The video shows the moon&#x27;s shadow racing across the country during the 2024 total solar eclipse.

Solar eclipse's shadow caught passing over the US in NASA–NOAA video

Despite efforts to reduce greenhouse gases, 2023 saw another steady increase in the amount recorded in the Earth&#x27;s atmosphere, according to a new report by the National Oceanic and Atmospheric Administration.

NOAA: Global greenhouse gases continued steady climb in 2023

The last time planet-warming greenhouse gases were this abundant in the atmosphere, seas were 75 feet higher than today.

NOAA finds greenhouse gases spiked to record in 2023

Atmospheric levels of the three &quot;most important&quot; greenhouse gases — methane, CO2 and nitrous oxide — reached record highs again in 2023.

3 ‘Most Important’ Greenhouse Gases Reached Record Highs Again Last Year: NOAA Scientists - EcoWatch

Timelapse video by NOAA&#x27;s GOES East satellite shows the shadow cast by the Moon covering the Sun&#x27;s face as it races across North America.

Watch: Lunar shadow races across America during solar eclipse as seen on NOAA satellite

Weather Forecast Office · On Monday April 8, 2024 a total solar eclipse will cross North America. The eclipse will be seen across portions of Mexico, the central and eastern United States, and southeastern Canada. The path of totality will first enter the United States in Texas, then advance ...

April 8, 2024 Total Eclipse

This was the twelfth consecutive year that carbon dioxide levels rose by more than 2 parts per million

Carbon dioxide in 2023 comparable to 4.3 billion years ago as global greenhouse gas levels hit all-time high: NOAA

BOSTON — A North Atlantic right whale mother found dead off the Virginia coast late last month died from a vessel strike, the National Oceanic and Atmospheric Administration recently announced.

NOAA: Right whale found dead was struck by vessel | News | newburyportnews.com

The Sun recently emitted two major solar flares, but officials at NOAA said the flares do not seem to have caused Thursday&#x27;s nationwide outage of cellular service.

'Unlikely' that solar flares caused cellphone outage across US, NOAA says

Last year’s historic tropical storm may not have been so historic after all. What was initially believed to be Tropical Storm (and former hurricane) Hilary was actually a post-tropical low when it hit California, according to a report by the National Oceanic and Atmospheric Administration’s ...

Hilary wasn’t tropical storm when it hit California: NOAA | KTLA

Noala

noala

This is by ( ). Phan writes about business and is the co-host of the Not Investment Advice podcast. Twitter thread Trung Phan @TrungTPhan source: 05-26-2023 https://twitter.com/TrungTPhan/status/1661769508439343104?embedable=true&thread=true The lead image for this article was generated by HackerNoon's via the prompt "Music.” AI Image Generator

