dataesr/scientific-paragraphs-categorization huggingface dataset:
SCA Maps Many applications using optical remote sensing to retrieve SCA maps in Himalayas are proposed in the literature  [73] [74] [75] , particularly for this Nepalese region using MODIS images  [5, 14]. The GR4J  [73]  or the J2000  [75]  hydrological models could be improved by using the SCA 5 m maps retrieved from the VENµS dataset thanks to their high spatial resolution. Indeed, the great asset of this SCA database retrieved from VENµS images relies on the high spatial (5 m) and temporal (2-day) resolution of this sensor. These SCA maps will be dedicated to the validation of the snow module from a distributed hydrological model DHSVM-GDM currently applied at IGE  [5, 13] , and actually only based on a daily MODIS 500 m images dataset. The very high temporal resolution of the VENµS satellite is an asset, especially in monsoon conditions, even if there are clouds.
Furthermore, such a database is of interest to the community studying the hydro-glaciological context of the region. In the recent study from Baba & Gascoin  [34] , an unsupervised classification method has been performed and compared to a supervised classification method to detect snow in VENµS images, showing that a supervised classification gives better results. Since one of the main limitations of this approach is the cloud detection (due to the absence of SWIR band on VENµS sensor) our CloudCov mask, with a 5 m resolution, could be applied ahead of the classification. Then, their method could be tested on this new product, to better discriminate clouds from snow and improve the computed SCA maps.
Conclusions The aim of this study was to evaluate and quantify the performance of different products retrieved by the CNES processing of the VENµS database over the Everest region in Nepal for glaciological and hydro-climatic applications. In such a high-mountain context, the focus was set on the interest in adding a fine DEM to the processing chain to enhance the pre-existing quality of the high temporal and spatial resolution from remote data provided by the VENµS sensor. The major findings are the following: (i) No consistent benefits for assessing the spatio-temporal evolution of surface albedo are retrieved using a cosine radiometric correction enhanced by a fine 5 m DEM regarding the Gamma one carried out with 90 m SRTM by CNES. The CNES FRE product offers efficient results versus in situ measurements for albedo retrieval, because Gamma correction takes into account both direct and diffuse illumination contributions.
This makes it easier to deploy and train ML models on mobile devices. The extensive software development environment made available to users by Nvidia contributes to the success of Nvidia GPUs for ML. In the widely adopted von Neumann architectures, ML workloads based on ANNs frequently perform multiply-accumulate operations, which generate multiple data movements between memory and processors. As a result of these exchanges, there is a high execution time and power consumption, and this is known as the "memory wall". Modern ML commodity chips combine CPUs with High-Bandwidth Memory (HBM) via efficient interconnects to address this problem. In parallel, an emerging paradigm, called near-data processing  [38] , has been studied to address the memory wall issue. Computing capability is built into the memory or storage, enabling data stored there to be processed.
The resulting classes are accompanied by some typical target algorithms that the corresponding device family can handle. Our proposed extension of the classification from Neto et al. [5]  is represented in Table  1. A total of six device classes are distinguished. The Class 0 devices are based on microcontrollers with limited memory capacity and power consumption. In general, the processed dataset is very small; for example, temperature and humidity measurements. Nevertheless, such devices can perform lightweight inference tasks using simple pretrained models. Hence, all the subsequent device classes can be used for inference. A Class 1 device is one that can store data in addition to collecting and processing data. Such devices generally run on monocore microcontrollers or application cores with larger storage and memory capacities.
Gained Insights and Discussion As a representative ultra-low-power device of the CYSmart system, the CYComs were the primary focus of the above use cases. There is also another component, the CYEdge, which embeds a Raspberry Pi 4 and a LoRa communication shield. Based on the power measurements of the CYEdge under normal operating conditions, it can be classified as a class 3 device, as described in Table  1. Below are some insights regarding CYSmart's current implementation and potential improvements. Gained insights. The CYCom component of the CYSmart system utilizes a commercialoff-the-shelf (COTS) microcontroller manufactured by STMicroelectronics. Choosing this approach reduces the cost of the component as well as the development time. A CYCom's CPU is the primary energy consumer in the aforementioned use cases, followed by the LoRa module and the sensor power supply.
Instead of analog sensors, digital sensors with internal 3. 3 V batteries could be considered here to reduce power consumption. Depending on the measurement environment, LoRa modules consume varying amounts of power. In both Cases I and II, the system can communicate across a reinforced concrete wall 90 cm thick with the initial parameters. In the case of a 15 dBm data transmission capacity and a spreading factor of 12, the maximum transmission delay is 2 s. Therefore, its maximum power consumption is 166 mW. These parameters can be adjusted according to the operational environment in order to reduce the LoRa module's power consumption. Comparison of CYSmart w. r. t. selected industrial solutions. As a mature low-power edge computing solution, the CYSmart system can be compared with a number of industrial technologies.
