In 1973, President Nixon signed into law the Emergency Daylight Saving Time Energy Conservation Act, which made DST permanent in the U.S. This helped reduce confusion throughout the country with some regions of the U.S. participating in the practice and some regions opting out.

Daylight Saving 2024: When does Daylight Savings start this year and clocks spring forward? - nj.com

Here's when to change the clocks in 2024 for daylight saving time. It's actually on a historic date this year.

When is daylight savings in 2024? Clocks change on a historic date this year - masslive.com

Also known as “spring forward,” the yearly time change means turning the clock one hour forward. The sun will rise and set later in Kansas City.

Get ready to spring forward and lose sleep, KC. Here’s when daylight saving time starts

Daylight saving time is happening soon in South Carolina. Here’s when you should spring forward.

Daylight savings is coming soon SC. Here’s when, but is Congress ending the time changes?

Kim Thatcher, R-Keizer, said Senate Bill 1548 would make Pacific Standard Time year-round, abandoning effort to make daylight saving time permanent.

Oregon may halt daylight saving time this year, without congressional approval

Texans should be prepared to lose sleep when the clock springs forward on daylight saving time. Here's when.

Daylight saving time is coming. Here's when clocks spring forward

A bill in the Oregon Legislature would end daylight saving time for good.

Could Oregon ditch daylight saving time forever? - oregonlive.com

DµST

Introduction To Training Pipelines The typical machine learning training process consists of various tasks such as data preparation, model training, model validation, model deployment and others. Because you may want to iterate on your model even after you deploy it to production, it is paramount to automate these tasks, e.g. to run them with new data, hyper-parameters, changes in the code, etc. Training pipelines help automate these tasks. At the same time, it is crucial to keep the pipeline reproducible, in the sense that you as well as others can replicate it and obtain similar results. In order to ensure that pipelines are reproducible, it is crucial to track infrastructure, code, data, hyper-parameters, experiment metrics, etc. In this article, I’ll give an overview on how WandB and dstack together can help build reproducible training pipelines. Experiment tracking First, we’ll start with experiment tracking. Experiment tracking involves tracking hyper-parameters (e.g., step-size, batch size, etc.) experiment metrics (e.g., accuracy, training loss, validation loss, etc.) hardware system metrics (e.g., the utilization of GPUs, memory, etc.) WandB provides one of the easiest ways to perform experiment tracking, as it is easy to integrate into the ML code (addition of only a few lines of code) uses cloud to store the results so you can always find these metrics by your run name (don’t underestimate the importance of this, as storing metrics on you local machine may be rather an anti-pattern in regard to reproducibility) automatically provides the visualization of various metrics with nice charts WandB is very easy to integrate into your existing codebase. Below is an example of how to do it if you use PyTorch Lightning. We need to import the object. Then, instantiate the object and pass a project name as its argument, as shown below. WandbLogger from pytorch_lightning.loggers import WandbLogger

wandb_logger = WandbLogger(project="my-test-project") The created can be passed into the logger argument in the object as shown below. wandb_logger Trainer trainer = Trainer(logger=wandb_logger) Once the run is completed, the information of the run is assigned to . my-test-project You may check and click your project for the results (loss, accuracy, system metrics etc) pertaining to various runs of the project, as shown below. wandb.ai In order to store hyper-parameters or other configuration parameters, is used and its integration into the ML code is easy as shown below. wandb.config import wandb
wandb.init()
wandb.config.epochs = 10
wandb.config.batch_size = 32 The experiment configuration for each run is automatically stored in the cloud, and it can be found in the file in the tab of a run at . config.yaml Files wandb.ai Automating workflows Now, let’s talk about the automation of tasks and tracking the rest of our pipeline, which may include data, code, and infrastructure. For this purpose, we use dstack. Here’s a brief list of what dstack can do: version data, code, and infrastructure automate training tasks through declarative configuration files automatically provision infrastructure in a linked cloud account (it supports AWS, GCP, and Azure), Most importantly, it can be used from your favorite IDE (or terminal) To automate workflows with dstack, one needs to define them in the file. ./dstack/workflows.yaml Here’s a very simple example: workflows:
  - name: prepare
    provider: python
    script: "prepare.py"
    requirements: "requirements.txt"
    artifacts: ["data"]

