dataesr/scientific-paragraphs-categorization huggingface dataset:
We set a batch size of 32 and the number of pretraining (N PRET EPOCHS) and refining (N REFINE EPOCHS) epochs to 40 and 60, respectively. We set to 64 the number of hidden units for the GRU cell. We remind that, for our framework, conDetSEC, exactly the same architecture is employed for all the different benchmarks involved in the experimental evaluation. For all the competing methods we use the TSLEARN python library  [28]  implementation of the Dynamic Time Warping and Soft Dynamic Time Warping measure. Experiments are carried out on a workstation equipped with an Intel(R) Xeon(R) W-2133, 3. 6Ghz CPU, with 64 Gb of RAM and one GTX1080 Ti GPU.
On some benchmarks (i. e. , JapVowel and ArabDigit), our framework achieves a perfect matching when enough input constraints are provided as input to the semi-supervised time series clustering process. While for other benchmarks (e. g. , HAR and PenDigits) the performance of conDet-SEC, in terms of absolute values, is also excellent (NMI and ARI around 0:9), for Dordogne and ECG5000 we can note slightly lower behaviors. This attitude is shared by all the competing approaches, pinpointing the fact that these datasets, compared to the other benchmarks employed in the experimental evaluation, constitute a challenging testbed for the semi-supervised learning algorithms. Finally, we can also observe that conDetSEC clearly benefits from the increasing amount of Must-and Cannot-link constraints, conversely to the competing approaches that are marginally impacted by the amount of available background knowledge.
Dataset To conclude the experimental evaluation, with the aim to visually inspect the behavior of conDetSEC, we depict the embedding generated by our approach on the ArabDigit dataset by varying the amount of random sampled constraints with values 1000; 3000 and 5000. The results of this analysis are reported in Fig. 7. To obtain the two dimensional representations, we apply the t-distributed stochastic neighbor embedding (TSNE) approach  [33]. For this evaluation we consider 100 instances per class. To make the visual results comparable, we firstly group together all the embedding generated by conDetSEC, no matter the amount of class samples were used to generated them. Successively, we deploy the TSNE approach and, finally, we separated the obtained projections to retrieve the original partitions.
In this way, all the TSNE projections exist in the same two dimensional space making the visual inspection fair. The experiment underlines the ability of conDetSEC to modify the data manifold exploiting the increasing amount of background knowledge. We observe that clear differences exist between the embeddings learnt when 1000 (Fig. 7  (a)), 3000 (Fig. 7 (b)) and 5000 (Fig. 7(c )) randomly sampled constraints are considered. Generally, increasing the amount of semi-supervision results in more clear cluster structure in which the classes are more distinguishable and separated from each other. As a final remark, we want to stress out how conDetSEC proved to be effective (constantly outperforming competing approaches) on benchmarks coming from different application domains and characterized by different structural characteristics.
Conclusion In this work we have presented conDetSEC, a new semisupervised (constrained) clustering algorithm especially tailored for multi-variate time series data. The proposed framework is based on Gated Recurrent Unit models and it includes two different stages: embedding generation and clustering refinement. Constraints information (or semi-supervision) is integrated in both stages. The evaluation on six benchmarks has demonstrated the effectiveness of conDetSEC and its flexibility on data coming from different application domains. The achieved results clearly point out that conDetSEC has the ability to effectively exploits the amount of supervision it can access. Additionally, we also conduct a visual inspection of the embedded representation learnt by conDetSEC that shows how the manifold learnt by the learning process is positively influenced by the amount of available background knowledge injected as pairwise constraints.
Despite the interesting behavior exhibited by conDetSEC, several limitations are still affecting our framework and need further research efforts. In the conducted research, we have made the strong assumption that clustering is performed once time series were collected. In a more realistic scenario multi-variate time series data could be acquired in a dynamic scenario (i. e. IoT or mobile sensors) where streams of information are generated. In such a context, incremental learning constitutes a possible way to limit human intervention and meet the specificity of data streams like concept drift as well as restricted access to the incoming data. Another limitation associated to our framework is that, as of now, conDetSEC is not capable to manage new must-or cannot-link constraints that can be provided in a successive moment.