Simulation Overview We used two approaches to estimate the six functions π(X, S), µ 1 (X, S), µ 0 (X, S), π(X), m 1 (X), m 0 (X), yielding two different versions of our proposed estimator. In one, we used the Super Learner  [31] , which finds an optimal combination of a set of candidate models or learners, as implemented in the SuperLearner package  [22]  in R. We denote this estimator "DR-SL" in what follows. In the second version, which we call "DR-lasso" we use the relaxed lasso  [14] , as implemented in the glmnet package  [8] , to estimate all needed functions. For all simulations, we truncated estimates of the propensity and surrogate scores so that 0. 01 ≤ π -k (X i , S i ), e -k (X i ) ≤ 0. 99 for all i. It is our understanding that there are no currently available methods to estimate the PTE of a high-dimensional surrogate.
We include software to implement our proposed methods in the R package crossurr available at github. com/denisagniel/crossurr. independently generated as w ∼ N (0, 1), s ∼ N (0, 1), y ∼ N (0, 0. 25 √ δ + 1). The surrogate ψ a (s) was taken to be an estimate of E(Y |A = a, S = s) obtained as the predicted value from a least squares fit of Y on S in treatment group A = a from study 1. Figure  1  depicts δ values of 0 (PTE 0), 0. 25 (PTE 0. 2), 0. 5 (PTE 0. 33), and 3 (PTE 0. 75).
For ease of read, we annotated the y axis with the antilog values. Normality or log normality of all data distribution was tested using the Kolmogorov-Smirnov, d'Agostino-Pearson, or Shapiro-Wilk test in Prism 9 (GraphPad). When distributions are best fitted by a log normal distribution, statistical tests were performed on log-transformed data. Significance for datasets displaying normal distributions was calculated in Prism with unpaired (unless otherwise specified) two-tailed Student's t test or one-way analysis of variance (ANOVA) and Dunnett's multiple comparison test when comparing more than two conditions. Significance for non-normally distributed datasets were calculated in Prism using a Mann-Whitney test or a Welch's t test for different SD, or a Kruskal-Wallis test and Dunn's multiple comparison test when comparing more than two conditions.
45 (Olympus, Melville, NY) and a camera ORCA-Flash4 (Hamamatsu, Japan). The microscope is controlled with the software Metamorph (Molecular Devices, Eugene, OR). Differential interference contrast imaging is performed with a far-red filter in the illumination path to avoid CRY2 activation. Optogenetic stimulations are performed every 2 to 2. 5 or 5 min with a digital micromirror device (DMD) in epi-mode (DLP Light Crafter, Texas Instruments) illuminated with a SPECTRA Light Engine (Lumencor, Beaverton, OR) at 440 ± 10 nm. Total internal reflection fluorescence (TIRF) images are acquired using an azimuthal TIRF module (iLas2; Roper Scientific, Tucson, AZ). An automated tracking algorithm is designed in MATLAB with a feedback-loop routine to ensure that the optogenetic activation is maintained in the specific area of interest.
Cell segmentation for cell tracking in optogenetic experiments Movies are analyzed using custom-built routines in Fiji  (48)  and MATLAB (MathWorks, Natick, MA). ARHGEF11-CRY2-mCherry signal is used to segment clusters by applying a Gaussian filter and a threshold. Trajectories are then analyzed in MATLAB, taking the displacement of the center of each segmented cluster along the microchannel. The beginning of the activation is considered as time 0 and origin of the position for all the trajectories. Trajectories are sometimes flipped to put all the activation on the same side (left side of the cluster).
Analysis of bead displacements in TFM experiments Time-sequence images of fluorescent beads are aligned using the SIFT algorithm in Fiji  (49). Bead displacements between first and last time points are then calculated using the cross-correlation PIV plugin in Fiji  (50). The heatmap of bead displacements is coded in MATLAB. Maximum bead displacement is computed as the mean of 0. 2% highest displacement amplitudes (mean over at least three displacement vectors) from the PIV result on the entire image field. Variation coefficient is computed as the ratio of SD of bead displacement amplitudes over their mean. It characterizes the dispersion of the distribution of bead displacements by the cluster: The higher it is, the more localized bead displacements are. Orientation of bead displacement vectors is computed as the average of cos() = (u•v)/(||u||×||v||) where u is the vector pointing from the cluster centroid (computed using ROI analysis in Fiji) to the coordinates of each bead, v is the bead displacement vector, and ||u|| (resp.
