dataesr/scientific-paragraphs-categorization huggingface dataset:
In 2018,  [22]  reformulates Problem (20) using recent results from proximal optimization  [23]  with sPLS Durif. In this last case, sPLS Durif provides an exact and closed-form solution reminiscing the soft threshold operator. Moreover, they suggest an adaptive method for computing the sPLS weight vectors using classical PLS ones. Along the lines of methods presented above, Dual-sPLS aims at inference and interpretability: accurate predictions combined with sparse localization features for better chemometrics performance. Following  [22] , we also wish to provide a means to tuning the relative sparsity of the outcome. Finally, as different analytical chemistry modalities provide different insights on chemical mixtures, the Dual-sPLS is designed to naturally allow the combination of heterogeneous datasets as a byproduct of the versatile dual norm approach 2.
t. Ω(w) = 1. ( 22 ) Although formulation is generic, we emphasize four types of norms that make practical sense when dealing with measurements typically available in chemometrics. We provide the corresponding R  [24]  package dual. spls [25]  with a complete description. It contains the following main functions, each of them being associated to specific penalty: 1. Dual-sPLS l (pseudo-lasso norm, d. spls. lasso()). Similar to the sPLS Problem  (20) , an intuitive norm combines 2 and 1 : Ω(w) = λ w 1 + w 2. ( 23 ) 2. Dual-sPLS gl (pseudo-group lasso norm, d. spls. GL()). Inspired by group lasso  [26] , it combines groups of measurements. It applies pseudolasso to each group individually while constraining the total set. For G groups, w g represents the variables of the loading vector w that belongs to group g.
Thus, for ν = λµ and p ∈ {1,. , P }, it can be expressed as: w p w 2 = 1 µ δ p (|z p | -ν) +. (30) A common issue is the choice of the appropriate shrinking parameter. Cross-Validation  [27] , evoked in Section 4. 3, is popularly adopted in sparse regressions. We choose a more intuitive option. We obtain it adaptively, according to the proportion of variables that we would like to keep in the active set at each iteration. The procedure is illustrated in Figure  1. It represents the empirical cumulative distribution of sorted magnitudes of |X T y| from the real data D NIR described later in Section (4. 2). Fixing a shriking ratio ς of expected zero coefficients (e. g. ς = 80 %), we select the threshold ν at iteration m as depicted. As the cumulative destribution is non-decreasing, we choose the first x-axis value corresponding to ordinate 0.
Pseudo-group lasso Response y may be explained separately by explanatory variables of different nature with prediction models. Combining them appropriately is potentially beneficial both in predictive and interpretative powers. The same reasoning could be used to partition the dataset into groups. Physico-chemical motivation resides in segmenting a spectrum into homogenous bands or combining complementary modalities (e. g. IR and NMR) to predict the same property (e. g. viscosity, density). We consider G groups, and z g sub-vector of z denotes variables belonging to group g. The group lasso inspired norm is expressed as in Equation  (24). The closed-form solution is collinear to the vector z νg. It is given by z νg = δ g (|z g | -ν g ) + and z ν = z νg g∈{1,. ,G} , (31) δ g being the vector of signs of w g and ν g = λ g µ for g ∈ {1,.
Real data: near-infrared (NIR) spectroscopy D NIR In chemistry, complex mixtures of molecules are analyzed with different physico-chemical methods. Besides, determining macroscopic properties is important to their use. The evaluation on real data is done using NIR spectra of hydrocarbon samples. NIR is based on the principle of absorption of radiation (infrared) by matter  [28]. Infrared radiations correspond to wavenumbers directly greater than those of the visible light spectrum. The absorption of radiation de-pends on chemical bonds, therefore a NIR spectrum encodes information about the composition of the sample. We focus on the density property which is obtained by standardized methods. The IFPEN dataset D NIR was partly exposed in  [29, 30]. It is available at http://www. laurent-duval.
eu and subject to a forthcoming publication  [31]. It is composed of 208 samples with 1557 variables. The corresponding matrix X is singular. Many chemical data require adequate preprocessing: normalization, baseline removal  [32] , deconvolution  [7]. Here we simply apply a discrete derivative obtained with a Savitzky-Golay smoothing filter  [33]  of degree 2 and length 15. It serves as both a crude baseline filter and diversity enhancement operator  [34]. The NIR preprocessed dataset D NIR is represented in Figure  3.
