dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion We conducted various experiments using mono-modality and multi-modalities to validate our dataset and to open perspectives the way for future research. The fusion levels exploited in our work are the following: low, intermediate and late levels. We can recognize the low level fusion when projecting narrow band data into UWB signals to define ROIs. The intermediate fusion consists of concatenating temporal and spectral features for acoustic data. Lastly, the late level is exploited in decisions fusion to obtain the final decisions of the total framework. From analyzing the fusion results presented in Table  7 , we notice that the performance has been clearly improved in terms of precision. The enhancement brought along with the acoustic system has a higher importance compared with the contribution of the radar-based system.
This is mainly because of the range and power limitations of the UWB radar. Despite this fact, it provides a unique signature for each type of object with a low price compared to the new sophisticated radars. For the acoustic system, the distance between the obstacle and the sensor presents an important challenge. Moreover, obstacles like pedestrians and cyclists have low magnitude acoustic signals and could not easily detected through acoustic based systems. The considered environments in OLIMP are challenging and present various confusing categories such as metal infrastructure, trafic signs, glass-surface buildings, etc. The obtained results for object detection are promising and show the importance of using multimodality for vehicle environment perception. To the best of our knowledge this is the first dataset that has exploited ultra UWB technology and acoustic data this shows the originality of our work.
Conclusions In this paper we propose OLIMP, a multimodal dataset for environment perception. It includes four modalities: images, ultra wide band radar signatures, narrow band data streams and acoustic data. Further, the acquired data is synchronized and the annotation process is provided for RGB images. This dataset unprecedentedly introduces ultra wide band radar and acoustic sensor. The proposed dataset was captured in various environments and dedicated mainly to dense urban traffic situtations. To demonstrate the effectiveness of our dataset, we presented a fusion framework which takes advantage of the results obtained using each modality separately. In spite of its simplicity, the proposed framework yields promising improvement in terms of precision. These experiments highlight the relevance of the proposed modalities.
LISA Pathfinder achieved residual acceleration noise levels of 1. 74 ± 0. 01 fm s -2 Hz -1/2 between 2 mHz and 20 mHz, and 60 ± 10 fm s -2 Hz -1/2 at 20 µHz  (Armano et al. 2016 (Armano et al. , 2018b)) , which went beyond the required perfomance and, hence, successfully demonstrated the technology to detect gravitational waves in space. Apart from achieving a high level of free-fall, it is also important to understand the different contributions that will build up the instrument noise model. Therefore, one of the main objectives of LISA Pathfinder is to split up the noise into different contributions and help on the design of a suitable environment for future gravitational-wave detectors. With that aim, LISA Pathfinder carried the so-called Data and Diagnostics Subsystem (DDS) which includes a temperature measurement subsystem  (Sanjuán et al.
All magnetometers channels are connected to a multiplexer after passing through an analog low-pass filter. The signal is then amplified by means of discrete operational amplifiers (OP471AY/QMLR) in an instrumentation amplifier configuration. Finally, it is digitally converted by a 16 bit analog to digital converter (7809LPRPFH). The sampling frequency is 50 Hz. Hence, since the input is multiplexed by 16 channels, the individual channel sampling frequency is 3. 125 Hz. In section 3. 2. 3 we will come back to the two last components of the read-out chain, instrumentation amplifier and analog to digital converter, which determine the noise performance of the magnetometer read-out. In terms of data handling, the magnetometers data were downloaded with a dedicated telemetry package, which also included all the diagnostics on-board the satellite.
IN-FLIGHT MEASUREMENTS After a one-month cruise phase, LISA Pathfinder reached the L1 Lissajous orbit in January, 2016. The magnetometers on-board started collecting data on January 11th, at the start of the LTP commissioning phase and, apart from some short outages events, they acquired data uninterruptedly until the end of the scientific operations in July, 2017. Contrary to many other missions that carry their magnetometers at the end of a long boom to isolate the measurement from spacecraft interferences, magnetometers onboard LISA Pathfinder are located inside the spacecraft with the main objective of monitoring the magnetic field as close as possible to the test mass position. At the same time, the magnetometers have to be sufficiently far away from the test masses since, as explained before, the fluxgate head contains a magnetically active component that could induce spurious forces on the test mass, therefore disturbing the main scientific measurement of the mission.
