dataesr/scientific-paragraphs-categorization huggingface dataset:
Toward emulating scientific deep learning workloads that are becoming popular on HPC systems, DLIO  [55]  provides an I/O benchmark suite. DLIO supports various scientific deep learning applications, including Neutrino and Cosmic Tagging with UNet, FFN (Distributed Flood Filling Networks), CNN (Convolutional Neural Networks), CosmoFlow for cosmology datasets, FRNN (Fusion Recurrent Neural NetN), and CANDLE (Cancer Distributed Learning Environment). DLIO allows reading data from different file formats and APIs, such as HDF5, CSV, and tfrecord formats.
Scalasca allows post-mortem analysis of event traces and automatically detects performance-critical situations. It is also possible to use the TAU visualization toolset I/O Access Patterns in HPC Applications: A 360-Degree Survey 46:25 Fig. 15. Two concurrent IOR instances using DXT Explorer. We depict the access pattern from the high-level library (HDF5) and their corresponding transformations until they reach the POSIX layer and the underlying OSTs in Lustre. to correlate performance data collected with Score-P or Vampir, which works as a post-mortem interactive event trace visualization software. DXT Explorer  [19]  is an interactive web-based log analysis tool to visualize Darshan DXT traces and help in understanding the I/O behavior of applications. The tool adds an interactive component to Darshan trace analysis that can aid researchers, developers, and end users to visually inspect their applications' I/O behavior, zoom in on areas of interest, and have a clear picture of where the I/O problem is.
Both instances were started simultaneously. We collected profile and tracing data using Darshan. As Darshan Extended Tracing does not yet capture fine-grained information about high-level libraries, such as HDF5, we rely upon manually instrumenting the code to collect timestamps before performing the write operations and after the dataset is completely written to a given file. We condensed both plot facets as all ranks collectively issued the I/O calls to the MPI-IO layer. We represented these collective calls by the star symbol on the y-axis. As far as the application is concerned, its data in memory is a 1D dataset represented by HDF5. HDF5 will define a hyperslab based on the start offset, count, stride, and block to access the data. A hyperslab represents a portion of the datasets that can be a logically contiguous collection of points in a dataspace or a regular pattern of points or blocks in a dataspace.
In our experiments, for a shared file, IOR defines the start offset as offset module segmentSize, count as 1, and a stride and a block equal to the transfer size (i. e. , 4 MB). However, once the requests reach the MPI-IO layer, they are further broken down by the four collective aggregators into a larger number of 1-MB POSIX requests, considering the underlying PFS striping configuration before sending them to each storage device. We have defined Lustre to use 1-MB stripes over eight servers to make it easier to visualize. Once we delve into lower levels of the I/O stack, we are to lose contextual information from the applications and start to observe the effect of natural interference in this shared storage infrastructure. For instance, if we glance at one OST, the requests arrive at the storage servers in an interleaved fashion, coming from the two applications that the file system is unaware of.
ACKNOWLEDGMENTS The work presented in this article was the result of a collaboration between the  Myriads  project team at  Inria  and  Lawrence Berkeley National Laboratory  in the framework of the Hermes Associate team. This research was supported by the  Exascale Computing Project  ( 17-SC-20-SC ), a collaborative effort of the  U. S. Department of Energy Office of Science  and the  National Nuclear Security Administration. The research used resources of the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the U. S. Department of Energy  under contract number  DE-AC02-05CH11231. This research also used resources from the  Oak Ridge Leadership Computing Facility  at the  Oak Ridge National Laboratory , which is supported by the  Office of Science of the U.
EGSnrc The simulations performed using EGSnrc consisted of two parts, using the beamline simulation code (BEAMnrc) and the dose deposition simulation code (DOSXYZnrc). At first, a phase space was obtained using BEAMnrc, then the dose was calculated using DOSXYZnrc in the water phantom. The voxel size was adjusted to closely match the dots per inch (DPI) scan setting of the gafchromic film and the film thickness. With BEAMnrc, we modeled the accelerator optics starting from the titanium window down to the tungsten template. To include all the beam optic materials, we expanded the default International Commission on Radiation Units and Measurements (ICRU) material data file (ICRU521) using the EGSnrc graphic interface.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. We thank  Fondazione Pisa  for funding CPFR with the grant "prog. n. 134/ 2021. " The research is also partially supported by  INFN CSN5  funded project " FRIDA. " Additionally, the research has received funding from the  European Union-NextGenerationEU  through the  Italian Ministry of University and Research  under  PNRR-M4C2-I1. 3  Project  PE_00000019  " HEAL ITALIA. ".