Myosin flow analyses Myosin flows are analyzed from movies of median sections of HT29-MTX clusters stably expressing mTurquoise-MLC using a custom-built routine in Fiji  (48)  and MATLAB (MathWorks, Natick, MA). Images are acquired every 1 to 5 min during 20 min to 7. 4 hours and the MLC channel is used to map actomyosin flow dynamics in clusters. Background contribution is removed using a custom-built macro in Fiji before PIV analysis. A bounding rectangle is adjusted to the cluster shape at every time point to obtain the cluster centroid positions. The Stramer lab (King's College London, UK) PIV package, developed in MATLAB  (51) , is used to extract maps of myosin flow velocity vectors from the median section of clusters. The size of objects to be tracked is 4 m (50% wider than the cluster cortex), and the size of the analysis window is the value of the mean displacement of myosin between two consecutive frames (5 to 7 m).
A MATLAB code was developed to display and exploit the PIV analysis. Mean PIV map of myosin flows of all clusters was obtained in two steps: (i) averaging over time the PIV maps of individual clusters, centered around a reference position; and (ii) averaging the time-averaged PIV maps over the clusters. This leads to a timeaveraged mean PIV map, averaged over the whole population of clusters considered. Myosin flow kinetics are extracted at every time point from observation windows in contact with the channel walls (both clusters sides): These windows are 2 m wide, and we chose for each cluster at what distance to the ends of the cluster (front and back) they start and finish, to avoid considering the curvature of the clusters that is not in contact with the channel walls (Fig. 5F ). In the cluster reference frame, for each cluster i, the amplitude of the fluctuations of myosin speeds (along the x axis only, fluct i ) is calculated by subtracting the average velocity of myosin flows at the contact (averaged over space and time, _ v x ) to the instantaneous velocity of myosin of each pixel n (v x,n (t)), and taking the absolute value of this quantity.
Analysis of nuclei movements Median sections are imaged every 2 to 30 min for 20 min to 11 hours. For mTurquoise-MLC-expressing HT29-MTX clusters, images of median sections of mTurquoise-expressing HT29-MTX clusters are previously inverted on Fiji to track nuclei where myosin is not expressed. Then, for all HT29-MTX clusters (expressing mTurquoise-MLC or H2B-RFP), automated tracking of nuclei is performed on Fiji with TrackMate plugin  (52). Centroids of clusters are tracked as well with the Manual Tracking plugin. A custom-made R code was used for further analysis and graphical representations. Nuclei velocities are extracted at every time point from observation windows in contact with the channel walls (both clusters sides) that are a cell width wide (15 m) and start and finish 12 m away from each end of the clusters (front and back) to avoid considering cells not in contact with the channel walls.
Acknowledgments: We thank the patients for accepting to enroll in our study, the members of the  Jaulin, Voituriez, Piel, and Montagnac laboratories  and  S. Ramaswamy  for helpful discussions. We thank  T. Manoliu  and  C. Laplace  from the  PFIC  facility for technical help. We thank  M. Polrot  from the  PFEP  facility for assistance with the animal work. We thank  C. Robert 's lab for the plasma cleaner and cell lines. We thank  G. Belthier ,  J. Pannequin  lab, and  F. André 's lab for cell lines. We thank  W. Beng  and  K. Vaidziulyte  for the automated tracking algorithm Downloaded from https://www. science. orgat  Institut Gustave Roussy on October 20 , 2022 Pagès et al. , Sci. Adv. 8, eabp8416 (2022) 30 September 2022 used in optogenetics. Funding: This work was supported by  Agence Nationale de la Recherche (ANR)  grant  ANR-20-CE13-0031-01  (F.
J. , M. P. , M. C. , and R. V. ),  Institut National du Cancer (INCa)  grants  2018-1-PLBIO-04-IGR-1  and  2020-1-PLBIO-04-IGR-1  (F. J. and M. P. ),  Fondation ARC  grant  PAJ20181208275  (F. J. ),  Ligue Nationale Contre le Cancer Île-de-France (LNCC-IDF)  grant  R19102LL  (F. J. ), Fundraising against  CRC  from the  Gustave Roussy foundation  (F. J. ),  ANR Laboratoires d'Excellence (LABEX)  grant  CelTisPhyBio   ANR-10-LBX-0038  (M. C. ),  ANR Initiatives d'Excellence (IDEX) Paris Sciences et Lettres  grant  ANR-10-IDEX-0001-02 PSL (M. C. ),  ANR Institut Convergences Q-life  grant  ANR-17-CONV-0005  (M. C. ),  ANR  (R. V. ),  Fondation pour la Recherche Médicale (FRM)  (R. V. and E. D. ),  INCa  (R. V. ),  Philanthropia  fellowship (D. -L. P. ),  Taxe d'Apprentissage  from  Université Paris-Saclay  grants  2019  and  2020  (D.