1998 ) in the same period of time. The ACE mission monitors different parameters of the solar wind by means of a suite of instruments on-board and, as LISA Pathfinder, follows a Lissajous orbit around L1. It is therefore a useful dataset with which to compare our measurements. ACE data is shown in Geocentric Solar Ecliptic Coordinates (GSE) system, which has its X-axis pointing from Earth towards the Sun and its Y-axis is chosen to be in the ecliptic plane pointing towards dusk. It is worth mentioning that even though both satellites are orbiting around L1, the distance separating them can be of the order of 10 5 -10 6 kilometers. However, we can safely compare the fluctuations of the magnetic field between both satellites at frequencies around the 20-50 µHz, which is the band in which we will focus our analysis.
Non-stationarities in the magnetic field fluctuations Until now we have based our analysis on the amplitude spectral density, which entails the information of the magnetic field in the frequency span of interest. The amplitude spectral density effectively describes the fluctuations during a fixed period of time. Hence, it would only be a complete statistical description if the environment on-board LISA Pathfinder were stationary. This is obviously not the case. On the contrary, several situations can induce abrupt changes in the measured magnetic field. In Table  2  we provided a series of events that we identified in the magnetic field timeline. Since these were associated to satellite operations these events are both easily identified and, eventually, removed from the dataset through post-processing.
When we analyze the parameters corresponding to the lower frequency part, we find that the spectral index E values are around 1, which are in good agreement with the f -1 behaviour of the interplanetary magnetic field fluctuations at this frequency range -even though the errors are high because we do not have many points for the fit. Finally, the values obtained for the parameter D are not very well fitted, probably because of the small amount of data points available in this frequency range. To finalise the study of the non-stationarity of the magnetic field fluctuations, we took a closer look at the impact of this contribution during the noise performance runs. Analogously as it is done with on-ground gravitational-wave detectors during science runs, in LISA Pathfinder these runs were periods where the instrument was configured in its more sensitive configuration and kept in data acquisition mode without introducing any calibration signal.
In the same panel, we also show the magnetic field B z component as measured in LISA Pathfinder during the same period. The reason to compare LPF's B z in LPF reference frame with ACE's B x in GSE coordinates is that LPF's Z-axis points always towards the solar panel, which is always pointing towards the Sun. Thus, LPF positive Z-axis is roughly equivalent to positive X-axis in GSE coordinates. To do this comparison, LISA Pathfinder data has been low-pass filtered at 1 mHz in order remove the higher frequency noise coming from electronics and the mean substracted in order to exclude the DC magnetic field coming from the spacecraft components. As we previously showed when analysing the components in the spectra, the main features of the long term evolution are driven by the interplanetary component.
ACKNOWLEDGMENTS This work has been made possible by the  LISA Pathfinder mission , which is part of the  space-science program  of the  European Space Agency. The French contribution has been supported by  CNES  ( Accord Specific de  projet  CNES 1316634/CNRS 103747 ), the  CNRS , the  Observatoire de Paris  and the  University Paris-Diderot. E. P. and H. I. would also like to acknowledge the financial support of the  Uni-vEarthS Labex program  at  Sorbonne Paris Cite Ì A  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). The Albert-Einstein-Institut acknowledges the support of the  German Space Agency, DLR. The work is supported by the  Federal Ministry for Economic Affairs and Energy  based on a resolution of the  German Bundestag  ( FKZ 50OQ0501  and  FKZ 50OQ1601 ). The Italian contribution has been sup-ported by  Agenzia Spaziale Italiana and Instituto Nazionale di Fisica Nucleare.