Behind the Incredible Soundtracks of Hans Zimmer and Christopher Nolan

The DeFi space seems overcrowded with launchpads and projects with lucrative participation terms for crypto enthusiasts. Still, there is much room to stand out in this niche, and the TAF project illustrates how to do it. The team behind BitcoinTAF is happy to introduce the TAF Token – an infrastructural token that can be used for transactions in its system, earn high APY on the TAF Strongbox platform, and get unique terms for participation in the early IDO pre-sales and launches. TAF has taken a different approach to launchpad participation by giving its users (TAF Token holders) exclusive privileges for early project entry and special IDO prices within the system. The TAF Token platform operates on the BSC blockchain, giving the community a safe and robust environment for token staking, product and service purchase, and participation in IDO pre-sales. The TAFKey program assigns one of four levels to users, each coming with special terms and privileges in the system. The TAF platform and launchpad are quickly rising to enable different operations for users: New project funding via the TAF Pad Project launches via TAF Launch Token staking in the TAF Strongbox NFT sale on the TAF Market This arrangement has enabled TAF to become a robust incubator for high-potential projects, enjoying a rapidly growing community of loyal users. With the project attracting much curiosity and interest among crypto enthusiasts, we have compiled an interview with . It is the outcome of our AMA sessions and weekly calls at which the team answered community members' questions and shared this project's value and long-term plans. Bill Nolan, CEO of TAF Q: So, Bill, what motivated you to start this project? Can you share any personal stories and references you used when creating TAF? A: We've been thinking about how to get more people into the DeFi project funding. Our assessment showed that the entry barriers are pretty high on most early-stage funding launchpads. So, life-changing opportunities for financial freedom are available only to the elite. We saw potential in lowering those barriers and giving more people entry to promising projects at the early stages of funding, awarding loyalty points by the number of tokens they hold and giving users access to private funding rounds. Another aspect we work on is the ability to fund great projects by giving them enough visibility among interested investors. It's a shame to see how many projects fail because they are overlooked, and we're here to give them a market opportunity by allocating funding. Q: You call TAF a 'metaverse launch ecosystem.' Could you briefly describe the main idea behind the project? A: In a nutshell, our system is a multichain DeFi project focusing on early-stage funding, staking, and launchpad services for IDOs. We partner with dozens of well-established players in the blockchain industry to ensure seamless and secure operations for our users. Thus, the TAF ecosystem can render broad support for new DeFi project launches. Q: What is your main competitive advantage? Name three killer features of TAF that you believe will take it ahead of competitors? A: First of all, it's our community. TAF has attracted investors from all strata and levels of experience. We currently have HNW investors with hands-on expertise in early-stage funding, full-time and part-time traders, and newbies in the early-stage funding space. Another unique feature of our ecosystem is the pre-sale program TAFPad, where we grow promising projects and not only give users early-stage entry opportunities but also provide funding to those  projects. Third, the TAF Strongbox program helps loyal users gain lucrative APY from the tokens they lock in the TAF ecosystem. Using the funds from Strongbox, we manage to fund high-potential projects via the TAF launchpad, enabling turnkey IDO launches for project owners. Q: What can you say about TAF tokenomics and its role in the TAF ecosystem? A: Our system's native token, TAF, can be bought on PanCakeSwap and AAX. It gives holders numerous benefits in our ecosystem, ranging from high APY for its staking in the TAF Strongbox to access to private seed and pre-sale funding rounds. Additionally, TAF can be used to pay for transactions in our TAFPay system (coming soon). To get the Platinum TAFKey level within our system, a user needs to hold a minimum of 50K TAF Tokens for 90 days. Q: Many investors hold tokens for the short term without giving much thought to its long-term value. What benefits would you like to share to convince your community about the long-term benefits of project membership to raise the number of holders? A: We spend time and effort educating about the long-term benefits of TAF token ownership and crypto investments. Users can find numerous super useful educational videos dwelling on the operational aspects of cryptocurrency and long-term investment strategies in our YouTube and Medium resources. our community Holding TAF tokens offers many perks, such as stably high APY on the Strongbox staking platform, access to private seeds and pre-sales via the TAFKey program, and priority access to the NFT sales planned for the near future. Also, you need to factor in the token's appreciation once the TAF ecosystem matures and rises in popularity. And finally later this year when  we transition TAF into a DAO you will need to hold a TAF NFT in order to participate in the governance of the project. We've recently offered the first round of 21-day staking contracts, where 20% of the total circulating TAF supply was locked. Our users also show a higher-than-average retention rate of coins purchased during the pre-sale round. Q: Great, and what about your staking program? What are the terms? A: Strongbox is a platform where users can earn a passive income from staking their TAF tokens and the coins received during pre-sale and seed rounds. They need to hold TAF tokens to receive a tier in the TAFKey system. So, TAF members don't simply keep the tokens in their wallets, getting high APY instead and receiving dual benefits from TAF token ownership. Q: Do you have a scaling strategy in mind? What are your long-term plans for the TAF project? A: Yes, the TAF project was initiated with long-term goals in mind. When creating the TAF architecture, we included the functionality necessary for turning it into a DAO. So, the TAF platform is a project with far-reaching plans to serve people. Next comes the NFT marketplace and game creation in partnership with our brand ambassador TAFFY. The full-scale IDO launchpad operations are scheduled for Q4 of 2022, and there is so much more in store for our users. Everyone will benefit from joining in. Q: Where can the interested users learn more about TAF project? We have an active following on social media, where every interested investor can join in to learn more about us, ask questions, and stay up to date with TAF news. Feel free to reach out to us via any channel, and welcome on board! By following us on social media, you’ll be the first to learn all the latest news, like reaching and lucrative staking terms for 90-day contracts reaching . 14.5M in staked TAF tokens 60% APY

Powering the Next Generation of DeFi: Q and A with TAF CEO Bill Nolan

Impulsando la próxima generación de DeFi: preguntas y respuestas con el CEO de TAF, Bill Nolan

“In these impressively unprecedented times…” Impressive people build unprecedented products. One such impressive human of is Read on for 's Noonies interview, right below the ad break. Hacker Noon Jørgen Svennevik Notland from Norway, who's been nominated in the Noonies Decentralization Award Category . Jørgen 🚀 This Year's Noonies were made possible by: Sustany Capital , .TECH Domains , Grant for the Web , Skillsoft , Flipside Crypto , Udacity , and Beyondskills ! There are over $10K+ & 3K+ Yrs Domain Registration Up for Grabs. VOTE TODAY! 🚀 1. , which 2020 Noonie have you been nominated for? Jørgen Hacker Noon Contributor of the Year - REGULATION 2. Tell us a bit about yourself. Bitcoin developer, co-founder, maker. I like to take the tram up to the top of a hill and then skateboard down back home, and it kind of feels like a ski lift. 3. Tell us about the things you make / write / manage / build. I'm currently working on an R&D project for making online voting more secure with bitcoin. Also, I'm the co-founder of a mental health app-startup. 4. What are you most excited about right now? Yerba mate, not coffee. 5. What are you worried about right now? Will I ever create a successful startup? 6. What's the most useful advice you've ever given somebody? Doers > Talkers 7. How has the pandemic changed your life and/or career? Less leisure/travelling, more work. 8. If we gave you $10 million to invest in one thing right now, where would you put it? Bitcoin. 9. What's an opinion you have that most people don't agree with? Ehm, yerba mate is better than coffee? Maybe: "Bitcoin is money" 10. Which apps can't you live without? Twitter, Radio Norway App, audible, scooter-sharing, ride-sharing. 11. What are you currently learning? Team communication, applying build-measure-learn to a startup, building an Android wallet from scratch. VOTE for Jørgen Svennevik Notland as Hacker Noon Contributor of the Year - REGULATION before voting closes on 12 October, 2020! With gratitude to Hacker Noon's Partners: , , , , , , and ! 2020 Noonies Sustany Capital .TECH Domains Grant for the Web Skillsoft Flipside Crypto Udacity Beyondskills