 - name: train
   provider: python
   version: "3.9"
   requirements: "requirements.txt"
   script: "train.py"
   depends-on:
      - prepare
   artifacts: ["model"]
   resources:
     gpu: 4 Here, we can define multiple workflows (i.e. tasks) and configure dependencies between them. In this particular example, we have two workflows: and . prepare train The workflow depends on the workflow. train prepare As you see, each workflow defines how to run the code, including what folders to store as output artifacts, and what infrastructure is needed (e.g. the number of GPUs, amount of memory, etc). dstack CLI is used to run the workflow from your local terminal. To run the above-mentioned workflow, execute the following command in your local terminal. dstack run train Once the run is submitted, the user can access relevant logs, changes in code, the artifacts etc in the dstack UI or CLI. In order to monitor the run, login to your account, and the contents in the dstack UI should look like below. dstack.ai Artifacts can be browsed through the user interface. Moreover, it is possible to download the contents of artifacts using the dstack CLI with the following command. dstack artifacts download <run-name> Versioning data In order to ensure reproducibility of the training pipelines, it is crucial to track data too. With dstack, data artifacts can be versioned by assigning tags, which can later be referenced in other workflows. In the example above, the workflow depended on the workflow. Each time you ran the workflow, dstack also ran the workflow, and then passed the output artifacts of the workflow to the workflow. train prepare train download prepare train Now let’s imagine that we’d like to run the workflow independently and then reuse the output artifacts of that particular run with the workflows. prepare train In order to do that, you have to run the workflow, and then assign a tag to it (e.g. through the UI as shown below or the CLI). prepare Then, you can refer to this tag from the workflow: train workflows:
  - name: prepare
    provider: python
    script: "prepare.py"
    requirements: "requirements.txt"
    artifacts: ["data"]

 - name: train
   provider: python
   version: "3.9"
   requirements: "requirements.txt"
   script: "train.py"
   depends-on:
      - prepare:latest
   artifacts: ["model"]
   resources:
     gpu: 4 By decoupling the workflows of preparing data and training the model, it becomes easier to train the model iteratively and keep every run reproducible. dstack + WandB Configuration It is possible to seamlessly use dstack and WandB together. To do that, obtain the WandB API key from “Settings” in Wandb as shown below. And add it to dstack Secrets. You can do it  via  “Settings” in . Click the “Add secret” button and add the key secret with the copied  WandB API key value. dstack.ai WANDB_API_KEY The dstack settings should look like below. In order to use the same  run names across dstack and WandB, you can use the environment variable to get the dstack run name and pass it as a display name to as shown below: RUN_NAME WandbLogger run_name = os.environ['RUN_NAME']

wandb log results to a project

wandb_logger = WandbLogger(name=run_name, project="my-test-project") That’s it for now. The source code used in this blog post can be found . here You’re very welcome to share your feedback, ask questions, and of course give a spin to WandB and dstack yourself. I want to try it. Where do I start? dstack Quickstart WandB Quickstart

Reproducible ML Training Pipelines With dstack And WandB

Predicting Links in Graphs with Graph Neural Networks and DGL.ai

አገናኞችን በግራፍ መተንበይ ከግራፍ ነርቭ ኔትወርኮች እና DGL.ai ጋር

Predikce odkazů v grafech pomocí Graph Neuron Networks a DGL.ai

Forudsigelse af links i grafer med Graph Neural Networks og DGL.ai

Predicción de vínculos en gráficos con redes neuronales gráficas y DGL.ai

Grafikoetan loturak aurreikustea Graph Neural Networks eta DGL.ai

グラフニューラルネットワークと DGL.ai によるグラフ内のリンクの予測

ទស្សន៍ទាយតំណភ្ជាប់ក្នុងក្រាហ្វជាមួយបណ្តាញសរសៃប្រសាទក្រាហ្វ និង DGL.ai

Графиктик нейрон тармактары жана DGL.ai менен графиктердеги шилтемелерди болжолдоо