-L. P. ), and  Ecole Polytechnique AMX  fellowship with financial support from  Inserm Cancer  (F. J. ) and  CNRS  (J. R. ). Author contributions: Conceptualization: F. J. , M. P. , R. V. , M. C. , D. -L. P. , E. D. , J. d. S. , E. G. , and  A. Mai. Formal analysis: D. -L. P. , E. D. , J. d. S. , E. G. ,  A. Mai. , A. Mac. , L. W. , G. L. , and R. L. Resources: D. -L. P. , E. D. , M. C. , J. d. S. , E. G. , A. Mac. , L. W. , G. L. , R. L. , J. C. , C. C. -J. , J. R. , M. D. , M. G. , and J. -Y. S. Software: D. -L. P. , M. C. , J. D. S. , E. G. , A. Mai. , and G. L. Methodology: F. J. , M. P. , R. V. , M. C. , D. -L. P. , E. D. , J. d. S. , E. G. ,  A. Mai. , L. W. , and R. L. Investigation: D. -L. P. , E. D. , J. d. S. , E. G. , A. Mac. , L. W. , G. L. , R. L. , J. R. , and M. L. Visualization: F.
VLA and LOFAR Karl G. Jansky VLA 6 GHz imaging was acquired as part of a larger program (18A-399, PI: P. Kamieneski). Details of the image reductions will appear in an upcoming paper  (Kamieneski et al. 2022, in preparation). Briefly, the G165 field was observed with C band (4-8 GHz) with full polarization in a 3 hr track to improve the UV-coverage, amounting to a total of 1. 5 hr of on-source integration time. Baselines for this A-configuration observation ranged from 0. 68 to 36. 4 km, resulting in a natural-weighted synthesized beam size of 0″. 65 × 0″. 37 at a position angle of 72°and a maximum recoverable scale of 8″. 9. The sensitivity is measured to be 2. 7 μJy beam -1 across the approximately 4 GHz of effective bandwidth. Data calibration and reduction were performed with the Common Astronomy Software Applications (CASA) package  (McMullin et al.
The data were reduced using the observatory pipeline, which performed the bias correction, flat-fielding, wavelength calibration, relative flux correction, coaddition, and extraction to 1D spectra. 17 Although redshift-fitting software was available, we opted to measure the spectroscopic redshifts of the coadded 1D spectroscopy in IDL 18  by way of our own software. The SPEC task has a library of spectroscopic features and a reference sky spectrum extracted from the pre-skysubtracted data, and is described elsewhere  (Frye et al. 2012, and references therein). Our catalog contains the secure spectroscopic redshifts for 86 of 88 sources. Of these, we find eight new galaxies that meet our criteria for cluster membership as defined in Section 6. 2.
Image Alignment and PSF Constructions PSFs need to be constructed for each LRI to prepare the images for simultaneous photometry. We start by aligning the images in each filter to the HRI in a two-step process using the astropy package reproject and astroalign tasks. We find astroalign outperforms reproject in producing astrometrically precise image alignment, but fails when images are significantly misaligned. Hence, we first apply reproject, which transforms images to the same orientation and pixel scale based on the World Coordinate System (WCS) header information. We then follow up on this step by applying the astroalign task in order to triangulate the source centroid positions and to calculate a revised value for the reprojection according to  Beroiz et al. (2020). We describe below the process of obtaining a good sampling of the PSF for each of the seven bands.
We thank the referee for the valuable comments that improved the manuscript. We appreciate the helpful discussions with  Neta Bahcall ,  Aliza Beverage ,  Megan Donahue ,  Matt Lehnert ,  James Lowenthal ,  Genviève Soucail ,  Daniel Wang ,  David Weinberg , and  Ann Zabludoff. Support  for program HST GO-14223 was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  Contract  NAS5-26555. M. P. was funded through the  NSF Graduate Research Fellowship  grant No. DGE 1752814 , and acknowledges the support of  System76  for providing computer equipment. B. L. F. gratefully acknowledges the hospitality of the  Institute for Advanced Study  and financial support of the  Ambrose Monell Foundation  and the Bershadsky Fund during much of this work.
