dataesr/scientific-paragraphs-categorization huggingface dataset:
VII. CONCLUSION AND FUTURE WORK We have conducted an empirical study using a collected list of 112 deprecated APIs to learn the characteristics of the API migrations required by Python deprecated APIs. Using manual thematic analysis, we found a number of categories of API considering three dimensions: update operation, API mapping, and context dependency. Based on findings in our empirical study, we created MLCatchUp, an automated tool to update the usage of deprecated ML APIs in Python. ML-CatchUp automatically infers the transformation operations required to update the usage of a deprecated API. MLCatchUp utilizes type inference to deal with the fact that Python is a dynamically-typed language. We have evaluated MLCatchUp using a dataset of 267 files containing 551 API usages from 68 different APIs, where it manages to detect 93.
7% of the deprecated API usages without type inference. Utilization of type inference improves this detection rate to 100. 00%. For the update accuracy measurement MLCatchUp achieves 80. 58% accuracy. We have further improved MLCatchUp's accuracy by adding transformation constraint inputs, allowing it to handle context-dependent deprecated API usage updates. With this improvement, MLCatchUp achieves 90. 74% accuracy. For future work, we plan to extend our study to other popular Python libraries. By increasing the scope of the case study, we may encounter more variety of deprecated APIs and their migrations. We also plan to address the current shortcomings of MLCatchUp, which have been described in Section IV-F. A replication package of MLCatchUp can be accessed in the following link: https://zenodo.
study of Python deprecated API migrations and its findings. Section III presents the structure and features of our Python API usage automated update tool, ML-CatchUp. Section IV describes our evaluation of MLCatchUp. Section VI discusses related work on API deprecation and program transformation. Section V discusses the threats to validity. Finally, Section VII concludes with future work. • We evaluate MLCatchUp using a dataset of 551 deprecated API usages of popular ML libraries collected from public GitHub repositories, where MLCatchUp can achieve up to 90. 7% accuracy. The rest of this paper is organized as follows. Section II discusses our empirical.
MS data processing and analysis Acquired raw data was processed with MaxQuant software (version 1. 5. 2. 8)  [60]  using the default settings if not stated otherwise. Raw files of particular experiments were processed separately. The built-in Andromeda search engines searched MS/MS spectra against fragment masses of peptides derived from an E. coli K12 reference proteome (taxonomy ID 83333) containing 4,324 entries (UniProt, release 2017/12) and a list of 245 common contaminants. The minimum required peptide length was set to 7 amino acids with the maximum of two missed cleavage sites allowed for endoproteinase Lys-C. For a protein to be quantified, two occurrences of the protein H/L ratio were required per time point (the multiplicity was set to two with Lys8 specified as the heavy label).
Carbamidomethylation of cysteine was set as a fixed modification, and protein N-terminal acetylation and methionine oxidation as variable modifications. Precursor ion mass tolerance was set to 20 ppm in the first search and to 4. 5 ppm and in the main search. Peptide and protein identifications were filtered using a target-decoy approach with a an estimated false discovery rate (FDR) of less than 1. 3% at protein and less than 0. 4% at peptide level  [61]. Proteins identified by the same set of peptides were combined into a single protein group. For protein quantification, at least 2 peptide ratio counts were required. Data analysis was performed using Microsoft Excel and Perseus software (version 1. 5. 6. 0)  [62]. Nonnormalized protein H/L ratios from proteingroup. txt were used for protein quantification.
All contaminants, reversed hits and proteins identified only by modification were removed. To simplify the data, the data sets from three biological replicate measurements were combined as a union, in which protein H/L ratios in each of the time points were kept if measured only in one replicate (Class I), a mean was calculated if measured in two (Class II) or in three replicates (Class III). To reduce the number of data points and missing values, time points were pooled into time bins. For that, the median protein H/L ratio of respective time points was calculated and assigned to a specific time bin. Proteins were then ranked based on their H/L ratio within each time bin and displayed in a heatmap-like representation sorted by the maximal H/L ratio across time bins. Gene-annotation and KEGG enrichment analysis was performed using DAVID tool (version 6.