Jørgen Svennevik Notland is Very Excited About Yerba Mate

ADA: A Powerful Data Augmentation Technique for Improved Regression Robustness

The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion.” India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa, according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre. Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform, which is able to simulate global weather patterns in great detail. Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The "Scalable Ensemble Envelope Diffusion Sampler" (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances. In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” “Humans are a lot better at that than AI is right now,” she said. The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” kill hundreds of people In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion .” The total cost of these 400 events exceeds $2.785 trillion India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. The frequency of flooding is getting worse Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa , according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Widespread flooding in West and Central Africa Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre . Red Cross Climate Centre Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform , which is able to simulate global weather patterns in great detail. Earth-2 platform Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The " Scalable Ensemble Envelope Diffusion Sampler " (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. Scalable Ensemble Envelope Diffusion Sampler SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances . Science Advances In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. joined forces Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. announced These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” Smithsonian Magazine interviewed “Humans are a lot better at that than AI is right now,” she said.

Will AI Ever Be Able to Predict Disasters and Save Lives?

Streamline Your API Tests: Less is More TL;DR: Use primitive steps to verify API behavior instead of direct requests. Problems Unnecessary API calls
Slow test performance
Overcomplicated validations
Fragile tests
Slow feedback loops
Maintainability
Misleading test results Solutions Test primitive outcomes
Validate responses directly
Avoid extra API steps
Simplify test logic Context When you test an API, you might fall into the trap of using multiple API requests to verify a single operation. Making a POST and immediately making a GET to check the existence of the resource with the payload is a straightforward solution. This pattern adds complexity and makes your tests slower. Also, if your GET operation breaks, you will have many tests failing not relating to the test GET operation. The epitome of testing is a single broken test for a wrong behavior. Instead of verifying resource creation through a GET and inspecting the JSON response, you can focus on primitive steps. Check if the POST succeeded by validating the status code or checking the resource's existence. Sample Code Wrong Feature: Movie Management
  Scenario: Create a movie and verify
    When I send a POST request to "/movies" with the following data:
      | title     | director          | year |
      | Klendathu | Christopher Nolan | 2010 |
    When I send a GET request to "/movies/Klendathu"
    Then the response status should be 200
    And the response should contain:
      | title     | director          | year |
      | Klendathu | Christopher Nolan | 2010 | Right Feature: Movie Management
  Scenario: Create a movie and verify
    When I create a movie with the following details:
      | title      | director          | year |
      | Klendathu  | Christopher Nolan | 2010 |
    Then the movie "Klendathu" should exist in the system
    ## This is a low level existance postcondition
    ## Without relying on a GET request Detection [x]Semi-Automatic You can detect this smell when you see test steps that use a GET request to verify the success of a POST. Tags Testing Level [x]Intermediate AI Generation AI generators often create this smell when generating API tests, defaulting to chaining requests and validating the entire resource, rather than focusing on the operation's outcome. AI Detection AI tools can potentially detect this smell with proper instructions. You could train an AI to identify patterns of consecutive POST and GET requests in scenarios and suggest consolidating them into more abstract, primitive steps. Try Them! Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Conclusion Focus your acceptance tests on the direct results of operations like POST. Avoid making a GET request afterward to verify what you already know. Relations https://hackernoon.com/code-smell-259-control-your-environment-to-avoid-test-failures?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vi-cmj31om?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xi-sit35t1 More Info https://hackernoon.com/fail-fast-philosophy-explained-si963vk9?embedable=true Disclaimer Code Smells are my opinion. Credits Photo by Dmitriy Demidov on Unsplash As a rule, software systems do not work well until they have been used, and have failed repeatedly, in real applications. Dave Parnas https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code Streamline Your API Tests: Less is More Streamline Your API Tests: Less is More TL;DR: Use primitive steps to verify API behavior instead of direct requests. TL;DR: Use primitive steps to verify API behavior instead of direct requests. Problems Unnecessary API calls Slow test performance Overcomplicated validations Fragile tests Slow feedback loops Maintainability Misleading test results Unnecessary API calls Slow test performance Overcomplicated validations Fragile tests Fragile tests Slow feedback loops Slow Maintainability Misleading test results Solutions Test primitive outcomes Validate responses directly Avoid extra API steps Simplify test logic Test primitive outcomes Validate responses directly Avoid extra API steps Simplify test logic Context When you test an API, you might fall into the trap of using multiple API requests to verify a single operation. Making a POST and immediately making a GET to check the existence of the resource with the payload is a straightforward solution. POST GET This pattern adds complexity and makes your tests slower. Also, if your GET operation breaks, you will have many tests failing not relating to the test GET operation. GET GET The epitome of testing is a single broken test for a wrong behavior. Instead of verifying resource creation through a GET and inspecting the JSON response, you can focus on primitive steps. GET Check if the POST succeeded by validating the status code or checking the resource's existence. POST Sample Code Wrong Feature: Movie Management
  Scenario: Create a movie and verify
    When I send a POST request to "/movies" with the following data:
      | title     | director          | year |
      | Klendathu | Christopher Nolan | 2010 |
    When I send a GET request to "/movies/Klendathu"
    Then the response status should be 200
    And the response should contain:
      | title     | director          | year |
      | Klendathu | Christopher Nolan | 2010 | Feature: Movie Management
  Scenario: Create a movie and verify
    When I send a POST request to "/movies" with the following data:
      | title     | director          | year |
      | Klendathu | Christopher Nolan | 2010 |
    When I send a GET request to "/movies/Klendathu"
    Then the response status should be 200
    And the response should contain:
      | title     | director          | year |
      | Klendathu | Christopher Nolan | 2010 | Right Feature: Movie Management
  Scenario: Create a movie and verify
    When I create a movie with the following details:
      | title      | director          | year |
      | Klendathu  | Christopher Nolan | 2010 |
    Then the movie "Klendathu" should exist in the system
    ## This is a low level existance postcondition
    ## Without relying on a GET request Feature: Movie Management
  Scenario: Create a movie and verify
    When I create a movie with the following details:
      | title      | director          | year |
      | Klendathu  | Christopher Nolan | 2010 |
    Then the movie "Klendathu" should exist in the system
    ## This is a low level existance postcondition
    ## Without relying on a GET request Detection [x]Semi-Automatic [x] Semi-Automatic [x] You can detect this smell when you see test steps that use a GET request to verify the success of a POST . GET POST Tags Testing Testing Level [x]Intermediate [x] Intermediate [x] AI Generation AI generators often create this smell when generating API tests, defaulting to chaining requests and validating the entire resource, rather than focusing on the operation's outcome. AI Detection AI tools can potentially detect this smell with proper instructions. You could train an AI to identify patterns of consecutive POST and GET requests in scenarios and suggest consolidating them into more abstract, primitive steps. POST GET Try Them! Remember: AI Assistants make lots of mistakes Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Copilot