Calibration and validation The evaluation of prediction models traditionally divides the dataset into two representative sets called calibration and validation. Three main methods are used. In the first one, observations are randomly selected. The second only considers the distribution of values in the response y [35, Stratified sampling]. A third class is known as Kennard and Stone method  [36]. It optimizes relative distances between observations according to variables of X. In chemometrics, one may expect the existence of a yet unknown dependence between analytical measurements and properties. Taking both X and y values for a proper calibration and validation split would be desirable. The attempts of  [37]  to consider X and y in a single distance with appropriate weights is not straightforward.
It however is not sufficient to hint at improvements in interpretability. Looking only at variables affecting the response (shaded red background in panel 1), most compared methods exhibit significant coefficients in many (useless) areas (transparent background). Only Dual-sPLS l , sPLS LeCao present concentrated coefficients that can help chemical interpretation. On this rudimentary yet explainable model, we hint that Dual-sPLS l provides a predictive quality comparable to its challengers, and is the best in providing at the same time accurate localization on simulated data, with a verifiable (yet simplified) prediction model. We are now able to evaluate the performance of Dual-sPLS l on real nearinfrared data D NIR (4. 2) for density prediction. Similarly to D SIM , RMSE curves in Figure  5  for calibration (top-left) and validation (top-right) globally decrease with an increasing number of components.
Dual-sPLS pseudo-least squares evaluation (D SIM ) The Dual-sPLS LS requires data to be represented by a non-singular matrix X, as explained in Section 3. 2. 3. Since real data D NIR is singular, we use simulated data D SIM presented in Section 4. 1. As the number of variables in D SIM is already small, we only shrink 60 % of its variables to evaluate the Dual-sPLS LS against classical least squares. The latter is denoted by dashes, as the number of latent components is meaningless in this case. For calibration (Figure  6  top-left) the RMSE for Dual-sPLS LS decreases mildly as the number of components increases. It approaches the least squares performance. For validation (Figure  6  top-right) Dual-sPLS LS performs similarly or better than least squares all over model orders. This contrast in performance might be explained by a tendency to overfit for least squares.
Dual-sPLS pseudo-ridge evaluation (D SIM , D NIR ) Dual-sPLS r is compared to classical ridge regression (Section 2. 2) either applied to simulated data D SIM or real data D NIR. Ridge hyper parameter t (equation (  17 )) is fixed using cross validation. We set λ 2 for Dual-sPLS r (equation (  26 )) to 1 t for easier comparison. All other parameters are kept as for Dual-sPLS l (Section 5. 1). Looking at top-left and -right in Figure  7  Dual-sPLS r reaches a plateau for D SIM after five latent components. Moreover, its RMSE values are slightly lower than ridge's for both calibration and     validation. We can safely select six latent components as before. Reference coefficients for ridge are misleading because the largest ones do not reside in influencing areas. They therefore can not be used for data interpretation.
By selecting only fifty variables, located in red regions governing the model, Dual-sPLS r better succeeds in both prediction and localization. Similar conclusions can be drawn for real data D NIR on RMSE values. Dual-sPLS r even better predicts the response y with only four components. Regression coefficients (Figure  8  bottom) yield comments akin to above. While ridge apparently emphasizes unimportant features, Dual-sPLS r seems more reliable in identifiying of relevant variables to predict density using chemical data.
Coefficients are sieved with a user-defined sparsity target. They are well-located in influential data ranges, suggesting a means for better interpretability of the trained prediction reduced model. Pseudo-lasso and ridge Dual-sPLS avatars exhibit close colocation of selected features in both datasets despite different penalties. This suggests a robust identification of meaningful information in signals. The Dual-sPLS framework is thus a good candidate for a host of applications. We provide it as an open-source package in R  [25]. It can be prolonged to other field-favorite penalties, for instance elastic net. We plan to evaluate the alluded "pseudo-group lasso" option, to refine feature selection on important contiguous areas, or to combine datasets providing complementary information on the predicted response.