Temporal analysis of label incorportion MaxQuant processed data were imported in the R environment (R Core Team, 2018) to perform a time series analysis. The heavy label incorporation was calculated by dividing the heavy label intensity by the total (heavy plus light) intensity for each protein. Reverse hits, potential contaminants and proteins only identified by sites were filtered out. The median of three replicates was computed per time points. Proteins with missing values were discarded from further analysis. The Euclidean distance between proteins was computed and used to generate the hierarchical clustering via the Ward's minimum distance using the stats R package. Time period resolution was achieved with minimal overlap when using eight cluster groups (k = 8). The resulting heatmap was drawn using the gplots R package (Warnes et al.
, 2019), while cluster protein profiles over time were generated using the ggplot2 R package (Wickham, 2016). For each cluster, the time needed to reach 50% heavy label incorporation was inferred by intersecting the 50% incorporation with the smoothed conditional mean. The list of proteins belonging to each cluster were imported into Perseus software suite (version 1. 6. 5. 0)  (Tyanova et al. , 2016). The proteins were functionally annotated with Gene Ontology, KEGG, Pfam, GSEA, UniProt keywords, Corum, Interpro, Reactome and EC functional resources. Each cluster was tested for function categories over-or under-representation using a fisher exact test (FDR ≤ 0. 1).
Reactive web application for data browsing To allow the scientific community to browse our datasets, we developed a Shiny application coded entirely in the R programming language (Chang et al. , 2019). Our application, called PCTsee, allows user to load different datasets, which are available through a drop down widget. It is then possible to select gene name or protein ID of interest in order to display their abundance value. Whereby the abundance values have been computed by the MaxQuant software during LC-MS/MS data processing (e. g. intensity, ratio). The user selection will result in a protein abundance profile plot, as well as a table containing some information about the selected protein (e. g. protein names, sequence coverage). Users can interact further with the displayed plot to zoom, rescale or remove samples due to the plotly R package (Sievert, 2018).
GO_term enrichment Gene ontology analysis of the proteins actively synthesized during resuscitation was perform with the online software DAVID (https://david. ncifcrf. gov/tools. jsp). Enrichment was performed comparing the gene names quantified proteins against the ones of identified proteins. DAVID was used with its preset with: similarity overlap 3 and threshold 0. 5; initial and final group membership 3 and multiple linkage 0. 5; enrichment thresohold at EASE 1.
Acknowledgements The authors thank  Karl Forchhammer  for useful discussions and comments on the manuscript. B. M. was supported by grants from the  Deutsche Forschungsgemeinschaft  ( German Research Foundation Cluster of Excellence   EXC 2124 ,  SFB 766 ,  FOR 2816 ,  TRR 261 ), the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 955626  and the  German-Israeli Foundation  grant  I-1464-416. 13/2018. K. G. was supported by a grant from the  Danish National Research Foundation  (grant identifier  DNRF120 ) and the  Novo Nordisk Foundation.
Experimental Results In this section, we apply our algorithm to various synthetic and natural colour and hyperspectral images. We first compare our MDPOHMT to an existing greyscale HMT in order to highlight the need for a generalisation to colour and multivariate imagery in order to better distinguish between objects of interest. We then compare our approach with other colour HMTs and investigate the benefits of the noise robustness offered by using PO. We also show how the MDPOHMT can be used to detect objects with varying size, shape, colour and textural information from high resolution images. Finally we show how the reduced ordering can be performed on higher dimensional data and apply the MDPOHMT to hyperspectral object detection using a synthetic dataset.