Copilot Gemini

Gemini Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Copilot Copilot Copilot Copilot Copilot Copilot Copilot Copilot Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Conclusion Focus your acceptance tests on the direct results of operations like POST . POST Avoid making a GET request afterward to verify what you already know. Relations https://hackernoon.com/code-smell-259-control-your-environment-to-avoid-test-failures?embedable=true https://hackernoon.com/code-smell-259-control-your-environment-to-avoid-test-failures?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vi-cmj31om?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vi-cmj31om?embedable=true https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xi-sit35t1 https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xi-sit35t1 More Info https://hackernoon.com/fail-fast-philosophy-explained-si963vk9?embedable=true https://hackernoon.com/fail-fast-philosophy-explained-si963vk9?embedable=true Disclaimer Code Smells are my opinion . opinion Credits Photo by Dmitriy Demidov on Unsplash Dmitriy Demidov Unsplash As a rule, software systems do not work well until they have been used, and have failed repeatedly, in real applications. As a rule, software systems do not work well until they have been used, and have failed repeatedly, in real applications. Dave Parnas Dave Parnas https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series. How to Find the Stinky Parts of your Code How to Find the Stinky Parts of your Code

Code Smell 272 - API Chain

Textbooks Are All You Need: Conclusion and References

Modern mobile weather apps for iOS and Android keep you up-to-date with rain and extreme heat, and can be life-saving during hurricane season. In this article, I'll analyze how these apps have evolved and explore their role for people living in a country that suffers from the threat of tropical storms and their consequences every year. How did we live without weather apps, or where did it all start? Immediately after the launch of the App Store in July 2008, the first iOS weather apps began to appear on this platform. They provided basic weather information, such as temperature, forecasts, and radar images. The first weather apps were basic with elementary features. They did not have interactive maps in their functionality; the apps duplicated the weather forecast from national weather agencies. At that time, weather maps were just static images. Over time, interactive maps began to appear. This was a significant step in the evolution of weather mobile apps, transforming the category and setting a new standard. Gradually, indie developers, and then weather-related TV channels in the US began to create their weather apps using interactive maps. These maps allowed the user to navigate, zoom in and out, and see various indicators. On the App Store, software developers started experimenting with new devices and weather maps. That's how Dark Sky appeared (and was among the leaders in this category in the App Store until Apple acquired it). The second important point in the evolution of mobile weather apps is the use of radar data with interactive maps. The third important point in the development of weather apps is an opportunity to use the location of the device and adjust the forecast exactly to the user's request. That is, to receive alerts about a certain intensity of precipitation, high temperature, wind speed, etc. Such data personalization in weather apps and selecting information important to the user has made them more convenient for each target audience. How weather apps changed the game during hurricane seasons? Mobile weather apps have evolved a lot, raising awareness and allowing people to plan their time with the weather in mind. Apps, as a whole, have changed our world and our perception. This also applies to tracking the movement of hurricanes because now we have the weather forecast in our pocket. Of course, this includes the significant role of TV in weather forecasts and a well-established system of notifications from weather agencies. However, in contrast, mobile weather apps make information consumption more personalized and accessible. You can now track the weather in a few seconds and at a convenient time. Weather apps are the best trackers for natural phenomena like tornadoes (which occur very quickly and are less predictable). For example, citizens of Puerto Rico are the first to "welcome" tropical storms from the Atlantic Ocean due to their geographical location. For at least six months of the year, weather apps are the most viewed apps in this country because life can depend on them. A hurricane can do much more damage if no one knows about it. Ignorance, not the hurricane itself, is the biggest problem for coastal Americans. The evolution of weather apps Back in 2013, I was interested in developing a weather mobile app that offered users more than just a standard forecast from a weather station. Living in Florida, I know firsthand the importance of providing users with accurate long-term weather forecasts (up to 48 hours). It's crucial to warn users about tropical storms and hurricanes, even if these storms only slightly affect their area. For example, the Rain Viewer weather app my team and I created has detailed rainfall maps, and tracks storms in different regions. Of course, this is not a panacea for the user, but such data is a valuable tool during hurricane season. Last year over 700 weather apps appeared in the App Store, and three times as many in Google Play. The qualitative characteristics of forecasts include not only visualization of maps but also accurate data. For this, the app must have extensive radar coverage (today, most mobile weather apps use radar data). The bad news is that climate change makes yearly tropical storms and hurricanes more destructive. The good news is that modern mobile apps are rapidly evolving. Now they allow you to predict the effects of hurricanes, such as strong winds, intense precipitation, and extreme heat. Weather apps designed for weather enthusiasts, outdoor workers, commuters, vacation planners, and those who like to keep everything under control, including the weather, offer the following hurricane tracking tools: Hurricane path tracking considering the change in its strength (categories),
Description of the hurricane’s characteristics at its location,
Detailed hurricane timeline,
Alerts about the formation of a hurricane and its approach to the current location,
Current precipitation on the radar map and its forecast for the next 24-48 hours,
Wind speed forecast map or hourly wind speed readings to track hurricane force winds. Add to that the ability to: Track temperature on the map,
View weather forecast on home screen widgets without launching the app,
Get customizable push notifications about upcoming precipitation (you can set up the intensity of rainfall to be notified about),