Saišu prognozēšana grafikos, izmantojot grafiku neironu tīklus un DGL.ai

Maminavina rohy amin'ny sary miaraka amin'ny Graph Neural Networks sy DGL.ai

Пешгӯии пайвандҳо дар графикҳо бо шабакаҳои графикии нейронӣ ва DGL.ai

การคาดการณ์ลิงก์ในกราฟด้วย Graph Neural Networks และ DGL.ai

LOS ANGELES, United States, October 30th, 2024/Chainwire/--Xsolla,a global video game commerce company, announces plans to launch Xsolla ZK and introduce a digital backpack of virtual items on the blockchain. Xsolla ZK is powered by ZKsync technology and will drive the continuous growth and expansion of Web3 technologies to further develop solutions on the blockchain for the video game industry. Xsolla ZK will become part of the Elastic Chain ecosystem- an expanding constellation of interconnected chains powered by Zksync, an Ethereum Layer 2 zero-knowledge roll-up technology. Xsolla ZK will also introduce its ‘digital backpack’ for game developers, item creators, and gaming infrastructure providers to store and manage in-game items. Xsolla has seen success in the gaming industry, with two decades of experience, over 2,500 games monetized with its products, and over 1,000 developers and publishers utilizing its technology for their games. Lee Jacobson, Senior Vice President of Business Development Web3 at Xsolla, expressed his enthusiasm for the project: "Xsolla ZK leverages Ethereum Layer 2 zk Rollup technology to create a digital backpack for game developers. By deploying our expertise on the ZKsync Elastic Chain, we provide game developers with a scalable and pioneering solution aligning with the economic models with which they are familiar. Xsolla ZK is not just about innovation; it's about creating real value for the gaming community."Xsolla will combine its expertise in in-game commerce with ZKsync’s cutting-edge blockchain technology. Rich Kim, Head of Gaming at Matter Labs, said, "We are thrilled to see companies like Xsolla launch pioneering projects for Web3 gaming. For gaming ecosystems to thrive, it’s critical to close the gap for both users and builders; builders need proven, plug-and-play infrastructure to launch rich features while handling the performance required by mass usage games. Additionally, users need easy-to-use and convenient in-game features and payment options that can be leveraged across a broad ecosystem of games. Xsolla ZK is carving a path for gaming to transition and thrive in Web3." About Xsolla Xsolla is a global video game commerce company with a robust and powerful set of tools and services designed specifically for the industry. Since its founding in 2005, Xsolla has helped thousands of game developers and publishers of all sizes fund, market, launch, and monetize their games globally and across multiple platforms. As an innovative leader in game commerce, Xsolla’s mission is to solve the inherent complexities of global distribution, marketing, and monetization to help their partners reach more geographies, generate more revenue, and create relationships with gamers worldwide. Headquartered and incorporated in Los Angeles, California, with offices in Montreal, London, Berlin, Beijing, Guangzhou, Seoul, Tokyo, Kuala Lumpur, Raleigh, and other cities around the world, Xsolla supports major gaming titles like Valve, Take-Two, KRAFTON, Nexters, NetEase, Playstudios, Playrix, miHoYo, and more. About the Elastic Chain The Elastic Chain is an ever-expanding cluster of ZK rollups, secured by cryptography and designed for native interoperability with a unified, seamless user experience. The Elastic Chain delivers the functionality of a multi-chain ecosystem with the simplicity of a single blockchain, enabling scalable, secure, and efficient transactions. These core components ensure that this cluster of ZK Chains can interact and transact with each other efficiently, inheriting the security of Ethereum and forming a network that can scale horizontally without compromising on the core properties that make blockchains so powerful. For more information about Xsolla ZK and how to get early access, users can visit: xsolla.pro/zk For additional information and to learn more, users can visit xsolla.com. For additional information and to learn more, users can visit matter-labs.io. Contact Global Director of Public Relations Derrick Stembridge Xsolla d.stembridge@xsolla.