Data collection A prospective collection was planned in all CLIP 2 , using a questionnaire developed by a team of sociologists and divided in two parts. The first part was self-completed by all patients referred to a CLIP 2 over a period of 12 months and collected sociodemographic and socioeconomic data (e. g. patient's age, sex, professional situation and education level). The second part was completed directly by the medical staff (clinical research assistant, nurse or clinician) and collected information about the trial and clinical data (e. g. cancer site, stage and grade). Three categories of care pathways were identified: internal, external and external-internal to the inclusion center. The internal pathway included patients with initial care received in the same hospital as the early-phase unit.
The education level and the number of people at home were forced into the multivariate model. P values <0. 05 were considered statistically significant. Several post hoc analyses were carried out. First, analyses were conducted in a subgroup of patients with non-female cancers (i. e. excluding breast and gynecological cancers) using the final multivariate logistic regression model constructed. These analyses were then replicated excluding patients with breast cancer. All analyses were carried out with SAS (version 9. 4) (SAS Institute Inc. , Cary, NC) and Odomatrix software.
We investigate the firm level investment responses to narrative shocks to average personal and corporate tax rates using a universal micro dataset of publicly traded U. S firms for the post-1962 period. By allowing for heterogeneous effects over the business cycle and accompanying monetary policy regime, as well as over firm-level characteristics, we show that : (i) corporate tax multipliers are negative overall, but this result is driven by smaller firms who face larger borrowing constraints, especially during high-unemployment periods or when the accompanying monetary policy is contractionary; (ii) while the magnitude and the significance of personal income tax multipliers are smaller on the aggregate, there is some evidence of positive personal tax multipliers in highunemployment state by large (dividend-paying) firms, which is consistent with the recent literature.
Data Our macroeconomic data include the narrative corporate and personal tax measures of  Mertens and Ravn (2012). Using narrative data allows us to identify causal effects of fiscal policy on investment, while using firm-level microdata allows us to study heterogeneity and control for firm characteristics. The merged macroeconomic time series together with the micro panel dataset comprises of close to 500,000 firm-year observations for the U. S. for the 1962-2019 time period. Our benchmark regression uses physical investment (which includes purchases of property, plant, and equipment) as the outcome of interest. We will also include some control variables like cash holdings and an indebtedness measure (leverage). 1  These financial variables are downloaded from COMPUSTAT. • Investment is given as the dollar value of property, plant, and equipment on the firm's balance sheet.
• Total assets is the dollar value of all assets on the firm's balance sheet. • Paying dividends status indicates whether the firm pays dividends in the current quarter. • Cash and short term assets is the dollar value of these assets on the firm's balance sheet. • Total debt is the dollar value of said liabilities on the firm's balance sheet. • Leverage is given as the ratio of total debt to total assets. COMPUSTAT data have numerous advantages compared with the information provided by the US Census Bureau Quarterly Financial Report (QFR), used in previous studies  (Eskandari and Zamanian 2020). First and foremost, Compustat spans all industries, whereas QFR covers limited number of manufacturing-oriented sectors. Second, COMPUSTAT data constitute the entire universe of active and inactive publicly held companies.
Methodology: Impulse Response Functions The results give impulse responses in investment change to a narratively identified tax shock in t = 0. The COMPUSTAT data provide a long, unbalanced panel of firm balance sheets from 1962 to 2019. In order to control for firm-specific characteristics, we do not aggregate the data into a time series. Because firms enter and leave the sample at different times, we restricted our sample to firms that were present through the entire impulse response horizon. As a robustness check, we considered filling out the panel with zeros for missing entries 2  and our results remained essentially the same. These two approaches eliminate the problem of firm selection into and out of the sample in response to tax shocks. A similar problem involves reporting frequency.