Acknowledgements This work was performed within the framework of the LABEX MILYON (ANR-10-LABX-0070) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). We acknowledge the financial support of the Research Council of the Saint Joseph University of Beirut. Ghislain Durif contributed in the code validation procedure in R. IFPEN provided the real data set used in the applications. We thank Noémie Caillol, Luca Castelli and Irène Gannaz for useful comments. Our mentor, colleague or friend François Wahl passed away unexpectedly during the writing of this paper. He was the driving force of our team. PLS Dual-sPLSl sPLSLeCao sPLSChun sPLSDurif lasso q q q q q q q q q q 2 4 6 (calibration) q q q q q q q q q q q q PLS Dual-sPLSl sPLSLeCao sPLSChun sPLSDurif lasso q q q q q q q q q q q q q q q q q q q q q q 2 4 6 (validation) q q q q q q q q q q q q PLS Dual-sPLSl sPLSLeCao sPLSChun sPLSDurif lasso q q q q q q q q q q q q Figure B.
9: Dual-sPLS l evaluation on simulated data D SIM. MAE (top) and R 2 (bottom) values for calibration (left) and validation (right) with respect to the number of latent components derived from PLS, Dual-sPLS l , sPLS LeCao , sPLS Chun , sPLS Durif and lasso regressions. PLS Dual-sPLSl sPLSLeCao sPLSChun sPLSDurif lasso q q q q q q q q q q 2 4 6 8 10 (calibration) q q q q q q q q q q q q PLS Dual-sPLSl sPLSLeCao sPLSChun sPLSDurif lasso q q q q q q q q q q q q q q q q q q q q q q 2 4 6 8 10 (validation) q q q q q q q q q q q q PLS Dual-sPLSl sPLSLeCao sPLSChun sPLSDurif lasso q q q q q q q q q q q q Figure B. 10: Dual-sPLS l evaluation on real data D NIR. MAE (top) and R 2 (bottom) values for calibration (left) and validation (right) with respect to the number of latent components derived from PLS, Dual-sPLS l , sPLS LeCao , sPLS Chun , sPLS Durif and lasso regressions.
2022; Lyu et al. 2022; Wang et al. 2022) , or methodological contributions (see, e. g. ,  Davis et al. 2022) , demonstrating the broad impact on the scientific community of the GW data releases. This paper is organized as follows. Section 2 summarizes the status of the detectors during the observing run O3, together with high-level indicators such as their distance reach and duty cycle of operation. This section also provides insights about how the data are collected and calibrated, about data quality, and about simulated signal injections. Section 3 describes the format, content, and provenance of the strain data files distributed through the GWOSC, including the nomenclature used for the calibration versions and channel names. Section 4 describes the Event Portal, a searchable GW event database accessible online.
No injections of BURST signals were performed during O3a and O3b. Some signal injections of the DETCHAR type were injected during O3a and O3b for both H1 and L1, while there were none for Virgo. The waveform model used for those injections is h t a e t t f t t sin 2 0 2 2 0 ( ) ( ) [ ( ) ] t p f = -- - - , where t 0 is the time of the injection. The signal parameters τ, f, and f were randomly chosen and are documented in separate databases for H1 323 and for L1. 324   H1 11,218,675 11,218,675 11,177,046 11,177,046 11,218,675 11,125,849 11,125,849 O3a 15,811,200 L1 11,956,179 11,956,179 11,943,913 11,943,913 11,956,179 11,879,365 11,879,365 V1 12,038,929 12,038,929 12,038,929 12,038,929 12,038,929 12,038,929 12,038,929 H1 9,967,195 9,967,195 9,964,945 9,964,945 9,967,195 9,915,276 9,915   Four STOCH signals were injected during O3.
Some events are listed in the database with multiple versions, typically corresponding to the event's inclusion in multiple releases. The cumulative GWTC catalog includes all confident GW events published by the LVK Collaboration, and currently includes 93 events. Events in the GWTC-2. 1confident and GWTC-3-confident releases all have a probability of astrophysical origin greater than 0. 5 329  in at least one of the search pipelines, and are included in the cumulative GWTC. The online catalogs are searchable via a web user interface. The Event Portal database can be queried based on specific source properties, namely the primary mass, secondary mass, total mass, chirp mass, final mass (of the merger remnant), luminosity distance, redshift, effective inspiral spin, or other properties associated with the observed signal, such as UTC or GPS event time, detector frame chirp mass, network S/N, false alarm rate, and the posterior probability of astrophysical origin.