OptoBox The OptoBox, (a. k. a. The Light-Plate Apparatus  (Gerhardt et al. , 2016) -Figure  1A ) was developed to illuminate 24-well imaging plates in a shaking incubator. Each well of the OptoBox contains two interchangeable LEDs connected to a Printed Circuit Board (PCB). Although the two LEDs can be different in order to be compatible with various optogenetic systems, we connected two blue LEDs (461 nm) to maximize light exposure for the EL222 optogenetic system. Programing of the device is particularly easy: the program can be set up online using the user-friendly software Iris, uploaded to an SD card, and plugged into the PCB. The programming consists of assigning each of the two LEDs an arbitrary value between 0 and 4,000 (for a given illumination pattern), corresponding, in our case, to an intensity of 0-4 mW/cm 2 per LED (Supplementary Figure  S1 ).
In terms of software, we developed a Node-Red program (detailed in Supplementary File 2 and available on Github) to communicate with the Arduinos in real-time, via the Firmata Arduino Template. This software handles the user interface used to set up and launch experiments, PID used to control temperature, illumination control and data-output. Using this platform containing 16 functional units and an efficient userinterface, we are able to quickly and easily vary multiple parameters at the same time  (illumination, volume, stirring, temperature, strain)  and thus achieve a high experimental throughput.
OptoTubes LEDs are encased in a box at regular intervals (4 x 2 LEDs) and the tubes are held on top, with their bottom almost touching the LED, via a 3D printed holder (Supplementary Figure  S2 ; Supplementary File S1). LEDs are directly connected to an Arduino PWM pin and were measured to result in a LED intensity of 12 mW/cm 2. Pictures and schemes are shown in Supplementary Figure  S2. To run an experiment, 3 mL (generally) of inoculated medium is placed into a 14 mL glass test tube. The tubes are closed with a cellulose cap, placed in the OptoTubes device, and the Arduino (previously programmed with the Arduino software for the chosen light intensities) is connected to 12 V power supply. The device also sits in a 30 °C incubator shaking at 250 rpm. eVOLVER eVOLVER was built following the instructions of  (Wong et al.
, 2018). To launch an experiment, autoclaved vials are filled with inoculated medium and closed with a simple lid or with the customlid (briefly detailed below and more extensively described in Supplementary File 2). The lid is loosely placed on top of the vial, loosely screwed on, to allow for gas exchange. The vials are then placed in the aluminum sleeves (which are used for temperature control) of each eVOLVER unit. There, the additional LEDs, are placed and stacked by hand into each custom-lid. Using Node-RED software on the user interface, the culture parameters can be set (stirring, illumination from the side-LED, temperature), measure cycles modified, and names and notes added for each unit independently. The lid of the glass vial used in eVOLVER is a hollow cap, normally closed with a removable silicone septum.
Microscopy Bright field (BF) and fluorescence images were acquired with MetaMorph software from an Olympus IX83 inverted epifluorescence microscope paired with an Andor CMOS Zyla camera using an 100x objective. Samples were illuminated using a CoolLED pE4000 fluorescence lamp. An exposure time of 200 ms was used to acquire BF images. Images of GFP fluorescence were acquired using excitation and emission wavelengths of 470 and 525 nm, respectively, and an exposure time of 250 ms at 25% light intensity. To acquire images, a small volumes of culture were centrifuged and the cells were simply loaded into a custommade PDMS chip  (Llamosi et al. , 2016)  to obtain a monolayer of cells. The chip was then placed under the 100x objective for image acquisition. are available in the following Github repository: https://github.
5 s; echo time: 76. 1 ms; flip angle: 90°; voxel size: 1. 97 × 1. 97 × 2 mm; matrix: 112 × 112 × 48; 64 independent directions; b-value: 3000 s/ mm 2 ). The acquisition time was 12 min and was repeated five times for averaging, for a total acquisition time of 1 h. We applied an LPCA filter combined with a Rician noise model  (52)  on MATLAB (MathWorks, Natick MA, United States) to denoise the raw diffusionweighted data. Using DSI studio  (53)  we then corrected these data for geometric distortion caused by eddy currents. We implemented the C++ ANTs toolkit  (54)  to register the T2-weighted images to b0 images for the purpose of anatomical referencing and threedimensional rendering of the brain.