PN Controllers The Initial Marking The Incidence Vector  PN Controllers The Initial Marking The Incidence Vector 3 (-1, -2, 0, 1, 2) 2 (0, -1, 0, 0, 1) In fact, the JAVA application was developed to model any PN controller and then generate the corresponding LIST code that will be sent to the target in a second time. The interface is functional from the creation of the project until the code generation. It is possible to save a project, to model the controlled PN as in any editor, and to associate the inputs to their addresses in the PLC. Then, the generated code (Figure  7 ) will be tested using the SIEMENS STEP7 compiler (Figure  8 ). The proposed policy was implemented in the flexible manufacturing system installed in our laboratory in France and the computation of PN controllers was done by using CPLEX software.
The results analysis showed that our control approach using PN was more effective than previous methodologies based on the theory of regions. Indeed, for the same number of controllers, one can supervise the system under less computational burden. Our proposed control approach was merged with mathematical modeling tools to face one of the real constraints in the industrial world. The proposed control policy was implemented in the FMS through a JAVA application (Figure  6 ),  (Rezig et al. 2018)   [23] , wherein the controlled PN model was edited and then transformed into a comprehensible code supported by the Programmable Logic Controller (PLC). Once the internal input/output signals of the PLC are detected, one can transform the PN model into a language supported by the PLC. The LIST language of IEC norm 61131-3 was chosen.
In fact, the JAVA application was developed to model any PN controller and then generate the corresponding LIST code that will be sent to the target in a second time. The interface is functional from the creation of the project until the code generation. It is possible to save a project, to model the controlled PN as in any editor, and to associate the inputs to their addresses in the PLC. Then, the generated code (Figure  7 ) will be tested using the SIEMENS STEP7 compiler (Figure  8 ). We note that the results obtained with CPLEX and Xpress were nearly the same for the case of 15 products. For the case of 20 and 25 products, we note that CPLEX was faster with the same given optimal solution. Moreover, in the case of 50 products, CPLEX reached the optimal solution within a few seconds, whereas Xpress spent much more time before getting a solution, which was not the optimal one.
Steep topography, a tropical climate, and mafic lithologies contribute to efficient chemical weathering and carbon sequestration in the Southeast Asian islands. Ongoing arc-continent collision between the Sunda-Banda arc system and Australia has increased the area of subaerially exposed land in the region since the mid-Miocene. Concurrently, Earth's climate has cooled since the Miocene Climatic Optimum, leading to growth of the Antarctic ice sheet and onset of Northern Hemisphere glaciation. We seek to evaluate the hypothesis that the emergence of the Southeast Asian islands played a significant role in driving this cooling trend through increasing global weatherability. To do so, we have compiled paleoshoreline data and incorporated them into GEOCLIM, which couples a global climate model to a silicate weathering model with spatially resolved lithology.
Paleoshorelines To determine the position of paleoshorelines in the SEAI over the past 15 m. y. , we use terrestrial and marine sedimentary deposits (Fig. 2 ; SI Appendix). The paleoshoreline data indicate that the Sunda-Banda Arc and New Guinea are primarily responsible for the increase in area since 15 Ma. Exhumation of the modern Sunda-Banda Arc is the result of ongoing arc-continent collision with the Australian Plate  (37). Most of Sumatra and Java along with the non-volcanic islands of the Outer Banda Arc were elevated above sea level after 5 Ma  (38). In New Guinea, emergence in the mid-Miocene is associated with collision between the Melanesian Arc and Australia's distal margin  (39) , which drove exhumation of the Irian-Marum-April Ophiolite Belt. Exhumation accelerated over the past 4 m.
Materials and Methods The code for the GEOCLIM model used in this study can be found at: https://github. com/piermafrost/ GEOCLIM-dynsoil-steady-state/releases/tag/v1. 0. The code that generated the inputs and analyzed the output of the GEOCLIM model can be found at: https: //github. com/Swanson-Hysell-Group/GEOCLIM_Modern. GEOCLIM Silicate Weathering Component. The silicate weathering component of the GEOCLIM model has been modified from the previously published version  (20). The new component implements the model of  Gabet and Mudd (2009)   (17)  for the development of a chemically weathered profile. We refer to this chemically weathered profile as regolith where the base of the regolith is unweathered bedrock. In the model of  Gabet and Mudd (2009) , material enters the regolith and leaves either as a solute through chemical weathering of the material during its travel from the bedrock towards the surface, or as a physically weathered particle once it reaches the top.
