass filter with a cutoff of 128 s included to eliminate low-frequency signal confounds such as scanner drift. These single participant models were fitted using a least-mean-squares method to each individual's data, and parameter estimates (i. e. , beta values) were obtained for all voxels for each of the 17 participants, each of the 2 scanning runs (sham and stimulation conditions), each of the 8 phase relation, and each of the 2 stimulus types (intelligible and unintelligible sentences). These beta values were used for further data analyses as described below.
A re-analysis of the data with target trials excluded did not change any of the results reported in the main text. In addition to potential BOLD effects, we also analyzed whether certain measures of performance in our behavioral task (probability of a target detection, i. e. , ''hit,'' probability of false alarm and d-prime, the z-transformed difference between the two measures  [24] ) are modulated by the phase relation between tACS and speech. We therefore calculated performance as a function of this phase relation and quantified the strength of behavioral modulation by extracting the amplitude of a fitted sine wave, similar to the analysis of BOLD modulation described above. However, as the behavioral task was mainly included to ensure participants remained alert, this analysis was based on only 4 target trials (and 28 non-target trials) per phase bin and condition.
ACKNOWLEDGMENTS This work was supported by the  German Academic Exchange Service (DAAD)  and the  Medical Research Council UK  (grant number  SUAG/008/RG91365 ). The authors thank  Gary Chandler  and  Johan Carlin  for technical support;  Flavio Fro ¨hlich ,  Rufin VanRullen ,  Malte Wo ¨stmann ,  Charlie Schroeder , and  Peter Lakatos  for fruitful discussions; and  Christoph Herrmann  for helpful comments on the manuscript.
Likelihoods To estimate A, we fitted eqn. (3) to the data using a likelihood-based approach. The data collected during the experiments consisted of a set of interval-and right-censored time-to-event data. Outcome measures of interest included exiting (i. e. leaving a space), repellency (i. e. reduced entry into a treated space), knockdown, diversion (defined as the movement to a hut other than the release hut) and loss to follow-up, where exiting, knockdown and loss to follow-up are competing events (i. e. the occurrence of one event precludes the occurrence of another). The cumulative, conditional probabilities for all events observed in the experiment can be directly obtained from the solutions of eqn. (3), as detailed in eqns. (  4 ), (  5 ) and (  6 ).
Interval-censored events Data pertaining to knockdown and exit events are interval-censored between time points t 1 and t 2 , with exit events recorded at 30 minute intervals and knockdown events at hourly intervals. Given model parameter set θ, the probability that a mosquito released in H rel is observed to be knocked down in hut H at time t 2 is Pr t 1 < T < t 2 ; Y ¼ kd H jH rel ; θ ð Þ ¼ F kdH t 2 jH rel ; θ ð Þ -F kdH t 1 jH rel ; θ ð Þ F divH t 2 jH rel ; θ ð Þ S t 2 jH rel ; θ ð Þ ¼ K H t 2 jH rel ; θ ð Þ -K H t 1 jH rel ; θ ð Þ ; ð4Þ where F(t) denotes the probability that a specific event (here knockdown and movement to H) occurred in hut H by time t and S(t) denotes the survival function (i. e. the probability that no knockdown, exit or loss to follow-up has occurred by time t).
Acknowledgements We express our gratitude to  Carlos Ique , Director of the  Institute Veterinario de Investigaciones (IVITA), Iquitos, Peru  for the use of land for experimental hut studies. Thanks to  Roxanne Burrus ,  Kirk Mundal  and  Victor Lopez  ( NAMRU-6 ) for their logistic support during the experiments and to  Neil Lobo  for valuable insights. Special thanks to  Edwin Requena ,  Hugo Jaba  and to the team of mosquito collectors for their dedicated effort and to reviewers for valuable comments.
Observations and data analysis In the following, we report on the VLA data obtained in 2016 and the ALMA and IRAM/NOEMA data acquired between December 2016 and February 2017. In all cases, the phase centre of the observations was set at the coordinates α(J2000)=06 h 12 m 54. s 02, δ(J2000)=17 • 59 ′ 23. ′′ 1.
Calibration and imaging were performed with the CASA 1 software, by using different versions (from 4. 3. 1 to 4. 5. 2) of the JVLA data reduction pipeline. We estimate a calibration accuracy of ∼10%. The continuum images were constructed using natural or Briggs robust=0 weighting. Table  1  reports the mean synthesized beam widths at half power, at the five observing epochs. The rms noise level of the continuum images is close to the expected thermal noise, in the range 10-20 µJy beam -1 at 6, 10 GHz, and 22 GHz, while it is significantly higher, 50-100 µJy beam -1 at 45 GHz, where atmospheric phase noise is dominant. At 22 and 45 GHz, we self-calibrated the compact continuum emission, improving the dynamic range of the image by a factor of a few.
