Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis

Khiem Vuong, Anurag Ghosh, Deva Ramanan*, Srinivasa Narasimhan*, Shubham Tulsiani* CVPR 2025

  License

We are currently sharing datasets only for research/non-commercial purposes. By downloading/using the data, the users will agree not to reproduce, duplicate, copy, sell, trade, resell, or exploit for any commercial purposes any portion of the images and any portion of derived data. They will also… See the full description on the dataset page: https://huggingface.co/datasets/kvuong2711/aerialmegadepth-tv.

The industry profile based on foreign and domestic sales which includes all members under film, television and video post-production industry (NAICS 512190) for one year of data.

Context

Events affect consumer behavior, driving people to stores in anticipation for certain occasions, yet also driving them away when life is too busy. The purpose of this data is to more thoroughly account for event impact on store sales thereby improving predictions.

Content

The comprehensive_event_calendar.csv file contains the events from the original M5 Competition dataset, but with additional holidays. Note that it does away with having 2 event columns and uses multiple rows to represent all the events on a given date. The dataset is meant to be used with the calendar.csv file of the M5 Competition, which contains the date information and the wm_yr_wk / d keys. Full disclosure, much of the data was taken from simple Google searches and Wikipedia for confirmation (https://www.wikipedia.org/).

Additional files with popular sporting events in the U.S. were included, as sports have traditionally had a significant impact on American culture. The basis of predictions is from the perspective of someone with knowledge up to May 22nd, 2016. Anything that would be unknown to a person as of that date is indicated as such in the data. That is, one can see finals events that are indicated as "tentative" and with teams that are blank because they would not have been known. The schedules for games, specifically NBA and NHL, are set in advance, so any information that would be known is indeed captured. Here are the following sporting events files with references used to report the data contained inside: - NFL_schedule.csv - https://www.pro-football-reference.com/years/2015/games.htm - https://en.wikipedia.org/wiki/2016_NFL_Draft - NBA_playoffs_schedule.csv - https://www.espn.com/nba/story/_/id/13728957/nba-tweaks-format-finals-ease-travel-demands - https://programminginsider.com/2016-nba-playoffs-conference-finals-tv-schedule-tnt-espn/ - https://www.basketball-reference.com/playoffs/NBA_2016_games.html - https://en.wikipedia.org/wiki/2016_NBA_Finals - NHL_playoffs_schedule.csv - https://www.cbssports.com/nhl/news/2016-nhl-playoffs-conference-finals-schedules-results-and-tv-listings/ - https://www.hockey-reference.com/leagues/NHL_2016_games.html - https://en.wikipedia.org/wiki/2016_Stanley_Cup_Finals - NCAA_FB_playoffs_schedule.csv - https://en.wikipedia.org/wiki/2016_College_Football_Playoff_National_Championship - NCAA_BB_playoffs_schedule.csv - https://en.wikipedia.org/wiki/2016_NCAA_Division_I_Men%27s_Basketball_Tournament#Bracket - MLB_playoffs_schedule.csv - https://www.baseball-reference.com/bullpen/2015_Postseason

Acknowledgements

Thank you to Samantha Gades for the banner photo taken from Unsplash.

This dataset displays the number of television receivers by country for the time period covering 1990 through 1997. Covered throughout this dataset is 150+ countries, This dataset was gathered from the United Nations Statistics Division. http://unstats.un.org/unsd/databases.htm Access Date: October 31, 2007

Information about Barnet Council's Pension Alternative and Private Funds - names and vintage years of private equity, venture capital, mezzanine, distressed, real estate/REIT, debt, infrastructure and hedge funds/partnerships in the Fund's portfolio. The names of the funds are: IFM Global Infrastructure (UK) B, L.P (2017) Alcentra European Direct Lending Fund II (2016) IIFIG Secured Finance Fund (2017) Partners Group Private Market Credit Strategies – Multi-Asset Credit 2015 Partners Group Private Market Credit Strategies – Multi-Asset Credit 2017 Partners Group Multi Asset Credit V S.C.A., SICAV-RAIF (2019) CBRE Global Alpha Property Fund Aberdeen Standard Long Lease Property Fund Adams Street 2019 Global Fund LCIV Private Debt (2021) LCIV Renewables Infrastructure (2021) Disclosures here are made for all relevant funds except for the below, please see Exemptions Notice. Partners Group Private Markets Credit Strategies S.A. - Compartment Multi Asset Credit 2015 (II) GBP Partners Group Private Markets Credit Strategies 2 S.A. - Compartment Multi Asset Credit 2017 (IV) GBP Partners Group Multi Asset Credit V S.C.A., SICAV-RAIF Dataset includes commitments made to each partnership, contributions drawn down since inception, distributions made to the Fund to date by each partnership, net asset value, internal rates of return (IRRs) for each partnership with and without the use of credit facility, investment multiple (TV/PI) for each individual partnership, the dollar amount of 'total management fees and costs paid' for each individual partnership, date as of which all the above data was calculated, names of all alternative asset partnerships partially and fully sold by The London Borough of Barnet Pension Found. This Dataset also includes the Quarterly Investment Monitoring Report.

