CTR is a population-based registry that holds records from twins born in Colorado in the United States of America, or who were living in Colorado at the time, as ascertained through school records. The study is lead by the Institute for Behavioral Genetics (IBG), University of Colorado Boulder. Recruitment of families began in 1984 with mailings from the Colorado Department of Health (CDH) to the parents of twins born in 1982. For twins born between 1982 and 1998, families that expressed interest in twin research were enrolled in the CTR. A similar procedure was used to contact and enroll twins born between 1968 and 1981 as young adults.

Clinical data on CTR, MND, T2DM and DM patients used in this study.

Union framework of the CTR elements at a scale of 1:5,000

Dataset

This dataset was created by Gaurav Dutta

Contents

The Feature Class Q_10K_CR408G_A_AB_CDA with Polygon geometry at the 1:10,000 scale represents the location and extension of hydrographic surfaces in particular Watercourse Bath Area. Derived from the database produced on a scale of 1:5,000 of the multiscale DBTI (Integrated Territorial Database), which provided for the adaptation and structuring, in relation to IntesaGIS standards, of the I and II lot of the CTRN and the subsequent update by means of the photogrammetric resumption of 2007. The geographical information was organised into hierarchical groups, following the model defined in the framework of the State-Regions Agreement on Geographical Information Systems.

Non-traditional data signals from social media and employment platforms for CTR stock analysis

Ctr Co Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

546 Global import shipment records of Adapter Ctr with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

2264 Global export shipment records of Ctr Upper with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Geographical representation of the execution lots of the historic regional technical map, created between 1971 and 1988.

Mexico Public Fin: Year to Date: NE: NP: FC: ICE: Pub Entity Under Direct Budget Ctr data was reported at 29,479.058 MXN mn in Feb 2019. This records an increase from the previous number of 18,303.578 MXN mn for Jan 2019. Mexico Public Fin: Year to Date: NE: NP: FC: ICE: Pub Entity Under Direct Budget Ctr data is updated monthly, averaging 12,536.980 MXN mn from Jan 1990 (Median) to Feb 2019, with 350 observations. The data reached an all-time high of 147,942.364 MXN mn in Dec 2018 and a record low of 223.573 MXN mn in Jan 1991. Mexico Public Fin: Year to Date: NE: NP: FC: ICE: Pub Entity Under Direct Budget Ctr data remains active status in CEIC and is reported by Secretary of Finance and Public Credit. The data is categorized under Global Database’s Mexico – Table MX.F005: Public Finance: Financial Position: Year to Date.

Non-traditional data signals from social media and employment platforms for CIVX stock analysis

Advertisement CTR (click-through-rate) prediction is the key problem in the area of computational advertising. Increasing the accuracy of advertisement CTR prediction is critical to improve the effectiveness of precision marketing. Based on the following datasets, a Kaggle competition was run for optimal advertisement CTR prediction models. The datasets contain the advertising behavior data collected from seven consecutive days, including a training dataset and a testing dataset.

Find details of Vermont Country Stores Dist Ctr Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

Picture of the union of the dials of CTR in scale 1:20.000 relative to the territory of the Veneto Region

These include statistical data generated by constructing the cxr-al14 dataset, data generated by deep learning model training, and raw data generated by model performance evaluation.

In 2020, nearly two-thirds of Google searches were zero-click searches. Out of the remaining 35 percent, 33.59 percent resulted in clicks on organic search results and 1.59 percent in paid search results.

United States GCR: sa: Current Transfer Receipts (CTR) data was reported at 174.200 USD bn in Mar 2009. This records an increase from the previous number of 166.600 USD bn for Dec 2008. United States GCR: sa: Current Transfer Receipts (CTR) data is updated quarterly, averaging 7.600 USD bn from Mar 1947 (Median) to Mar 2009, with 249 observations. The data reached an all-time high of 174.200 USD bn in Mar 2009 and a record low of 0.500 USD bn in Jun 1947. United States GCR: sa: Current Transfer Receipts (CTR) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A188: NIPA 2003: Government Revenue and Expenditure: Seasonally Adjusted.

United States SLCR: saar: Current Transfer Receipts (CTR) data was reported at 583.800 USD bn in Mar 2013. This records a decrease from the previous number of 597.500 USD bn for Dec 2012. United States SLCR: saar: Current Transfer Receipts (CTR) data is updated quarterly, averaging 76.200 USD bn from Mar 1947 (Median) to Mar 2013, with 265 observations. The data reached an all-time high of 660.800 USD bn in Dec 2010 and a record low of 1.600 USD bn in Mar 1947. United States SLCR: saar: Current Transfer Receipts (CTR) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A145: NIPA 2009: State and Local Government Revenue and Expenditure: Seasonally Adjusted at Annual Rates.

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.