Visualize precipitation and cloud cover in real time using radar and satellite maps. While weather apps still use core data from NOAA (National Oceanic and Atmospheric Administration) and NHC (National Hurricane Center), they have completely personalized weather alerts and made tropical storm tracking as quick and easy as possible. Radar data and map visualization have also brought the use of these apps to a new and more interactive level. Many users rely on weather apps for evacuation alerts during approaching hurricanes, even though this information is primarily provided by weather agencies. Modern mobile weather apps for iOS and Android keep you up-to-date with rain and extreme heat, and can be life-saving during hurricane season. In this article, I'll analyze how these apps have evolved and explore their role for people living in a country that suffers from the threat of tropical storms and their consequences every year. How did we live without weather apps, or where did it all start? Immediately after the launch of the App Store in July 2008, the first iOS weather apps began to appear on this platform. They provided basic weather information, such as temperature, forecasts, and radar images. The first weather apps were basic with elementary features. They did not have interactive maps in their functionality; the apps duplicated the weather forecast from national weather agencies. At that time, weather maps were just static images. Over time, interactive maps began to appear. This was a significant step in the evolution of weather mobile apps, transforming the category and setting a new standard. Gradually, indie developers, and then weather-related TV channels in the US began to create their weather apps using interactive maps. These maps allowed the user to navigate, zoom in and out, and see various indicators. This was a significant step in the evolution of weather mobile apps, transforming the category and setting a new standard. On the App Store, software developers started experimenting with new devices and weather maps. That's how Dark Sky appeared (and was among the leaders in this category in the App Store until Apple acquired it). The second important point in the evolution of mobile weather apps is the use of radar data with interactive maps. The second important point in the evolution of mobile weather apps is the use of radar data with interactive maps. The third important point in the development of weather apps is an opportunity to use the location of the device and adjust the forecast exactly to the user's request. That is, to receive alerts about a certain intensity of precipitation, high temperature, wind speed, etc. Such data personalization in weather apps and selecting information important to the user has made them more convenient for each target audience. How weather apps changed the game during hurricane seasons? Mobile weather apps have evolved a lot, raising awareness and allowing people to plan their time with the weather in mind. Apps, as a whole, have changed our world and our perception. This also applies to tracking the movement of hurricanes because now we have the weather forecast in our pocket. Of course, this includes the significant role of TV in weather forecasts and a well-established system of notifications from weather agencies. However, in contrast, mobile weather apps make information consumption more personalized and accessible. You can now track the weather in a few seconds and at a convenient time. Weather apps are the best trackers for natural phenomena like tornadoes (which occur very quickly and are less predictable). For example, citizens of Puerto Rico are the first to "welcome" tropical storms from the Atlantic Ocean due to their geographical location. For at least six months of the year, weather apps are the most viewed apps in this country because life can depend on them. A hurricane can do much more damage if no one knows about it. Ignorance, not the hurricane itself, is the biggest problem for coastal Americans. Ignorance, not the hurricane itself, is the biggest problem for coastal Americans. The evolution of weather apps Back in 2013, I was interested in developing a weather mobile app that offered users more than just a standard forecast from a weather station. Living in Florida, I know firsthand the importance of providing users with accurate long-term weather forecasts (up to 48 hours). It's crucial to warn users about tropical storms and hurricanes, even if these storms only slightly affect their area. For example, the Rain Viewer weather app my team and I created has detailed rainfall maps, and tracks storms in different regions. Of course, this is not a panacea for the user, but such data is a valuable tool during hurricane season. Last year over 700 weather apps appeared in the App Store, and three times as many in Google Play. The qualitative characteristics of forecasts include not only visualization of maps but also accurate data. For this, the app must have extensive radar coverage (today, most mobile weather apps use radar data). The bad news is that climate change makes yearly tropical storms and hurricanes more destructive. The good news is that modern mobile apps are rapidly evolving. Now they allow you to predict the effects of hurricanes, such as strong winds, intense precipitation, and extreme heat. Weather apps designed for weather enthusiasts, outdoor workers, commuters, vacation planners, and those who like to keep everything under control, including the weather, offer the following hurricane tracking tools: Hurricane path tracking considering the change in its strength (categories), Description of the hurricane’s characteristics at its location, Detailed hurricane timeline, Alerts about the formation of a hurricane and its approach to the current location, Current precipitation on the radar map and its forecast for the next 24-48 hours, Wind speed forecast map or hourly wind speed readings to track hurricane force winds. Hurricane path tracking considering the change in its strength (categories), Description of the hurricane’s characteristics at its location, Detailed hurricane timeline, Alerts about the formation of a hurricane and its approach to the current location, Current precipitation on the radar map and its forecast for the next 24-48 hours, Wind speed forecast map or hourly wind speed readings to track hurricane force winds. Add to that the ability to: Track temperature on the map, View weather forecast on home screen widgets without launching the app, Get customizable push notifications about upcoming precipitation (you can set up the intensity of rainfall to be notified about), Visualize precipitation and cloud cover in real time using radar and satellite maps. Track temperature on the map, View weather forecast on home screen widgets without launching the app, Get customizable push notifications about upcoming precipitation (you can set up the intensity of rainfall to be notified about), Visualize precipitation and cloud cover in real time using radar and satellite maps. While weather apps still use core data from NOAA (National Oceanic and Atmospheric Administration) and NHC (National Hurricane Center), they have completely personalized weather alerts and made tropical storm tracking as quick and easy as possible. Radar data and map visualization have also brought the use of these apps to a new and more interactive level. Many users rely on weather apps for evacuation alerts during approaching hurricanes, even though this information is primarily provided by weather agencies.