The shape file shows TV ratings by Lower 48 Designated Market Areas (DMA) , also known as TV markets for 2007-2008. It also has estimates of number of household Television sets as well as the percent of TV viewership by DMA. Also known as Nielsen Ratings, the data is used heavily for advertising and marketing sectors.

Here are a few use cases for this project:

1. Advertising Cleanup: This model could be used by advertising or content agencies looking to clean up visuals by removing any competing brands or unnecessary text, ensuring they use images that only highlight the products or services they are promoting.

2. Television Broadcasts: TV networks could use this model to identify and remove any unsolicited logos or text present in the live feeds or prerecorded content to avoid copyright issues or unwanted product placements.

3. eCommerce Platforms: Online retailers could use this model to scan and remove logos or text from product images in instances where they sell unbranded or generic versions of products.

4. Image Restoration: This model could be used by digital artists and graphic designers to restore vintage images, illustrations or photos by efficiently removing any text or logos added over time.

5. Education Sector: In the education field, this model could help remove any kind of text or logo distractions from images used in digital textbooks or online resources, making it easier for learners to focus on the image content.

The total number of vehicles sold by TVS Motor Company was at **** million units in financial year 2024. This represented an increase compared to the previous year. TVS Motor is one of the largest two-wheeler manufacturers in India.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can TV Stock Regain Its Lost Luster?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Our Media Executives in North America Data helps you find and connect with the right decision-makers across the media industry. This package provides verified contacts so you can build strong business relationships, launch sales campaigns, or explore new partnerships. It includes executives from TV networks, news outlets, film studios, digital media, and publishing companies. Organized by industry and location, you can quickly identify leaders in New York, Los Angeles, Toronto, and other major hubs. Verified and updated regularly, this digital directory from List to Data gives you a competitive edge.

• Format: Excel / CSV (Instant Download)
• Fields: Name, Job Title, Company, Email, Phone
• Industries: TV, Film, Digital Media, Publishing
• Coverage: North America (U.S. & Canada)

Media Executives in North America Database

Our Media Executives in North America Database delivers powerful results for marketing and outreach. You can reach executives directly through cold calls, email, or SMS marketing, saving time and boosting productivity. It is GDPR-compliant and verified with 95% accuracy, making it ideal for sales teams, marketers, and recruiters looking to expand quickly in the media sector. With this tool, you can improve sales, grow brand visibility, and achieve fast results.

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Buy Media Executives in North America Data today and grow your network in the media industry. Our dataset is carefully checked by experts before delivery to ensure quality and accuracy. Available at an affordable price, it offers high returns by connecting you directly with key executives. Whether you are selling, partnering, or hiring, this product ensures faster results and reliable contacts. List to Data also offers 24/7 support to help you with any questions about the dataset.

• Affordable Pricing & Verified Leads
• Fresh Contacts: Rechecked Before Delivery
• Targeted Audience: U.S. & Canadian Media Executives
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This table contains 35 series, with data for years 1982 - 1989 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (6 items: Canada;Atlantic provinces;Ontario;Quebec ...), Trade group (10 items: Total; all trades;Apparel; dry goods; furniture and general merchandise;Tobacco; drugs and toilet preparations;Food ...), Wholesale trade components (3 items: Sales;Inventories;Stocks and sales ...).

Residential Property Price Index annual weights are presented in terms of the relative importance of the sales values of each Census Metropolitan Area (CMA), building type and construction type. The relative importance is calculated by dividing the weight of each of the six CMAs by the sum of the weights of those six CMAs. The weights correspond to a three-year average of the value of sales of residential properties. Annual weights are available from 2017.

Number of units and total sales of new motor vehicles by vehicle type and origin of manufacture, monthly.

Quarterly data on vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

Monthly Canadian manufacturers' sales, new orders, unfilled orders, raw materials, goods or work in process, finished goods, total inventories, inventory to sales ratios and finished goods to sales ratios for durable and non-durable goods by North American Industry Classification System (NAICS), in dollars unless otherwise noted. Unadjusted and seasonally adjusted values available from January 1992 to the current reference month.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of motion picture and video production (NAICS 512110), annual, for five years of data.

Number and value of mink pelts produced, by colour type, Canada and provinces. Data are available on an annual basis.