Image Acquisition The primary source of images used when testing our MDPOHMT is the DOTA dataset  [47]  which contains thousands of images taken from Google Earth with 15 object classes to be detected. For the purposes of testing the MDPOHMT, a subset of these images which contain swimming pools are used as they have varying size, shape, orientation and colour information as well as having varying amounts of pixels on target, providing a very varied target set. The dataset contains a ground truth for its training and validation subsets, ground truths for further images are created in order to increase the volume of data available to test on. In total, 7 high resolution colour images were selected for testing with a total of 1257 instances of the swimming pool object included. Other natural and synthetic images were used for testing our MDPOHMT against other greyscale and colour HMTs.
Application of the MDPOHMT to high resolution Aerial Imagery The MDPOHMT is also designed to generalise to objects of interest over multiple images. In order to validate this, the MDPOHMT was applied to a set of aerial images from the DOTA dataset to test its performance for detecting various sizes and shapes of colour objects, in this case swimming pools. This class of objects was chosen for the challenge that it presented with varying size, shape, colour and level of occlusion. In order to detect these objects first a generalised SE must be designed from examples or from prior knowledge of the objects of interest. After viewing some examples, the average colour size and shape can be used to create an appropriate SE. In order to generalise well to objects, including those which are noisy or partially occluded, the fit often needs to be relaxed.
In this particular example, another three SEs were used to detect objects the first had missed, using information from the examples of false negatives to design appropriate SEs. This process, however, comes at the expense of execution time, highlighted by the results in Table  1. This has the desired effect of increasing the recall of the MDPOHMT at the expense of some precision due to false positives introduced by each SE. In order to compare the MDPOHMT with other related techniques, the SHMT was also applied to the same subset of the DOTA dataset with identical SEs to those used in the MDPOHMT. The results of the SHMT with a single SE and with multiple SEs are shown in Table  1. From these results we can see that the SHMT fails to generalise due to the need for the SE to fit the query image more exactly.
In order to assess how reliably the MDPOHMT can detect targets in a more complex domain, an additional set of images was created again using the USGS  [17]  Spectral Library along with the Hyperspectral Imagery Synthesis (EIAs) MATLAB toolbox  [10]  which allowed for the creation of randomised abundance maps for hyperspectral image synthesis. Four spectra from the USGS Library were selected, three natural materials: dry grass, spruce needles, and limestone which would form the background of the synthetic scene and concrete which again forms the in-painted targets at abundancies decreasing from 100% to 1%. Each of these endmember spectra present in the image are displayed in Figure  20a  with both the grass and limestone spectra being similar to that of the target concrete. The background scene was generated using the Gaussian Fields method  [10]  and the targets were subsequently inserted, with an abundance of 100%, i.
Conclusion In this paper we presented and described our proposed novel method based on the Hit-or-Miss Transform, the Multi-Dimensional Percentage Occupancy Hit-or-Miss Transform. The MDPOHMT is intended as a general technique which extends the morphological HMT for use in object detection and pattern recognition applications in colour and multivariate images. This is achieved using a reduced ordering scheme which reduces multivariate data into a scalar value via some distance or similarity measure. We have validated our approach, first using synthetic colour images in order to display the correct operation of our technique followed by object detection and discrimination experiments on natural images. We tested the MDPOHMT on the DOTA aerial image dataset in order to display how the technique performs in a large dataset with objects of interest varying in size, shape, colour, texture, and occlusion levels.
Through the use of percentage occupancy and rank order filters combined with the use of PO plots for setting the appropriate parameters, the MDPOHMT has been shown to perform favourably when compared with related single channel and multivariate HMT definitions on the considered pattern recognition tasks. Through experimentation using the DOTA dataset we found that our MDPOHMT operator, in conjunction with the Euclidean distance-based reduced ordering, can be very sensitive to changes in colour. The Euclidean distance measure was used as an example of the reduced ordering necessary for the operation of our technique. Other distance measures may be more appropriate for specific applications, especially in the case of hyperspectral imaging where dedicated spectral measures may be implemented.