How Mobile Weather Apps Help You Survive the Hurricane Season

Enhancing Data Quality: Conclusion and Future Work, and References

Authors:
(1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu);
(2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work;
(3) Yixu Chen, Amazon Prime Video;
(4) Yongjun Wu, Amazon Prime Video;
(5) Hai Wei,  Amazon Prime Video;
(6)Sriram Sethuraman, Amazon Prime Video;
(7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Table of Links Abstract and Introduction
Related Work
Details of Subjective Study
Subjective Analysis
Objective Assessment
Conclusion, Acknowledgment and References VI. CONCLUSION We presented the first ever study on comparing HDR and SDR videos of the same content encoded at different bitrates and resolutions on different display devices. Our study shows that despite HDR’s theoretical capabilities over SDR, its perceptual quality depends heavily on the display device used in practice. We also evaluated several NR and FR VQA algorithms on the new database, and presented a novel NR VQA algorithm called HDRPatchMAX that exceeds the current state-of-the-art on this database. We hope that this spurs research on the modelling of display devices in VQA algorithms as well as for optimal bitrate ladders for streaming. ACKNOWLEDGMENT This research was sponsored by a grant from Amazon.com, Inc., and by grant number 2019844 for the National Science Foundation AI Institute for Foundations of Machine Learning (IFML). The authors also thank the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported in this paper. URL: http://www.tacc.utexas.edu. REFERENCES [1] ITU, “BT.709 : Parameter values for the HDTV standards for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2011. [2] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Study of the subjective and objective quality of high motion live streaming videos,” IEEE Trans. Image Process., vol. 31, pp. 1027– 1041, 2022. [3] D. Y. Lee, S. Paul, C. G. Bampis, H. Ko, J. Kim, S. Y. Jeong, B. Homan, and A. C. Bovik, “A subjective and objective study of space-time subsampled video quality,” arXiv preprint arXiv:2102.00088, 2021. [4] P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “Subjective and objective quality assessment of high frame rate videos,” IEEE Access, vol. 9, pp. 108 069–108 082, 2021. [5] R. R. R. Rao, S. Goring, W. Robitza, B. Feiten, and A. Raake, “Avt- ¨ vqdb-uhd-1: A large scale video quality database for uhd-1,” in 2019 IEEE Intl. Symposium on Multimedia (ISM). IEEE, 2019, pp. 17–177. [6] J. P. Ebenezer, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective quality assessment of high-motion sports videos at lowbitrates,” in 2022 IEEE Intl. Conf. on Image Process. (ICIP), 2022, pp. 521–525. [7] V. Hosu, F. Hahn, M. Jenadeleh, H. Lin, H. Men, T. Sziranyi, S. Li, and ´ D. Saupe, “The Konstanz natural video database (Konvid-1k),” in Int. Conf. Quality of Multimedia Experience, 2017, pp. 1–6. [8] Y. Wang, S. Inguva, and B. Adsumilli, “Youtube UGC dataset for video compression research,” in IEEE Int. Workshop Multimed. Signal Process. IEEE, 2019, pp. 1–5. [9] Z. Ying, M. Mandal, D. Ghadiyaram, and A. Bovik, “Patch-VQ: ‘patching up’ the video quality problem,” in IEEE Conf. Comp. Vision Pattern Recognit., 2021, pp. 14 014–14 024. [10] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in IEEE Intl. Conf. Image Process., 2022, pp. 786–790. [11] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “A study of subjective and objective quality assessment of HDR videos,” submitted to IEEE Trans. Image Process., 2022. [12] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in Proceedings of the IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV) Workshops, January 2023, pp. 556–564. [13] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. [14] Netflix, VMAF: The Journey Continues, 2018 (accessed December 28, 2021). [Online]. Available: https://netflixtechblog. com/vmaf-the-journey-continues-44b51ee9ed12 [15] C. G. Bampis, P. Gupta, R. Soundararajan, and A. C. Bovik, “Speedqa: Spatial efficient entropic differencing for image and video quality,” IEEE Signal Process. Letters, vol. 24, no. 9, pp. 1333–1337, 2017. [16] R. Soundararajan and A. C. Bovik, “Video quality assessment by reduced reference spatio-temporal entropic differencing,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, pp. 684–694, 2012. [17] P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “St-greed: Space-time generalized entropic differences for frame rate dependent video quality prediction,” IEEE Trans. Image Process., vol. 30, pp. 7446–7457, 2021. [18] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695–4708, 2012. [19] M. A. Saad, A. C. Bovik, and C. Charrier, “Blind prediction of natural video quality,” IEEE Trans. Image Process., vol. 23, no. 3, pp. 1352– 1365, 2014. [20] Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “UGCVQA: Benchmarking blind video quality assessment for user generated content,” IEEE Trans. Image Process., vol. 30, pp. 4449–4464, 2021. [21] Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “RAPIQUE: Rapid and accurate video quality prediction of user generated content,” arXiv