The top row provides a 'minimalist' specification with only a time trend. The bottom adds control variables, including prevailing corporate and personal income tax rates, and the value of 'cash and other short term assets' on the firm's balance sheet. A period of fiscal contraction is defined as one in which an increase in the federal funds rate compared to the previous quarter occurs. Figure  6  bins the data by firm size and monetary expansion/contraction. The most robust result is the negative response of small firms during contractions to corporate income tax shocks. There is also significant evidence of a negative response by large firms during contractions, and small firms during monetary expansions; however, both relationships weaken with the inclusion of control variables. Robust also is the negative response by small firms during contractions to personal income tax shocks.
Conclusion In the midst of a pandemic that forces governments to use fiscal policy front-an-center for sustained economic recovery, it is essential to understand the transmission mechanisms for the fiscal policy. Our paper contributes to the ongoing discussion by investigating how tax policy innovations affect firm investment behavior. While doing so, we tackle a number of issues that may have plagued the previous literature. We not only use a universal dataset of public firms, which minimizes the sample selection bias, but also a disaggregated measure for corporate and personal taxes (as opposed to total tax liability) which solves the aggregation problem. Moreover, we identify the aforementioned tax shocks using a narrative approach, and therefore take into consideration the gap between the announcement and implementation of policy innovations.
The data include daily pipeline gas flows across gas balancing zones of the pipeline network and storage facilities  (ENTSOG, 2022b) , daily power production from gas  (ENTSOE, 2022) , and the monthly/annual partitioning of gas used to different sectors, including households, commercial and public buildings, industry, and power  (Eurostat, 2022a, b, c). The supply-storage-consumption amounts and shares from Russian supply and all other supply sources were calculated from the above data based on mass balance. We then investigate how a shortage of Russian gas equivalent to a complete stop of supply could be filled by reducing demand for heating, increasing power generation from other sources, increasing production in the EU, and increasing international imports both at liquefied natural gas (LNG) terminals and pipelines from non-EU countries other than Russia.
We further consider existing transmission constraints on the intra-EU gas reallocation with the current pipeline infrastructure. We provide two datasets, the "EU27&UK daily gas supply-consumption" (EUGasSC) with the share of different supply sources including Russia imports, LNG imports, EU gas production, and pipeline imports from other countries, and the "EU27&UK daily gas reduction potential" (EU-GasRP). The EUGasSC data give the country-and sectorspecific natural gas supply-storage-consumption at a daily resolution. These data allow us to quantify the shortfalls if Russian imports were to terminate. The EUGasSC data can be used for the country-and sector-based policy decisionmaking and further socioeconomic analysis. The EUGasRP shows the daily gas consumption saving potential that would be achieved by reducing demand for heating and increasing power generation from coal, nuclear, and biomass.
Data collection The workflow of this study is shown in Fig. 1a. We collect several open datasets as input data: (1) ENTSOG (European Network of Transmission System Operators for Gas) daily physical pipeline flow (ENTSOG, 2022b), which was used to simulate gas transmission, consumption, storage, and imports; (2) hourly ENTSOE (European Network of Transmission System Operators for Electricity) electricity generation  (ENTSOE, 2022; Liu et al. , 2020) , which was used to estimate how the Russian gas gap could be alleviated by increasing coal and nuclear power (Sect. 2. 3. 2); (3) gas import and energy balance datasets from Eurostat, which were used to adjust/complete sectoral consumption values for ENTSOG data and as cross-validation of annual consumption totals; (4) BP Statistical Review of World Energy (BP, 2022), which was used to estimate the potential global increment capacity for LNG import and within-EU production as well as for data cross-validation; and (5) ERA5 daily 2 m air temperature data  (Hersbach et al.