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here LOS ANGELES, United States, October 30th, 2024/Chainwire/--Xsolla,a global video game commerce company, announces plans to launch Xsolla ZK and introduce a digital backpack of virtual items on the blockchain. Xsolla ZK is powered by ZKsync technology and will drive the continuous growth and expansion of Web3 technologies to further develop solutions on the blockchain for the video game industry. Xsolla ZK will become part of the Elastic Chain ecosystem- an expanding constellation of interconnected chains powered by Zksync, an Ethereum Layer 2 zero-knowledge roll-up technology. Elastic Chain Elastic Chain Xsolla ZK will also introduce its ‘digital backpack’ for game developers, item creators, and gaming infrastructure providers to store and manage in-game items. Xsolla has seen success in the gaming industry, with two decades of experience, over 2,500 games monetized with its products, and over 1,000 developers and publishers utilizing its technology for their games. Lee Jacobson, Senior Vice President of Business Development Web3 at Xsolla, expressed his enthusiasm for the project: "Xsolla ZK leverages Ethereum Layer 2 zk Rollup technology to create a digital backpack for game developers. By deploying our expertise on the ZKsync Elastic Chain, we provide game developers with a scalable and pioneering solution aligning with the economic models with which they are familiar. Xsolla ZK is not just about innovation; it's about creating real value for the gaming community."Xsolla will combine its expertise in in-game commerce with ZKsync’s cutting-edge blockchain technology. Rich Kim, Head of Gaming at Matter Labs, said, "We are thrilled to see companies like Xsolla launch pioneering projects for Web3 gaming. For gaming ecosystems to thrive, it’s critical to close the gap for both users and builders; builders need proven, plug-and-play infrastructure to launch rich features while handling the performance required by mass usage games. Additionally, users need easy-to-use and convenient in-game features and payment options that can be leveraged across a broad ecosystem of games. Xsolla ZK is carving a path for gaming to transition and thrive in Web3." Rich Kim, Head of Gaming at Matter Labs, said, "We are thrilled to see companies like Xsolla launch pioneering projects for Web3 gaming. For gaming ecosystems to thrive, it’s critical to close the gap for both users and builders; builders need proven, plug-and-play infrastructure to launch rich features while handling the performance required by mass usage games. Additionally, users need easy-to-use and convenient in-game features and payment options that can be leveraged across a broad ecosystem of games. Xsolla ZK is carving a path for gaming to transition and thrive in Web3." About Xsolla Xsolla is a global video game commerce company with a robust and powerful set of tools and services designed specifically for the industry. Xsolla Xsolla Since its founding in 2005, Xsolla has helped thousands of game developers and publishers of all sizes fund, market, launch, and monetize their games globally and across multiple platforms. As an innovative leader in game commerce, Xsolla’s mission is to solve the inherent complexities of global distribution, marketing, and monetization to help their partners reach more geographies, generate more revenue, and create relationships with gamers worldwide. Headquartered and incorporated in Los Angeles, California, with offices in Montreal, London, Berlin, Beijing, Guangzhou, Seoul, Tokyo, Kuala Lumpur, Raleigh, and other cities around the world, Xsolla supports major gaming titles like Valve, Take-Two, KRAFTON, Nexters, NetEase, Playstudios, Playrix, miHoYo, and more. About the Elastic Chain The Elastic Chain is an ever-expanding cluster of ZK rollups, secured by cryptography and designed for native interoperability with a unified, seamless user experience. Elastic Chain Elastic Chain The Elastic Chain delivers the functionality of a multi-chain ecosystem with the simplicity of a single blockchain, enabling scalable, secure, and efficient transactions. These core components ensure that this cluster of ZK Chains can interact and transact with each other efficiently, inheriting the security of Ethereum and forming a network that can scale horizontally without compromising on the core properties that make blockchains so powerful. For more information about Xsolla ZK and how to get early access, users can visit: xsolla.pro/zk xsolla.pro/zk xsolla.pro/zk For additional information and to learn more, users can visit xsolla.com . xsolla.com xsolla.com For additional information and to learn more, users can visit matter-labs.io . matter-labs.io matter-labs.io Contact Global Director of Public Relations Derrick Stembridge Xsolla d.stembridge@xsolla.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here