For example, the internal capsule may contribute to the one-humped camel's ability to navigate challenging terrains, since the internal capsule works as a relay station for the body's motor fonction  (61). Differences concerning our DTI tractography findings relate to the cingulum. Our fiber dissection results show a curved band of neural tissue located in the cerebral cortex, encircling the corpus callosum with parietal and occipital radiations also described by  Pascalau et al. in 2016. For instance, we failed to observe in DTI tractography theses parietal and occipital radiations. Moreover, only a small part of the cingulum attached to the corpus callosum could be reconstructed. DTI tractography has a major advantage over post mortem anatomical techniques such as fiber dissection, as it allows for noninvasive dissection, yielding a three-dimensional representation of several white-matter tracts from a single imaging dataset  (1, 3, 62).
Higher up in GT, ChemCam data indicated significant gradients in the copper concentration in coherent bedrock on a local scale of only a few meters, which suggests a different alteration style and possibly different types of diagenetic fluids. Plain Language Summary Gale crater, Mars, about 152-km in diameter and 3. 6 Ga in age, has a central mound that is partly of sedimentary origin. To date (July 2022), the NASA rover Curiosity has been exploring the crater floor and the lower-most 600 m (in elevation) of sediments of that mound. ChemCam, an instrument mounted on top of the remote-sensing mast utilizing Laser-Induced Breakdown Spectroscopy, has been measuring chemical composition and specifically copper abundances along the rover traverse. We identified 10 areas of copper enhancement along Curiosity's traverse.
Data and Methods The ChemCam instrument onboard Curiosity  (Maurice et al. , 2012; Wiens et al. , 2012)   et al. , 2017; Gasda et al. , 2021; Wiens et al. , 2013)  and for some trace elements  (Li, Rb, Sr, and Ba; Ollila et al. , 2014; Payré et al. , 2017). At each point on the Martian surface that is sampled by the ChemCam laser (λ = 1,067 nm), the spectrum of light emitted by the laser-generated plasma cloud is recorded by detectors in the ultraviolet, violet, and visible/near-infrared regions (from here on referred to, respectively, as UV, VIO, and VNIR). Radiance data acquired without laser action can be converted into relative reflectance spectra that are referred to as "ChemCam passive spectra" and provide additional information on the LIBS-analyzed targets  (Johnson et al. , 2015). Besides the LIBS-related modules, the ChemCam instrument suite includes the Remote Micro-Imager (RMI) that provides high-resolution (gray-scale) images of the LIBS-analyzed targets  (Le Mouelic et al.
, 2015). Cu has 2 major emission lines, centered respectively at λ = 324. 789 and 327. 428 nm (according to the current ChemCam wavelength calibrated to vacuum,  Clegg et al. , 2017; Wiens et al. , 2013). Each Cu line overlaps strongly with three other (mostly titanium) emission lines, resulting in the presence of two broad emission bands. From here on, these two lines/bands are referred to as "Cu324" and "Cu327" or as "left band" and "right band", respectively. Prior to the analysis of these bands, the spectra were normalized to the spectral total in the VNIR spectrum. Among the different models for normalization  (Fabre et al. , 2014; Ollila et al. , 2014) , the VNIR normalization (also applied to quantification of boron,  Gasda et al. , 2017)  provided the most consistent data set in the present case of Cu analysis.
Further improvement of data analysis, especially in multipeak fitting and fit constraints, will likely decrease Cu's limit of detection (LOD) and increase the number of Cu-bearing targets in Gale crater. In the context of this work, "Cu-bearing points" are those that contain Cu above ChemCam's LOD, which is ∼50-100 ppm, depending on the presence of emission lines from other elements (the limit of quantification [LOQ] is ∼200 ppm  (Payré et al. , 2019) , again depending on the presence of other elements). In most ChemCam spectra, no Cu is detected. This indicates that (a) ChemCam's detection limit for Cu is not particularly low in comparison to standard laboratory techniques and (b) a target where Cu is detected by ChemCam may be termed "Cu enriched Figure  2  plots the Cu327 areas for all Cu-bearing points in the sol range 0-3007 as a function of elevation.