We present a search for quasi-monochromatic gravitational-wave signals from the young, energetic X-ray pulsar PSR J0537-6910 using data from the second and third observing runs of LIGO and Virgo. The search is enabled by a contemporaneous timing ephemeris obtained using NICER data. The NICER ephemeris has also been extended through October 2020 and includes three new glitches. PSR J0537-6910 has the largest spin-down luminosity of any pulsar and exhibits frequent and strong glitches. Analyses of its long-term and inter-glitch braking indices provide intriguing evidence that its spin-down energy budget may include gravitational-wave emission from a time-varying mass quadrupole moment. Its 62 Hz rotation frequency also puts its possible gravitational-wave emission in the most sensitive band of the LIGO/Virgo detectors.
2018). It is this behavior that provides tantalizing suggestions that PSR J0537-6910 could be losing some of its rotational energy to gravitational-wave emission. In particular, a slightly deformed pulsar can emit gravitational waves that results in n = 5, and a r-mode fluid oscillation in a pulsar can emit gravitational waves that results in n = 7 (see, e. g. ,  Riles 2017; Andersson et al. 2018; Glampedakis & Gualtieri 2018; Gao et al. 2020). In this work, we search for mass quadrupolar gravitational-wave emission from PSR J0537-6910 that follows the same phase as that of the pulsar's rotation. Previously, data from initial LIGO's fifth and sixth science runs (S5 and S6) and Virgo's second and fourth science runs (VSR2 and VSR4), in conjunction with RXTE timing measurements, were used to set limits on gravitational-wave emission by PSR J0537-6910 that closely approached the spin-down limit  (Abbott et al.
Search pipeline The time-domain Bayesian method performs a coherent analysis of the interferometers' data, meaning that we analyze the entire data set with an effective single Fourier Transform, thereby preserving the phase information. First, the raw strain data are heterodyned (Dupuis & Woan 2005) using the expected signal phase evolution, known precisely from the electromagnetic timing ephemeris. Then a low-pass filter with a knee frequency of 0. 25 Hz is applied, and the data are downsampled so that the sampling time is one minute, compared to 60 microseconds originally. This heterodyning is performed for an expected signal whose frequency is at once or twice the rotational frequency of the pulsar. The heterodyned data is the input to a nested sampling algorithm that is a part of the LALInference package  (Veitch & Vecchio 2010; Veitch et al.
2  The dimensionless gravitational-wave amplitude h 0 and 2 Simulations on independent and identically distributed noise realizations show that the different noise instantiations can produce upper limits that vary by ∼ 20% at a 1σ confidence level. However, the Bayesian credible limits we present are valid for our particular dataset. coefficients C 21 and C 22 are constrained for the single and dual harmonic searches, respectively. For the single harmonic search, h 0 can be mapped to a limit on the maximum ellipticity ε using equation (4). In Table  3 , we show the different constraints for both searches using all O3 data and the last ∼ 6 days of O2 data (see Section 2. 3). In addition to the detector calibration uncertainties discussed in Section 2. 3, we estimate that the statistical uncertainty on the upper limits due to the use of a finite number of posterior samples is on the order of 1%.
To provide some insight, if we compare the sky-averaged emission strength at only the rotation frequency to emission at only twice the rotation frequency, the spin-down limit would allow the amplitude of the radiation at the rotation frequency to be approximately twice as strong as that at twice the rotation frequency (see Section 3 of Jones 2010, for more details). The results presented above use all O3 data in combination with about six days of O2 data, when NICER was operating and monitoring PSR J0537-6910. We also conducted searches using only O3 data or using O3 data plus O2 data from 2017 March 22 to the end of O2. The latter analysis assumes no glitches occurred during the additional time and represents the estimated maximum time that can be safely included without a contemporaneous timing model (see Section 2.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Netherlands Organization for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO con-sortium.