preprint arXiv:2101.10955, 2021. [22] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “ChipQA: No-reference video quality prediction via space-time chips,” IEEE Trans. Image Process., vol. 30, pp. 8059–8074, 2021. [23] ——, “HDR-ChipQA: No-reference quality assessment for high dynamic range videos,” submitted to IEEE Trans. Image Process., 2023. [24] A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209–212, 2012. [25] J. Korhonen, “Two-level approach for no-reference consumer video quality assessment,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 5923–5938, 2019. [26] ITU, “ BT.2020 : Parameter values for ultra-high definition television systems for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2015. [27] S. Miller, M. Nezamabadi, and S. Daly, “Perceptual signal coding for more efficient usage of bit codes,” SMPTE Motion Imaging J., vol. 122, no. 4, pp. 52–59, 2013. [28] Rtings, Peak Brightness Measurement, 2023 (accessed February 28, 2023). [Online]. Available: https://www.rtings.com/tv/tests/ picture-quality/sdr-peak-brightness [29] ITU, “BT.500 : Methodologies for the subjective assessment of the quality of television images,” Intl. Telecomm. Union, Tech. Rep., 2019. [30] ——, “ITU. 910 : Subjective video quality assessment methods for multimedia applications,” Intl. Telecomm. Union, Tech. Rep., 2008. [31] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in Asilomar Conf. Signals, Syst., Comput., vol. 2, 2003, pp. 1398–1402 Vol.2. [32] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, 2006. [33] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” submitted to IEEE Signal Process. Letters, 2023. [34] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, and A. C. Bovik, “Noreference video quality assessment using space-time chips,” in IEEE Intl. Workshop Multimedia Signal Process., 2020, pp. 1–6. [35] D. W. Dong and J. J. Atick, “Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus,” Netw.: Comput. Neural Syst., vol. 6, no. 2, pp. 159–178, 1995. This paper is available on arxiv under CC 4.0 license. Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work; (3) Yixu Chen, Amazon Prime Video; (4) Yongjun Wu, Amazon Prime Video; (5) Hai Wei,  Amazon Prime Video; (6)Sriram Sethuraman, Amazon Prime Video; (7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Authors: Authors: (1) Joshua P. Ebenezer, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work (e-mail: joshuaebenezer@utexas.edu); (2) Zaixi Shang, Student Member, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA, contributed equally to this work; (3) Yixu Chen, Amazon Prime Video; (4) Yongjun Wu, Amazon Prime Video; (5) Hai Wei,  Amazon Prime Video; (6)Sriram Sethuraman, Amazon Prime Video; (7) Alan C. Bovik, Fellow, IEEE, Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. Table of Links Abstract and Introduction Related Work Details of Subjective Study Subjective Analysis Objective Assessment Conclusion, Acknowledgment and References Abstract and Introduction Abstract and Introduction Related Work Related Work Details of Subjective Study Details of Subjective Study Subjective Analysis Subjective Analysis Objective Assessment Objective Assessment Conclusion, Acknowledgment and References Conclusion, Acknowledgment and References VI. CONCLUSION We presented the first ever study on comparing HDR and SDR videos of the same content encoded at different bitrates and resolutions on different display devices. Our study shows that despite HDR’s theoretical capabilities over SDR, its perceptual quality depends heavily on the display device used in practice. We also evaluated several NR and FR VQA algorithms on the new database, and presented a novel NR VQA algorithm called HDRPatchMAX that exceeds the current state-of-the-art on this database. We hope that this spurs research on the modelling of display devices in VQA algorithms as well as for optimal bitrate ladders for streaming. ACKNOWLEDGMENT This research was sponsored by a grant from Amazon.com, Inc., and by grant number 2019844 for the National Science Foundation AI Institute for Foundations of Machine Learning (IFML). The authors also thank the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported in this paper. URL: http://www.tacc.utexas.edu. REFERENCES [1] ITU, “BT.709 : Parameter values for the HDTV standards for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2011. [2] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Study of the subjective and objective quality of high motion live streaming videos,” IEEE Trans. Image Process., vol. 31, pp. 1027– 1041, 2022. [3] D. Y. Lee, S. Paul, C. G. Bampis, H. Ko, J. Kim, S. Y. Jeong, B. Homan, and A. C. Bovik, “A subjective and objective study of space-time subsampled video quality,” arXiv preprint arXiv:2102.00088, 2021. [4] P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “Subjective and objective quality assessment of high frame rate videos,” IEEE Access, vol. 9, pp. 108 069–108 082, 2021. [5] R. R. R. Rao, S. Goring, W. Robitza, B. Feiten, and A. Raake, “Avt- ¨ vqdb-uhd-1: A large scale video quality database for uhd-1,” in 2019 IEEE Intl. Symposium on Multimedia (ISM). IEEE, 2019, pp. 