The events can also be selected by identification such as partial event name, release catalog, or group of catalogs. The output format can be one of the following: HTML, JSON, CSV, or plain ASCII text. To ease the analysis of multiple events, the catalogs can be queried programmatically with scripts using the REST API that returns all catalog lists in a JSON format. Catalogs can be queried with a GET request. As an example, to request all merger events for which the primary mass is less than 3 M e , the URL for the GET request would be https://gwosc. org/ eventapi/html/query/show?max-mass-1-source=3. A detailed explanation of the query API nodes can be found on the GWOSC website. 330   3 ). Color bars represent times when data are available, and white areas show times when data are not available.
Parameter Estimation and Other Science Data Products In addition to the information provided by GWOSC, complementary data about various aspects of the GWTC catalog production are available on the Zenodo open repository, see  LIGO Scientific Collaboration & Virgo Collaboration (2021b , 2021c , 2022a)  for GWTC-2 and GWTC-2. 1 and LIGO Scientific Collaboration, Virgo Collaboration & KAGRA Collaboration (2021a , 2021b , 2021c , 2023a)  for GWTC-3. They are linked from the documentation in GWOSC. 331  Those include lists of candidate events, the description of the search pipeline sensitivity, and data quality products. The additional data release also include posterior samples from Bayesian inference analyses, see  LIGO Scientific Collaboration & Virgo Collaboration (2022b)  for GWTC-2. 1 and LIGO Scientific Collaboration, Virgo Collaboration & KAGRA Collaboration (2021d) for GWTC-3.
For convenience, those data are directly linked from the single event page of the online catalog. They can also be programmatically accessed through the JSON API as downloadable links to the files on Zenodo. The parameter names in the posterior samples follow a standard nomenclature 332   (Hoy & Raymond 2021). Parameter estimates may change with the different versions of the event or catalog release. The users should refer to the release pages on Zenodo (LIGO Scientific Collaboration, Virgo Collaboration & KAGRA Collaboration 2021d; LIGO Scientific Collaboration & Virgo Collaboration 2022b) and their associated publications  (Abbott et al. 2021b (Abbott et al. , 2021c) )  to obtain the full details about the configuration and assumptions made by the different analyses that are denoted by the set of version numbers for each event (depending on the number of releases in which that event appears).
Technical Validation The O3 GWOSC data release is repackaged for the broader user community beginning with the internal strain data products used for data analysis by the LVK Collaboration for publication purposes. The repackaging produces new GWOSC gwf and hdf5 files containing the previously discussed strain, data quality, and hardware injection information for each detector. The repackaging allows us to add data quality segments, remove times outside of observing mode, and simplify the channel names. In addition, versions of these GWOSC files at a reduced sampling rate of 4096 Hz for the strain channel of each detector are also produced. All data for the release are carefully reviewed by the internal GWOSC team and then reviewed by an independent review team made up of members from the LVK Collaboration.
This review process checks that: 1. The strain vectors at the maximum sample rate (16 kHz) in the GWOSC hdf and gwf files are identical to machine precision to the corresponding strain vectors of the LVK main archives; 2. The strain vectors after resampling at 4 kHz do not have numerical artifacts that may arise from the resampling technique; 3. The data quality and injection information located in either the GWOSC hdf and gwf files or the online Timeline tool described in detail in Section 6, agree with all available records; and 4. The documentation associated with the O3 data products found online is correct and contains comprehensive information for the broader user community. The data files and accompanying documentation are released to the public on the GWOSC website once all checks have passed at the designated date and time agreed to by the LIGO, Virgo and KAGRA Collaborations.