Xsolla To Launch Xsolla ZK, Advancing Web3 Adoption For Video Games

Xsolla, Xsolla ZK-ni işə salır, Video Oyunlar üçün Web3 qəbulunu inkişaf etdirir

Xsolla ke spuštění Xsolla ZK, postupující přijetí Web3 pro videohry

Xsolla lanzará Xsolla ZK, impulsando la adopción de Web3 para videojuegos

Xsolla lanzará Xsolla ZK, avanzando na adopción de Web3 para videoxogos

Xsolla が Xsolla ZK をリリース、ビデオゲームへの Web3 導入を推進

Xsolla Ko Lancer Xsolla ZK, Ko Avancé Adoption Web3 Pona Ba Jeux Vidéo

Xsolla lanceert Xsolla ZK, wat de acceptatie van Web3 voor videogames bevordert

Xsolla lançará Xsolla ZK, promovendo a adoção do Web3 para videogames

Xsolla va lansa Xsolla ZK, promovând adoptarea Web3 pentru jocurile video

Xsolla Kuzindua Xsolla ZK, Kuendeleza Uasili wa Web3 kwa Michezo ya Video

Xsolla запускає Xsolla ZK, просуваючи впровадження Web3 для відеоігор

I-Xsolla iyakuQalisa i-Xsolla ZK, iPhuhlisa iWeb3 yoMthombo kwiMidlalo yeVidiyo

How to Simplify Data Flow By Multi-Table Synchronization With Apache SeaTunnel

Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion
Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. References [AGL05] AHRENS J., GEVECI B., LAW C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005). 2 [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S., [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S.,DASARI H. P., RAITSOS D. E., GITTINGS J. A., JAMIL T., SRINIVASAN M., HOTEIT I.: RedSeaAtlas: A visual analytics tool for spatiotemporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25–32. 1 [AHG∗19] AFZAL S., HITTAWE M. M., GHANI S., JAMIL T., KNIO O., HADWIGER M., HOTEIT I.: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881–907. 1, 4 [AMM17] AMORES A., MELNICHENKO O., MAXIMENKO N.: Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum. Journal of Geophysical Research: Oceans 122, 1 (2017), 23–41. 5 [BNBD∗07] BENITEZ-NELSON C. R., BIDIGARE R. R., DICKEY T. D., LANDRY M. R., LEONARD C. L., BROWN S. L., NENCIOLI F., RII Y. M., MAITI K., BECKER J. W., ET AL.: Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017–1021. 1 [DAN12] DINESHA V., ADABALA N., NATARAJAN V.: Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265–278. 1 [FD06] FRASER S., DICKSON B.: Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5–7. 1 [Fer23] Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [FFH21] FRIEDERICI A., FALK M., HOTZ I.: A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop [GEP04] GUO D., EVANGELINOS C., PATRIKALAKIS N.: Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162–173. doi:10. 1109/CGI.2004.1309207. 5 [GSK∗08] GROCHOW K., STOERMER M., KELLEY D., DELANEY J., LAZOWSKA E.: COVE: A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092. 1 [KNR∗07] KUMAR S. P., NUNCIO M., RAMAIAH N., SARDESAI S., NARVEKAR J., FERNANDES V., PAUL J. T.: Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619–1640. 4 [LJP∗19] LI S., JAROSZYNSKI S., PEARSE S., ORF L., CLYNE J.: Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488. 1 [Mad08] MADEC G.: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008. 2 [MAIS16] MATSUOKA D., ARAKI F., INOUE Y., SASAKI H.: A new approach to ocean eddy detection, tracking, and event visualization– application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601–1611. 5 [McW90] MCWILLIAMS J. C.: The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361–385. 4 [McW08] MCWILLIAMS J. C.: The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5–15. 1 [MJD∗99] MCNEIL J., JANNASCH H., DICKEY T., MCGILLICUDDY D., BRZEZINSKI M., SAKAMOTO C.: New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537–15548. 1 [myO23] Copernicus myOcean. https://marine.copernicus. eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]. 1 [NL15] NOBRE C., LEX A.: OceanPaths: Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. doi:10.2312/eurovisshort.20151124. 1 [Oku70] OKUBO A.: Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445–454. 4 [PBI04] PARK S., BAJAJ C., IHM I.: Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419–426. 1 [pyF23] pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [Ros89] ROSENBLUM L. J.: Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14–19. 1 [RR10] ROBINSON I. S., ROBINSON I. S.: Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69–114. 