17–177. [6] J. P. Ebenezer, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective quality assessment of high-motion sports videos at lowbitrates,” in 2022 IEEE Intl. Conf. on Image Process. (ICIP), 2022, pp. 521–525. [7] V. Hosu, F. Hahn, M. Jenadeleh, H. Lin, H. Men, T. Sziranyi, S. Li, and ´ D. Saupe, “The Konstanz natural video database (Konvid-1k),” in Int. Conf. Quality of Multimedia Experience, 2017, pp. 1–6. [8] Y. Wang, S. Inguva, and B. Adsumilli, “Youtube UGC dataset for video compression research,” in IEEE Int. Workshop Multimed. Signal Process. IEEE, 2019, pp. 1–5. [9] Z. Ying, M. Mandal, D. Ghadiyaram, and A. Bovik, “Patch-VQ: ‘patching up’ the video quality problem,” in IEEE Conf. Comp. Vision Pattern Recognit., 2021, pp. 14 014–14 024. [10] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in IEEE Intl. Conf. Image Process., 2022, pp. 786–790. [11] Z. Shang, J. P. Ebenezer, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “A study of subjective and objective quality assessment of HDR videos,” submitted to IEEE Trans. Image Process., 2022. [12] Z. Shang, Y. Chen, Y. Wu, H. Wei, and S. Sethuraman, “Subjective and objective video quality assessment of high dynamic range sports content,” in Proceedings of the IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV) Workshops, January 2023, pp. 556–564. [13] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. [14] Netflix, VMAF: The Journey Continues, 2018 (accessed December 28, 2021). [Online]. Available: https://netflixtechblog. com/vmaf-the-journey-continues-44b51ee9ed12 [15] C. G. Bampis, P. Gupta, R. Soundararajan, and A. C. Bovik, “Speedqa: Spatial efficient entropic differencing for image and video quality,” IEEE Signal Process. Letters, vol. 24, no. 9, pp. 1333–1337, 2017. [16] R. Soundararajan and A. C. Bovik, “Video quality assessment by reduced reference spatio-temporal entropic differencing,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 4, pp. 684–694, 2012. [17] P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli, and A. C. Bovik, “St-greed: Space-time generalized entropic differences for frame rate dependent video quality prediction,” IEEE Trans. Image Process., vol. 30, pp. 7446–7457, 2021. [18] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695–4708, 2012. [19] M. A. Saad, A. C. Bovik, and C. Charrier, “Blind prediction of natural video quality,” IEEE Trans. Image Process., vol. 23, no. 3, pp. 1352– 1365, 2014. [20] Z. Tu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “UGCVQA: Benchmarking blind video quality assessment for user generated content,” IEEE Trans. Image Process., vol. 30, pp. 4449–4464, 2021. [21] Z. Tu, X. Yu, Y. Wang, N. Birkbeck, B. Adsumilli, and A. C. Bovik, “RAPIQUE: Rapid and accurate video quality prediction of user generated content,” arXiv preprint arXiv:2101.10955, 2021. [22] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “ChipQA: No-reference video quality prediction via space-time chips,” IEEE Trans. Image Process., vol. 30, pp. 8059–8074, 2021. [23] ——, “HDR-ChipQA: No-reference quality assessment for high dynamic range videos,” submitted to IEEE Trans. Image Process., 2023. [24] A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “completely blind” image quality analyzer,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 209–212, 2012. [25] J. Korhonen, “Two-level approach for no-reference consumer video quality assessment,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 5923–5938, 2019. [26] ITU, “ BT.2020 : Parameter values for ultra-high definition television systems for production and international programme exchange,” Intl. Telecomm. Union, Tech. Rep., 2015. [27] S. Miller, M. Nezamabadi, and S. Daly, “Perceptual signal coding for more efficient usage of bit codes,” SMPTE Motion Imaging J., vol. 122, no. 4, pp. 52–59, 2013. [28] Rtings, Peak Brightness Measurement, 2023 (accessed February 28, 2023). [Online]. Available: https://www.rtings.com/tv/tests/ picture-quality/sdr-peak-brightness [29] ITU, “BT.500 : Methodologies for the subjective assessment of the quality of television images,” Intl. Telecomm. Union, Tech. Rep., 2019. [30] ——, “ITU. 910 : Subjective video quality assessment methods for multimedia applications,” Intl. Telecomm. Union, Tech. Rep., 2008. [31] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in Asilomar Conf. Signals, Syst., Comput., vol. 2, 2003, pp. 1398–1402 Vol.2. [32] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3440–3451, 2006. [33] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, S. Sethuraman, and A. C. Bovik, “Making video quality assessment models robust to bit depth,” submitted to IEEE Signal Process. Letters, 2023. [34] J. P. Ebenezer, Z. Shang, Y. Wu, H. Wei, and A. C. Bovik, “Noreference video quality assessment using space-time chips,” in IEEE Intl. Workshop Multimedia Signal Process., 2020, pp. 1–6. [35] D. W. Dong and J. J. Atick, “Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus,” Netw.: Comput. Neural Syst., vol. 6, no. 2, pp. 159–178, 1995. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Stories, News & Mentions about NOALA

Noala’s Stories on HackerNoon

Noala's News & Mentions