List of Observing Segments Segment lists describe times when GW detectors are collecting data and are operating in a normal condition, as described in Section 3. 2. The GWOSC website provides an online app called Timeline to discover, plot, and download segment lists. 336  The Timeline query page allows users to select observing runs from a drop-down menu, and then view the names of segment lists associated with the selected run. Segment lists may be downloaded as ASCII text files or in a JSON format. Alternatively, segment lists may be displayed in an interactive plot, as seen in Figure  3. To explore times within a run, a visitor can use the mouse to scroll and zoom on the Timeline plots. Hovering the mouse over a segment displays a tool tip with the exact start and stop times, in both GPS and UTC time.
Summary The O3 data set described in this paper represents the most sensitive GW observations to date. The data contain over 80 compact object merger signals, as described in a number of catalog releases, including GWTC-2  (Abbott et al. 2021a ), GWTC-2. 1  (Abbott et al. 2021b), and GWTC-3 (Abbott et al. 2021c ). O3 includes three main phases: O3a, O3b, and O3GK. O3a and O3b are both joint runs of LIGO and Virgo, while the O3GK run involved KAGRA and GEO 600. Data and documentation for all O3 data are available from the GWOSC website. Looking ahead, LIGO, Virgo, and KAGRA are planning an O4 run, scheduled to begin in 2023, with improved sensitivity. Data from events discovered in O4 will be released as the events are published, and release of the next large strain data sets are planned for 2025.
Acknowledgments Calibration of the LIGO strain data was performed with a GstLAL-based calibration software pipeline (Viets et al. 2018). Calibration of the Virgo strain data was performed with C-based software (Acernese et al. 2022b). Data quality products and event-validation results were computed using the DMT (https://labcit. ligo. caltech. edu/~jzweizig/DMT-Project. html), DQR (https://docs. ligo. org/detchar/data-quality-report/), DQSEGDB (Fisher et al. 2021), gwdetchar (Macloed et al. 2021a), hveto (Smith et al. 2011), iDQ (Essick et al. 2020), and Omicron (Robinet et al. 2020) software packages and contributing software tools. Analyses relied upon the LALSuite software library (LIGO Scientific Collaboration 2018). PESummary was used to postprocess and collate parameter estimation results (Hoy & Raymond 2021).
For an exhaustive list of the software used for searching the GW signals and characterizing their source, see Abbott et al. (2021c). Plots were prepared with Matplotlib (Hunter 2007), seaborn (Waskom 2021), GWSumm (Macleod et al. 2021b), and GWpy (Macleod et al. 2021c). NumPy (Harris et al. 2020) and SciPy (Virtanen et al. 2020) were used in the preparation of the manuscript. This material is based upon work supported by  NSF 's  LIGO Laboratory  which is a major facility fully funded by the  National Science Foundation. The authors also gratefully acknowledge the support of 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 GEO 600 detector.
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 Leading-edge 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 Grantin-Aid for Scientific Research  (S)  17H06133  and  20H05639 ,  JSPS Grant-in-Aid for Transformative Research Areas (A )  20A203 :  JP20H05854 , the  joint research program  of the  Institute for Cosmic Ray Research, University of Tokyo ,  National Research Foundation (NRF) ,  Computing Infrastructure Project of Global Science experimental Data hub Center (GSDC)  at  KISTI ,  Korea Astronomy and Space Science Institute (KASI) , and  Ministry of Science and ICT (MSIT) in Korea ,  Academia Sinica (AS) ,  AS Grid Center (ASGC)  and the  National Science and Technology Council (NSTC) in Taiwan  under grants including the  Rising Star Program and Science Vanguard Research Program , Advanced Technology Center (ATC) of NAOJ , and  Mechanical Engineering Center of KEK.
As negative control, we included the reported lack of effect of the SINGLET OXYGEN RESISTANT 1 (SOR1) TF on PSBS1 transcript levels in diurnal culture  43  as well as a set of four TFs, designated TF1-4, whose knock-out or overexpression did not affect LHCSR3. 1 transcript levels (Supplementary Note 1, Supplementary Fig. 2  and Fig. 1b ). When we assessed the predicted ranks, as a measure of confidence assigned to the positive and negative ground truth data, we found that two approaches were clear outliers, showing sensitivity of 0%. More specifically, ARACNE  44  and global silencing  45  are unable to recover any positive literature interactions when using a network density threshold of 10% of all possible TF-TF and TF-target interactions (most prominently observable in the case of LCR1 interactions).