L. D. acknowledges the research grant support from the  Alfred P. Sloan Foundation  (award number  FG-2021-16495 ). R. A. W. was funded by  NASA JWST Interdisciplinary Scientist  grants  NAG5-12460 ,  NNX14AN10G , and  80GNSSC18K0200  from  NASA Goddard Space Flight Center. N. F. and A. M. B. were both funded by a  UA/NASA   Space Grant for Undergraduate Research. A. Z. acknowledges support by grant No. 2020750  from the  United States-Israel Binational Science Foundation (BSF)  and grant No. 2109066  from the  United States National Science Foundation (NSF) , and by the  Ministry of Science & Technology ,  Israel. J. D. acknowledges the support of project  PGC2018-101814-B-100  ( MCIU/AEI/ MINECO/FEDER ,  UE) Ministerio de Ciencia, Investigación y Universidades. This project was funded by the  Agencia Estatal de Investigación ,  Unidad de Excelencia María de Maeztu , ref.
0 m 2 m -2 in SiB3-exp2), temperature and water stress (ORCHIDEE-exp1 includes temperature stress; ORCHIDEE-exp2 accounts for temperature and water stress) and data assimilation (ORCHIDEE-exp3, in which a subset of model parameters controlling photosynthesis and phenology are optimized against global OCO-2 (Orbiting Carbon Observatory 2) SIF data;  Bacour et al. , 2019). Most of the TBM model experiments were run with default parameters (BEPS, CLM50, SiB3, SiB4, ORCHIDEE-exp1 and ORCHIDEE-exp2). The other experiments were optimized in the following ways: either (a) parameters were hand-tuned based on the US-NR1 data (CLM4. 5) or (b) the parameters were optimized using OCO-2 SIF data (ORCHIDEE-exp3). For more details on the parameterization of the TBM-SIF experiments, we refer the reader to  Parazoo et al.
The standard deviation in tower-derived mean spring GPP (March-May) is approximately 0. 25 g C m -2 d -1. The addition of the cold-temperature limitation improves the model's ability to match the IAV of mean spring GPP (Fig. 6a-b ). An examination of all modeled scenarios for CARD and CARDcold (i. e. , all 4000 DALEC2 simulations) shows that the cold-temperature limitation produces spring IAV values much closer to what is observed in the tower-derived GPP data. Only 0. 3 % of CARD ensembles produce mean spring IAV values within 20 % of the tower-derived spring GPP standard deviation (0. 25 ± 0. 05 g C m -2 d -1 ), whereas 69 % of CARDcold ensembles have standard deviation values within the same range. Interestingly, assimilating only the first 10 years of GPP data (Half experiments, Fig. 6b ) slightly increases the number of ensemble members with standard deviations within the mentioned range for both CARD-Half (2.
Temperature controls on springtime GPP The added value of the DALEC2 cold-temperature limitation for modeling mean spring (March-May) GPP is logically due to large fluctuations in spring temperature at Niwot Ridge. The cold-temperature limitation allows DALEC2-CARDAMOM to match the IAV of tower-derived spring GPP closely. Furthermore, the cold-temperature limitation enables the model to match tower spring IAV in the second half of the time period (2010-2018) when only the first 10 years of GPP data is assimilated  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009). This indicates that the cold-temperature limitation is able to estimate spring GPP outside of its training window and could be useful at other sites where data availability is limited. Future work will include evaluating the cold-temperature limitation at other sites to ensure that it is applicable beyond Niwot Ridge, for example using forecast skill metrics proposed by  Famiglietti et al.
Model intercomparison and implications for GPP models Here, we evaluate DALEC2-CARDAMOM against mean spring GPP estimates from TBM-MIP models (Sect. 2. 5 and  Parazoo et al. , 2020). It is important to remind the reader that the CARDAMOM runs have a significant advantage over the TBM-MIP models in this analysis, as CARDAMOM is trained on US-NR1 GPP data. While TBM-MIP models use tower-observed meteorological inputs, prescribe towerspecific and time-invariant structural properties such as the LAI observed at US-NR1 (SiB3-exp2 and CLM4. 5), and use data assimilation of global remote sensing data to constrain globally representative plant functional types (ORCHIDEE-exp3), they are not directly constrained by time-varying car-bon fluxes at the tower. As such, we emphasize that our model comparison is not a strict assessment of performance but rather an attempt to learn how model simulation of GPP at an evergreen needleleaf site can be improved.