Daily gas supply and consumption To quantify country-and sector-specific gas supply and consumption, we built a graph network simulation model of daily physical gas flows for the 2016-2022 period. The model simulates gas supply, temporary storage, and consumption sources for households and public buildings, power generation, industry, and other sectors in each country, as shown in Fig. 1b. The detailed equations and the model are presented in the Supplement. As model input data, we completed the raw ENTSOG data with Trading Hub Europe (THE) for German consumption (Trading Hub Eu- rope, 2022) and with E-control for Austria consumption  (E-Control, 2022). Briefly, the simulation evaluates the daily share of the supply/consumption source of nodes (country or region) and edges (pipeline) by iteratively solving the mass balance of the physical gas flow in the network.
We assumed that the mass balance of each supply source is achieved daily for the transmission network and storage so that the simulation results from the previous day are used as initial values for the next day. The gas consumption in the simulation was split into five sectors based on the Eurostat energy balance datasets (Eurostat, 2022b, c): household buildings, public buildings, power, industry, and other sectors. The simulated sector splitting values are validated with data reported by a few counties for which ENTSOG data directly provide details on usage splitting for the distribution (DIS, covering heating and other sectors) and final consumer (FNC, covering power and industrial sectors) groups of sectors. The details are presented in the Supplement. We performed the simulation from 1 January 2016 to 28 February 2022 for each EU27 country and the UK with a daily resolution, and we separated the share of different supply sources (Russia, Norway, Algeria, Azerbaijan, Libya, Serbia, Turkey, LNG imports, and EU production) in the consumption sectors listed above, which yields the EUGasSC dataset.
The daily maximum potential gas saving capacity of the first two solutions define the second dataset: EUGasRP. Note that we only investigated short-term solutions that could be immediately implemented (e. g. , for the upcoming year) given strong assumptions that (1) the gas supply for the industry will be prioritized and remains at current levels and (2) that no massive increase in power production from renewable energy will happen in the next year, although long-term investments could partly substitute Russian gas use by renewables.
Reduced gas use in the residential household and public building sectors The potential gas consumption reductions due to reduced heating in buildings were estimated for each country based on empirical temperature-gas consumption (TGC) curves, similar to those shown by  Ciais et al. (2022). The TGC curves were constructed using daily consumption from EU-GasSC and daily population-weighted air temperatures based on the Eurostat population dataset (Eurostat, 2022d) and ERA5 daily 2 m air temperature data  (Hersbach et al. , 2018). Figure  2  shows an example for France, outlining the TGC curve fitting (Fig. 2a ), how the consumption reduction was estimated (Fig. 2b ), and a time series of reduced gas consumption (Fig. 2c ). The TGC curves were fitted with a two-segment linear regression separated by a critical temperature (the start-heating temperature), as shown in Fig.
Data validation and uncertainty estimation We validate the EUGasSC dataset with the Eurostat (Eurostat, 2022a, b, c) and BP Statistical Review of World Energy datasets for the following variables: (1) annual total gas consumption (Fig. 3a ), (2) monthly total gas consumption (Fig. 3b ), (3) annual total LNG imports (Fig. 3c ), (4) annual total EU gas production (Fig. 3d ), and (5) total gas consumption in each country (Fig. 3e ). The results show low discrepancies for the annual total consumption (12 ± 5 %, where ± denotes the standard deviation across years or all EU countries), monthly total gas consumption (11±7 %), annual total LNG imports (0 ± 14 %), and total gas consumption in each country (9 ± 7 %, excluding Spain and Latvia-Estonia), whereas large differences were found for the annual total EU gas production (-42 ± 12 %), Spain (-65 %), and Latvia-Estonia (-153 %).
A negative difference means that our dataset has lower values than Eurostat or BP data. The validations of our dataset with Eurostat are done for the total consumption, even though Eurostat was used for splitting the consumption sectors in EUGasSC (Sect. 2. 2). Thus, the use of Eurostat data for consumption attribution and the national total cross-validation is not circular in our approach. https://doi. org/10. 5194/essd-15-949-2023 Earth Syst. Sci. Data, 15, 949-961, 2023 The larger differences between EUGasSC and Eurostat were found for the year 2020 because the UK data were not provided in the Eurostat dataset due to the withdrawal of the UK from the EU. Although our validation results indicate an overall good quality of our dataset, the following uncertainties still exist: (1) we might underestimate the EU production, as EUGasSC has significantly smaller production values compared with both the Eurostat and BP datasets (Fig.