The authors gratefully acknowledge the support of the  NSF ,  STFC ,  INFN  and  CNRS  for provision of computational resources. This work was supported by  MEXT ,  JSPS   Leadingedge Research Infrastructure Program ,  JSPS   Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905 :  JP17H06358 ,  JP17H06361  and  JP17H06364 ,  JSPS Core-to-Core Program A. Advanced Research Networks, JSPS   Grant-in-Aid for Scientific Research (S)   17H06133 , the joint research program of the  Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF)  and  Computing Infrastructure Project  of  KISTI-GSDC in Korea, Academia Sinica (AS), AS Grid Center (ASGC)  and the  Ministry of Science and Technology (MoST) in Taiwan  under grants including  AS-CDA-105-M06 ,  Advanced Technology Center (ATC) of NAOJ, and Mechanical Engineering Center of KEK.
2018; Ho et al. 2020b). Assuming a (unobserved) glitch occurred on 2017 March 22 with the same size as the largest NICER glitch (i. e. , glitch 2 with ∆f rot = 36 µHz), we would expect a subsequent glitch 224 d later (at 68% confidence) on 2017 November 1, which is the earliest estimated date at which glitch 1 occurred (see Figure 2 and Ho et al. 2020b). Thus 2017 March 22 to Novem- ber 1 is the longest period over which we would expect PSR J0537-6910 to not have undergone a glitch and the NICER ephemeris to be valid. O3 lasted from 2019 April 1 to 2020 March 27, with a one-month pause in data collection in October 2019. The three detectors' datasets H1, L1, and V1 had duty factors of ∼ 72%, ∼ 69%, and ∼ 69%, or 259, 248 and 248 d of data, respectively during O3.
Implantable pressure sensors represent an important part of the research activity in laboratories. Unfortunately, their use is limited by cost, autonomy and temperature-related drifts. The cost of use depends on several parameters, particularly their low battery life and the need for miniaturization to be able to implant the animals and monitor them over a time that is long enough to be physiologically relevant. This paper studied the possibility of reducing the thermal drift of implantable sensors. To quantify and compensate for the thermal drift, we developed the equivalent model of the piezoresistive probe by using the Cadence software. Our model takes into account the temperature (34-39 • C) as well as the pressure (0-300 mmHg). We were thus able to identify the source of the drift and thanks to our model, we were able to compensate for it thanks to the compensation circuits added to the conditioning circuits of the sensor.
The activity of epiphyseal growth plates, which drives long bone elongation, depends on extensive changes in chondrocyte size and shape during differentiation. Here, we develop a pipeline called 3D Morphometric Analysis for Phenotypic significance (3D MAPs), which combines light-sheet microscopy, segmentation algorithms and 3D morphometric analysis to characterize morphogenetic cellular behaviors while maintaining the spatial context of the growth plate. Using 3D MAPs, we create a 3D image database of hundreds of thousands of chondrocytes. Analysis reveals broad repertoire of morphological changes, growth strategies and cell organizations during differentiation. Moreover, identifying a reduction in Smad 1/5/9 activity together with multiple abnormalities in cell growth, shape and organization provides an explanation for the shortening of Gdf5 KO tibias.
An additional challenge is investigating these processes in mice, where tissues and organs can contain hundreds of thousands of cells, which forms multiple data scales. Focusing on the tissuelevel results in loss of subcellular resolution, while focusing on smaller regions at high-resolution results in loss of their relationship to 3D tissue morphology. Recent advances in tissue clearing combined with light-sheet fluorescence microscopy has allowed for high-resolution mapping of fine tissue architectures in intact 3D samples  [29] [30] [31] [32]. Yet, despite the advances in whole-organ imaging, there is still a lack of tools with which to accurately extract the 3D morphology of cells and identify morphological patterns in large 3D datasets in a time-efficient manner. The growth plate is an excellent model for studying 3D cell behavior as a driver of tissue morphogenesis.