IRAM/NOEMA We observed S255 NIRS 3 with the interferometer on January 23 and February 15, 2017. The array consisted of eight elements in the compact D configuration. The bandpass and phase calibrator were J0507+179, while flux calibration was performed by means of observations of LkHa101. We used the wideband correlator (WideX) to obtain a measurement of the continuum emission at both 3. 4 and 2 mm. For this purpose the receivers were tuned to cover a 3. 6 GHz wide band centred at 87 and 150 GHz. All observations were performed with onsource integration times of 10 min. Observations were carried out first at 3. 4 mm, and were immediately followed by observations at 2 mm. Data calibration and imaging were made through standard procedures, using the GILDAS package 2. All maps were made with natural weighting, resulting in a synthesized beam with full widths at half power of 5.
′′ 0×3. ′′ 5 with PA=195 • at 3. 4 mm and 3. ′′ 0×1. ′′ 9 with PA=0 • at 2 mm in January, and 5. ′′ 1×3. ′′ 3 with PA 193 • at 3. 4 mm and 2. ′′ 9×1. ′′ 8 with PA=11 • at 2 mm in February. The continuum maps were obtained by visually inspecting the data cubes and selecting channels with no evidence of line emission. These channels were then averaged in the u, v domain and pure continuum images were made from the averaged u, v data. The resulting continuum bandwidth was 682 MHz at 3. 4 mm and 379 MHz at 2 mm. The thermal noise level is typically 0. 1 mJy/beam at 3. 4 mm and 0. 3 mJy/beam at 2 mm. Flux calibration is accurate to within 20% at both wavelengths. 2. 3. Atacama Large Millimeter/submillimeter Array S255 NIRS 3 was observed with ALMA at band 3 on December 16, 2016. The array consisted of 45 antennas arranged in a configuration with baselines from ∼20 to ∼400 m, which provides sensitivity to structures up to ∼30 ′′.
Since our main goal was to image the continuum emission, we configured the correlator to cover a total bandwidth of ∼8 GHz with four units of 2 GHz, in double polarization, centred at 85. 2, 87. 2, 97. 2, and 99. 2 GHz. We chose the maximum possible spectral resolution of 0. 49 MHz in order to identify strong lines that could significantly contaminate the continuum measurement. For flux and bandpass calibration we used the strong calibrators J0510+1800 and J0750+1231, while the phase calibrator J0613+1708 was separated by less than 1 • from our target. Calibration and imaging were performed with the CASA software by employing the ALMA data reduction pipeline. We estimate a calibration error of ∼20%. A data cube was created from each 2 GHz correlator unit with natural weighting. In order to obtain a pure continuum image, we adopted the STATCONT method by  Sánchez-Monge et al.
Centimetre emission Our VLA monitoring started on March 11, 2016, about 9 months after the estimated onset of the outburst (mid-June 2015; see  Caratti o Garatti et al. 2017). As illustrated in Fig. 1 , comparison with VLA archival data obtained at the same frequency on August 27, 1990 (project AM301), with similar angular resolution reveals some structural change. In the middle panel of the figure we also show the archival map after smoothing to the same angular resolution as our new data. All images clearly outline an elongated structure consisting of a central bright core, coincident with the NIRS 3 infrared source, plus two symmetrically displaced knots to the NE and SW. We interpret this structure as a bipolar radio jet, identified by us for the first time. Indeed, the jet lobes are oriented in the same direction as the axis of the bipolar outflow identified by  Zinchenko et al.
Millimetre emission Our monitoring with the IRAM/NOEMA and ALMA interferometers provides flux density measurements starting from December 16, 2016. In addition to this observation, we report on two more epochs, namely January 23 and February 15, 2017. These data allow us to establish that the millimetre emission from source NIRS 3 is basically constant in time. Our angular resolution is sufficient to resolve the hot molecular core associated with NIRS 3, named SMA1 by  Zinchenko et al. (2012) , from the nearby fainter core, named SMA2 by the same authors. In Table  1  we give the mm flux densities of NIRS 3 at the different epochs. Clearly, no change is seen within a calibration error of ∼20%. As already done for the VLA data, it is worth comparing our flux density measurements with those from the literature.
Interpretation: An expanding jet model We wish to demonstrate that an expanding thermal jet boosted by the accretion outburst is a viable explanation for the observed exponential increase in the radio emission from S255 NIRS 3. From the archival data we know that the radio jet was already present at the moment of the radio burst, which means that any realistic jet model should consider a scenario where a pre-existent thermal jet is suddenly perturbed by a massive ejection event. However, modelling the propagation of a similar perturbation across the jet is too complicated for our purposes. We thus prefer to simplify the problem by assuming that the expanding jet due to the outburst becomes detectable only when its radio flux overcomes that of the pre-existent jet. While this is not strictly correct, it should nevertheless provide us with an acceptable description of the outbursting component of the jet, which is what we actually measure with our VLA monitoring.
Acknowledgements. It is a pleasure to thank Francesca Bacciotti for stimulating discussions on jets from YSOs. We also thank the Italian ARC node and the IRAM technical staff for their support in this project. The research leading to these results has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreementNo. 730562 [RadioNet]. This study is based on observations made under the project 16A-424 of the VLA of NRAO. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This work is also based on observations carried out under project number E16AB with the IRAM NOEMA Interferometer. IRAM is supported by INSU/CNRS (France), MPG (Germany), and IGN (Spain).