3d ), and (2) the consumption differences in each country might bias our analysis of potential solutions for the Russian gap, particularly in Latvia-Estonia as our dataset underestimates their gas consumption. Other uncertainties from collecting, processing, and analysis mainly arise from the fact that (1) values and figures in this paper follow the ENTSOG data collected in April 2022, but the ENTSOG database is regularly corrected and updated, even for very early data (we will also update the EUGasSC and EUGasRP datasets regularly); (2) we estimate the daily Russian supply share based on a simulation that assumes a daily balance of the pipeline network, which might oversimplify gas balancing processes; (3) our estimation of sectoral consumption might not be able to reproduce unusual daily consumption variations, as our values were estimated based on daily temporal total consumption variation patterns from ENTSOG and monthly (thus smoothed) sectoral Eurostat energy balance to attribute total consumption to each sector; and (4) we estimate potential solutions for alleviating the Russian supply gap based on empirical capacities using a number of assumptions and without considering social, economic, international cooperation, and geopolitical barriers (as they are important but not within the scope of this study).
Gas supply shares and recent trends We analyzed the gas supply shares and trends based on the EUGasSC dataset for pipeline imports from Russia (RU), Norway (NO), LNG imports (LNG), other imports through pipelines connected to the EU (Other, such as from Azerbaijan and Algeria), and EU production (PRO) from 1 January 2019 to 28 February 2022 at the breakout of the Russian invasion of Ukraine. As shown in Fig. 5 , we found that there was a relatively constant gas supply structure before 2021. Annual changes for all of the supply sources ranged from -1. 6 % to 1. 6 %. The supply shares before 2021 in decreasing order were as follows: Russian (36 ± 4 %) > Norwegian (26 ± 3 %) > LNG (21 ± 4 %) > EU production (10±2 %) > other exporters (5±2 %). However, significant changes in supply sources occurred after 2021, particularly for Russia (decreased by 11 %), LNG (increased by 9 %), and Norway (increased by 4 %).
Potential solutions We present the country-based capacities for alleviating the Russian gas gap with a daily resolution from the EU-GasRP dataset. These potential solutions (Sect. 2. 3) include demand-side reduction for household and public buildings; increasing power production from coal, nuclear, and biomass; and increasing EU production as well as LNG and pipeline imports. An overview is presented in Fig. 7. We found that, according to our demand reduction scenarios, saving gas with respect to space heating in buildings could cover 17 %-23 % of the total Russia gap. TGC curves and reduction estimations for each country and scenario are presented in Figs. S3-S5 in the Supplement. An additional fraction of 18 %-41 % of the total Russian gas gap could be replaced by substituting gas with coal, nuclear, and biomass sources for power generation.
Increasing coal-fired power would save 218-497 TW h of natural gas, and nuclear power would save another 142-317 TW h. In our scenarios, France alone contributes 40 %-51 % to the nuclear power capacity. The uncertainty ranges of the solutions are estimated from the different scenarios presented in Sect. 2. 3. 1 (for the heating sector) and Sect. 2. 3. 2 (for the power sector). On the supply side, we estimate, from recent data on production and production change (Sect. 2. 3. 3), that increased natural gas production in EU countries and the UK could only fill up to 5 % of the gap. However, our dataset might underestimate European gas production, as discussed in Sect. 3. We further estimate that the USA and Australia might be able to produce up to an extra 470 TW h of LNG, and the rest of the world might be able to produce up to an extra 414 TW h of LNG; moreover, other exporters, including Norway, might be able to produce up to an extra 115 TW h of gas carried by pipelines (Table  S2 ).