1 [Sar13] SARMIENTO J. L.: Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013. 4 [SDVN22] SINGH U., DHIPU T. M., VINAYACHANDRAN P. N., NATARAJAN V.: Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. doi:https://doi.org/10.1016/j.cageo. 2021.104993. 5 [TFL∗17] TIERNY J., FAVELIER G., LEVINE J. A., GUEUNET C., MICHAUX M.: The Topology Toolkit. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 832–842. 5 [TZG∗17] TOYE H., ZHAN P., GOPALAKRISHNAN G., KARTADIKARIA A. R., HUANG H., KNIO O., HOTEIT I.: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 7 (Jul 2017), 915–933. doi:10.1007/ s10236-017-1064-1. 2 [VCMN04] VINAYACHANDRAN P. N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 This paper is available on arxiv under CC 4.0 license. Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Authors: Authors: (1) Toshit Jain, Indian Institute of Science Bangalore, India; (2) Varun Singh, Indian Institute of Science Bangalore, India; (3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India; (4) Upkar Singh, Indian Institute of Science Bangalore, India; (5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden; (6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India; (7) Vijay Natarajan, Indian Institute of Science Bangalore, India. Table of Links Abstract and Intro Ocean data pyParaOcean: Architecture pyParaOcean: Functionalities Case study: Bay of Bengal Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Ocean data Ocean data pyParaOcean: Architecture pyParaOcean: Architecture pyParaOcean: Functionalities pyParaOcean: Functionalities Case study: Bay of Bengal Case study: Bay of Bengal Conclusion Conclusion Acknowledgments and References Acknowledgments and References Acknowledgments This research was funded by a grant from SERB, Govt. of India (CRG/2021/005278), partial support from National Supercomputing Mission, DST, the J. C. Bose Fellowship awarded by the SERB, DST, Govt. of India, the Dr. Ram Kumar IISc Distinguished Visiting Chair Professorship in EECS, and a scholarship from MoE, Govt. of India. Part of this work was carried out towards partial fulfilment of a thesis requirement at BITS Pilani. References [AGL05] AHRENS J., GEVECI B., LAW C.: Paraview: An end-user tool for large data visualization. The visualization handbook 717 (2005). 2 [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S., [AGT∗19] AFZAL S., GHANI S., TISSINGTON G., LANGODAN S.,DASARI H. P., RAITSOS D. E., GITTINGS J. A., JAMIL T., SRINIVASAN M., HOTEIT I.: RedSeaAtlas: A visual analytics tool for spatiotemporal multivariate data of the red sea. In EnvirVis: Workshop on Visualization in Environmental Sciences (EnvirVis2019) (2019), pp. 25–32. 1 [AHG∗19] AFZAL S., HITTAWE M. M., GHANI S., JAMIL T., KNIO O., HADWIGER M., HOTEIT I.: The state of the art in visual analysis approaches for ocean and atmospheric datasets. Computer Graphics Forum 38, 3 (2019), 881–907. 1, 4 [AMM17] AMORES A., MELNICHENKO O., MAXIMENKO N.: Coherent mesoscale eddies in the North Atlantic subtropical gyre: 3-D structure and transport with application to the salinity maximum. Journal of Geophysical Research: Oceans 122, 1 (2017), 23–41. 5 [BNBD∗07] BENITEZ-NELSON C. R., BIDIGARE R. R., DICKEY T. D., LANDRY M. R., LEONARD C. L., BROWN S. L., NENCIOLI F., RII Y. M., MAITI K., BECKER J. W., ET AL.: Mesoscale eddies drive increased silica export in the subtropical pacific ocean. Science 316, 5827 (2007), 1017–1021. 1 [DAN12] DINESHA V., ADABALA N., NATARAJAN V.: Uncertainty visualization using HDR volume rendering. The Visual Computer 28 (2012), 265–278. 1 [FD06] FRASER S., DICKSON B.: Data mining geoscientific data sets using self organizing maps. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts (2006), 5–7. 1 [Fer23] Ferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [FFH21] FRIEDERICI A., FALK M., HOTZ I.: A winding angle framework for tracking and exploring eddy transport in oceanic ensemble simulations. In EnvirVis: Workshop [GEP04] GUO D., EVANGELINOS C., PATRIKALAKIS N.: Flow feature extraction in oceanographic visualization. In Proceedings of Computer Graphics International Conference (07 2004), pp. 162–173. doi:10. 1109/CGI.2004.1309207. 5 [GSK∗08] GROCHOW K., STOERMER M., KELLEY D., DELANEY J., LAZOWSKA E.: COVE: A visual environment for ocean observatory design. Journal of Physics: Conference Series 125, 1 (2008), 012092. 