The majority of the original 93,600 parameter combinations produce CO 2 consumption maps with poor fits to the measured watershed data (Fig. S3 ). Given that the coefficient of determination (r 2 ) is calculated using Equation rather than fitting a linear model, many of the combinations associated with particularly poor fits result in negative r 2 values (Fig. S3 ). However, given the right permutation of parameters, GEOCLIM produces CO 2 consumption maps that fit the measured watershed data reasonably well. We eliminate all parameter combinations Park et al. that produce a r 2 ≤ 0. 5, which leaves 573 unique parameter combinations (Figs. S3  and S4 ). Global CO 2 consumption calculated from these 573 parameter combinations ranges from 3. 7×10 12 mol/yr to 6. 3×10 12 mol/yr, with a mean of 5.
Southeast Asian Islands Scenarios We use geological data to quantify the changes in the area of the SEAIs over the past 15 m. y. (described in detail in Paleoshoreline Reconstruction and Geological Synthesis). To generate the lithologic map used in the "removed SEAIs" scenario (Fig. S7 ), we remove all land associated with arc-continent collision in the SEAIs. To generate the lithologic map used in the "paleo-SEAIs" scenarios (Fig. S7 ), we take the estimated paleoshorelines of the SEAIs for 15, 10, and 5 Ma, then remove all land that falls outside of these bounds. The slope and lithologic classification of each pixel is identical to that of the present day, provided that these pixels fall within the estimated paleoshorelines of the SEAIs for their respective time slice. This means that while we are changing the amount of emergent land, we are not changing the latitudinal or longitudinal position and are keeping the slope of emerged pixels the same.
Given that similar distributions of lithology and slope are reasonable in the past and that latitudinal translation has been relatively minor, we consider this simplification to be reasonable to first-order (Fig. S10 ). We then add land in the region that is not exposed today. The assignment of lithology, runoff, and slope for pixels on the Sunda Shelf is not trivial, given that the shelf is not currently exposed. As discussed in Paleoshoreline Reconstruction and Geological Synthesis, the Sunda Shelf is a flat-lying, relatively stable platform of continental crust. Islands between Malaysia/Sumatra and Borneo are composed of granite and are surrounded by shallow marine siliciclastic sediments  (Darmadi et al. , 2007; Hall, 2009 Hall, , 2013b)). However, basement highs of granite do not appear further to the northwest in the Gulf of Thailand, and seismic and drill core data suggest siliciclastic fluvial systems draining out toward the South China Sea when the Sunda Shelf was exposed  (Darmadi et al.
y. ago, but are currently submerged, we take the same approach as that described for the Sunda Shelf to generate the runoff field. For the slope and lithology, we take the mean slope and Ca+Mg concentrations from neighbouring pixels. We also remove the Greenland ice sheet in the "paleo-SEAIs" scenarios, to be consistent with Northern Hemisphere ice sheets developing after 5 Ma  (Haug et al. , 2005). The runoff field over Greenland is generated using the same method as that for the Sunda Shelf. For the slope, we take the mean slope value (0. 0529 m/m) of land for which slope data exists around the edges of Greenland, and assign this value to all pixels of Greenland covered by the ice sheet. We also assign the Ca+Mg concentration of bulk continental crust to these pixels. To explore the sensitivity of our results to the inclusion of the Sunda Shelf and the removal of the Greenland ice sheet, we also tested "paleo-SEAIs -Greenland" scenarios for 15, 10, and 5 Ma, which are identical to that of the "paleo-SEAIs" scenarios without the removal of the Greenland ice sheet.
y. are shown in Figure S11  (Bereiter et al. , 2015; Foster et al. , 2017; Ji et al. , 2018; Cui et al. , 2020). Even when stringent quality criteria and the latest understanding of each of the pCO 2 proxies have been applied to available pCO 2 records, both significant uncertainty in the estimated pCO 2 for any given data point as well as  Park et al. 18  disagreement between techniques remain. Statistical methods that are often utilized in an attempt to extract trends from this data (e. g. locally-weighted scatter plot smoothing, LOWESS) evaluate the mean (with some weighting) of the highly scattered data in a given time interval. However, such methods are only appropriate for estimating the "true" value at any given time interval in datasets in which individual data points are being drawn from a single distribution about a mean value.