In addition, the running average of these areas was computed within 7. 5-m wide elevation intervals, scaled up by an arbitrary factor (i. e. , a factor of 3. 2) and plotted as a thick red line in Figure  2. Whenever that scaled running average exceeded a threshold of 3 10 -5 , the corresponding cluster of Cu-bearing ChemCam points was interpreted to represent a "region of enhanced Cu abundance". The data analysis presented in this manuscript covers all types of surface material, including bedrock, float rocks, and diagenetic features although some focus is on bedrock. Only ChemCam data of "good quality" were taken into account. The standard checks used to validate ChemCam LIBS data for archiving in the Planetary Data System were used. These include thresholds for total optical emission, distance, and checks on the focus quality.
We have identified ∼360 Cu-enriched ChemCam points (∼120 in the GT area). The associated peak areas (Cu324 and Cu327) were plotted against each other and fitted by a straight line with slope 0. 996 and correlation coefficient R 2 = 0. 61 (Figure  S2  in Supporting Information S1). Based on the NIST Atomic spectra data base  (Kramida & Ralchenko, 1999) , a slope of ∼1 is expected, since both Cu I emission lines listed in that database have similar relative strength. Of particular importance for this work are ChemCam's major element oxide predictions (below referred to as MOC [Major Oxide Composition] data). In this work, the latest calibration for MOCs includes a distance correction for SiO 2 , Al 2 O 3 , Na 2 O, and K 2 O  (Wiens et al. , 2021). The sums of major oxides (usually referred to as "totals") are of particular importance as they are a proxy for missing compounds, that is, compounds that are not currently quantified by the routine ChemCam data processing pipeline  (Clegg et al.
, 2017). In this study, it was noted that the Cu324 and Cu327 peak areas are not consistent with each other for ChemCam points with totals <50 wt%, that is, the Cu324 area remains close to zero independent of the corresponding Cu327 area. This implies that these points are actually not enriched in Cu. Therefore, ChemCam points with totals <50 wt% were discarded from the present study. All data tables in this manuscript are sorted according to stratigraphic sequence. There is some (limited) overlap between different data tables in this manuscript in order to demonstrate how they are linked to each other. An effort has been made to ensure consistency with similar data tables and results in  Dehouck et al. (2022). Finally, a note on the term "bedrock" as it will be used in this manuscript: Following  Dehouck et al.
Detection of Copper in the Kimberley Area The Cu points located in the Kimberley area have been analyzed by  Payré et al. (2019). Here, we address a single aspect of that data set that will be instructive when discussing Cu abundances in GT. The detailed local stratigraphy for the Kimberley area was worked out by  Rice et al. (2017). The two lower-most m of that stratigraphic column contain four members, referred to as Point Coulomb, Liga, Square Top, and Dillinger, respectively (Figure  3 ). The plots in Figures  3a  and 3b  (MnO abundance vs. Cu327 area, Cu324 vs. Cu327 areas) display strong geochemical differences between Liga and Dillinger. Cu-enriched Liga points were obtained on poorly sorted sandstone and conglomerate in the Liga member. However, Cu-enriched Dillinger points were not obtained on the fine-to medium-grained Dillinger sandstone, but mostly on fracture fills in that member.
It also shows, although at low statistics, that Cu-and Ni-bearing ChemCam points have Fe abundances close to Mt. Sharp average. Gretna Green ccam is located in a field (labeled "a") in that diagram (Figure  5 ), where we may expect more chondritic meteorites. The fifth column of    1  in  Payré et al. , 2019). Two outliers (Wallal and Blina, black arrows) have been ignored for the linear fit. (b) Cu324 versus Cu327 peak areas with a linear fit through all pointsincluding the outlier marked by black arrow (discarding that outlier would result in R 2 = 0. 95). The slope of that line is close to 1. 0, which is the value expected from the NIST data base. The final part of this section will focus on bedrock (including Cu-bearing bedrock) in Jm/GT and KHm. Figure  6a  conveys a qualitative impression of the relative importance (in area coverage) of coherent and rubbly bedrock in Jm/GT, while Figures  6b-6e  shows their Harker diagrams for Mg, K, Fe, and Mn.