Paleoshoreline Reconstruction and Geological Synthesis Geological maps, stratigraphic data, and previous paleoshoreline compilations were used to calculate the changes in different types of subaerially exposed rocks in the SEAIs. Following  Molnar and Cronin (2015) , we analyzed the area changes of islands that are larger than ∼200 km 2 (Fig. 1  in main text). We also included changes in areas of submerged continental shelves that were previously exposed, like the Sunda Shelf. Larger islands were further divided into regions based upon their position on microplates as defined by  Matthews et al. (2016)  Ma), but we recognize that the biostratigraphic resolution is an additional source of uncertainty. Several new sources for paleoshoreline data have become available since  Molnar and Cronin (2015).
ACKNOWLEDGMENTS. Collaborative research between N. L. S. -H. and Y. G. was initially supported by a grant from the  France-Berkeley Fund. N. L. S. -H. and F. A. M. gratefully acknowledge support through  NSF   FRES grants  # 1926001  and # 1925990. We thank  Alec Brenner ,  Sam Lo Bianco ,  Mariana Lin , and  Judy Pu  for their data compilation contributions to the paleoshoreline reconstructions.
The decision to sequence exomes or whole genomes is further confounded as more recent publications in oncology select either WGS  [16] [17] [18] [19] [20]  or WES  [21] [22] [23] [24]. Recognizing the unresolved nature of this issue, Schwarze et al. have called for more comprehensive studies comparing the WES and WGS studies, especially as this issue has important ramifications for the clinic  25. Our analysis provides confidence that mutation calls within the captured exonic regions of these two data sets are largely consistent. We highlight common sample, cohort, and caller-specific challenges in cancer variant detection from the TCGA and ICGC efforts. We show that variants that are most confidently called in one database i. e. , called by multiple callers, are very likely to be called in the other.
One such example subsumed two mutations on chromosomal location 3p21. 1 (genomic locations chr3:52442525 and chr3:52442604) that were annotated as missense mutations of BAP1 by MC3, but as 5'Flank SNPs of PHF7 by PCAWG. While identical pipelines resolve such differences, we stress the potential for misinterpretations when combining these publicly-available datasets. Effects of software. Another important issue we assess is the degree to which differences in bioinformatics pipelines impact concordance. We extracted calls from MuSE and MuTect, both of which were executed on each dataset, and examined 6 subsets of results: MuSE-only-calls and all calls save MuSE-calls (the complement), MuTect-only-calls and their complement, and MuSE + MuTect calls and their complement. MuSE and Mutect each generate around 95% of the total calls, of which each respective subset shows close to 80% concordance between WES and WGS (Supplementary Fig.
5 ). These call sets themselves overlap almost completely, with their combination (MuSE + MuTect) giving a marginally higher concordance. Conversely, the data-specific caller combinations (referred to above as the complements) each furnish small call sets which vary considerably between WES and WGS (concordance as low as 15%). Because of the vast difference in the sizes of the MuSE/MuTect and the complementary call sets, there is little difference in the original analysis versus analyses restricted to variant callers common to both platforms. Differences in software pipelines do not appear to be significant confounding factors in concordance here. Effects on higher-level analysis. We also sought to assess how higher-level analyses might be impacted using mutation signature analysis as a representative.
Therefore, we sought to test whether the trinucleotide context of variants correlated with relative call-rates in MC3 and In addition, if one's hovers over a point on the scatter plot, a pop-up window will automatically display, providing the user with basic statistics used to calculate that point, i. e. , total number of mutations, number of unique and matched mutations. c A table is also presented based on the selection criteria in panel a. PCAWG. Before applying the MC3 OxoG filters, we found a huge predominance of CA variants unique to MC3, with the trinucleotide contexts most specific to one database or another being 7-9 times more specific than the least specific trinucleotide contexts. After applying the MC3 OxoG filters, nucleotide contexts differed by less than four-fold in their specificities.