These data clouds serve as a reference frame that allows us to put the Cu data into context. Figures 7b-7e demonstrates the following geochemical trends: bc-MA < bc-GE < br (for Si), bc-MA < bc-GE < br (for Fe and K), and bc-MA > bc-GE > br (for Mg and Mn). These inequalities imply the following trend for the ratio Fe/Mn: bc-MA < bc-GE < br. Thus, also in the KHm member, rubbly bedrock appears to be more altered than coherent bedrock. Again, as in the above-discussed case of Jm/GT, this statement is not supported by the CIA values, which are similar for both types of bedrock (Table  2 ;  Dehouck et al. , 2022). Comparing Figures 7b-7e to the analogous figure for Jm/GT (Figures  6b-6e ) we can see that KHm bedrock is more diverse than Jm/GT bedrock, that is, its ChemCam points cover a larger area in the Harker plots, even though their averages are similar to those obtained in Jm/GT.
The continued exploration of Mt. Sharp (beyond Sol 3007) may return further ChemCam-Cu data of that bedrock. If Cu enrichment is confirmed in that bedrock at a statistically significant level, then Cu-bearing Stimson sandstone can be compared to Cu-bearing Bradbury group sediments, since both types of sediments apparently share a similar basaltic source region  (Bedford et al. , 2020). In conclusion, we tracked Cu abundances along the rover traverse in the sol range from 0 to 3007 and identified different modes of Cu enrichment and redistribution. Building on  Payré et al. (2019) , we reanalyzed Cu enrichment in the Kimberley area (crater floor). Another focus of this work was the analysis of Cu enrichment in GT, some 300 m above Kimberley, where we used ChemCam Cu data to constrain the evolution of rubbly and coherent bedrock in the Jm and KHm.
The selected energy levels and impact mass are representative of the impact of a falling tool during an assembly or maintenance operation. The orientation of the upper plies of the specimen is used as a reference to determine the longitudinal and transverse directions. Fig. 4  depicts typical experimental force-displacement curves for a representative test at the four impact energies. All these curves were drawn as result of numerical integration of the force-time data recorded using a data acquisition system during each experimental test. For the plywood specimens, the force-displacement plot, shows an initial linear increase related to the contact between the impactor and the specimen. Beyond this range, a small plateau appears, indicating the crushing of cell walls due to radial compression and a gradual drop in force due to stiffness degradation caused by fibre breakage, transverse shear or debonding.
5 s mixing time CP PDSD experiment (Fig. 2a ), which probes intermolecular distances out to ~0. 7-1. 0 nm. Surprisingly, no interactions between callose and other secondary cell wall polymers (cellulose, xylan and lignin) were detected, even though the two cellulose domains clearly show cross peaks with one another. The cellulose domains 1 and 2 approximate to interior and surface residues  37, 41  and represent 39% and 61% of the cellulose in our dataset. The right-hand side of Fig. 2a  shows slices taken at the callose Cal4 shift at 68. 5 ppm (pink line), at the cellulose domain 1 C4 1 shift (88. 7 ppm, blue) and at domain 2 C4 2 at 84. 3 ppm (green) and 83. 7 ppm (purple). The C4 2 cellulose surface sites are clearly visible in the interior C4 1 slice at 88. 7 ppm and vice versa, whereas there does not seem to be any interaction visible in the callose Cal4 slice (peaks at ~101, 79, 53.
DNA constructs and transgenic plants The promoters of IRX3 (At5G17420), IRX8 (At5G54690) and ZCP4 (Zinnia elegans ZCP4 gene for cysteine protease, GenBank: AB264052. 1 (ref. 27)) genes were amplified by PCR and cloned using MultiSite Gateway (see Table  1  for primer list). For the pAtIRX8-XVE::cals3m estradiol-inducible construct, the promoters of IRX8 were amplified by PCR and cloned into P4P1RpGEMt containing the oestrogen receptor XVE  31, 32  by restriction ligation at the KpnI restriction site. Using the MultiSite Gateway system (Thermo Fisher), the promoters and their inducible versions were combined with the cals3m construct and the nopaline synthase terminator in destination vector pBm43GW or pHm43GW to produce the pAtIRX3::cals3m, pAtIRX8::cals3m, pZeZCP4::cals3m and pAtIRX8-XVE::cals3m constructs.