1 [KNR∗07] KUMAR S. P., NUNCIO M., RAMAIAH N., SARDESAI S., NARVEKAR J., FERNANDES V., PAUL J. T.: Eddy-mediated biological productivity in the Bay of Bengal during fall and spring intermonsoons. Deep Sea Research Part I: Oceanographic Research Papers 54, 9 (2007), 1619–1640. 4 [LJP∗19] LI S., JAROSZYNSKI S., PEARSE S., ORF L., CLYNE J.: Vapor: A visualization package tailored to analyze simulation data in earth system science. Atmosphere 10, 9 (2019), 488. 1 [Mad08] MADEC G.: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No 27, ISSN No 1288-1619, 2008. 2 [MAIS16] MATSUOKA D., ARAKI F., INOUE Y., SASAKI H.: A new approach to ocean eddy detection, tracking, and event visualization– application to the northwest pacific ocean. Procedia Computer Science 80 (2016), 1601–1611. 5 [McW90] MCWILLIAMS J. C.: The vortices of two-dimensional turbulence. Journal of Fluid mechanics 219 (1990), 361–385. 4 [McW08] MCWILLIAMS J. C.: The nature and consequences of oceanic eddies. Ocean modeling in an eddying regime 177 (2008), 5–15. 1 [MJD∗99] MCNEIL J., JANNASCH H., DICKEY T., MCGILLICUDDY D., BRZEZINSKI M., SAKAMOTO C.: New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off bermuda. Journal of Geophysical Research: Oceans 104, C7 (1999), 15537–15548. 1 [myO23] Copernicus myOcean. https://marine.copernicus. eu/access-data/ocean-visualisation-tools, 2023. [Online; accessed 28-April-2023]. 1 [NL15] NOBRE C., LEX A.: OceanPaths: Visualizing multivariate oceanography data. In Proceedings of the Eurographics Conference on Visualization (EuroVis 2015) - Short Papers (2015), The Eurographics Association. doi:10.2312/eurovisshort.20151124. 1 [Oku70] OKUBO A.: Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep sea research and oceanographic abstracts 17, 3 (1970), 445–454. 4 [PBI04] PARK S., BAJAJ C., IHM I.: Visualization of very large oceanography time-varying volume datasets. In Computational Science-ICCS 2004: 4th International Conference, Kraków, Poland, June 6-9, 2004, Proceedings, Part II 4 (2004), Springer, pp. 419–426. 1 [pyF23] pyferret. https://ferret.pmel.noaa.gov/Ferret/, 2023. [Online; accessed 28-April-2023]. 1 [Ros89] ROSENBLUM L. J.: Visualizing oceanographic data. IEEE computer graphics and applications 9, 3 (1989), 14–19. 1 [RR10] ROBINSON I. S., ROBINSON I. S.: Mesoscale ocean features: Eddies. Discovering the Ocean from Space: The unique applications of satellite oceanography (2010), 69–114. 1 [Sar13] SARMIENTO J. L.: Ocean biogeochemical dynamics. In Ocean Biogeochemical Dynamics. Princeton university press, 2013. 4 [SDVN22] SINGH U., DHIPU T. M., VINAYACHANDRAN P. N., NATARAJAN V.: Front and skeleton features based methods for tracking salinity propagation in the ocean. Computers & Geosciences 159 (2022), 104993. doi:https://doi.org/10.1016/j.cageo. 2021.104993. 5 [TFL∗17] TIERNY J., FAVELIER G., LEVINE J. A., GUEUNET C., MICHAUX M.: The Topology Toolkit. IEEE Transactions on Visualization and Computer Graphics 24, 1 (2017), 832–842. 5 [TZG∗17] TOYE H., ZHAN P., GOPALAKRISHNAN G., KARTADIKARIA A. R., HUANG H., KNIO O., HOTEIT I.: Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 7 (Jul 2017), 915–933. doi:10.1007/ s10236-017-1064-1. 2 [VCMN04] VINAYACHANDRAN P. N., CHAUHAN P., MOHAN M., NAYAK S.: Biological response of the sea around Sri Lanka to summer monsoon. Geophysical Research Letters 31, 1 (2004). 6 [VY98] VINAYACHANDRAN P. N., YAMAGATA T.: Monsoon response of the sea around Sri Lanka: generation of thermal domes and anticyclonic vortices. Journal of Physical Oceanography 28, 10 (1998), 1946– 1960. 5, 6 [WHP∗11] WILLIAMS S., HECHT M., PETERSEN M., STRELITZ R., MALTRUD M., AHRENS J., HLAWITSCHKA M., HAMANN B.: Visualization and analysis of eddies in a global ocean simulation. Computer graphics forum 30, 3 (2011), 991–1000. 5 [XLWD19] XIE C., LI M., WANG H., DONG J.: A survey on visual analysis of ocean data. Visual Informatics 3, 3 (2019), 113–128. 1, 2 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

pyParaOcean, A System for Visual Analysis of Ocean Data: Acknowledgments and References

pyParaOcean, একটি সিস্টেম ফর ভিজ্যুয়াল অ্যানালাইসিস অফ ওশান ডেটা: স্বীকৃতি এবং রেফারেন্স

pyParaOcean, ein System zur visuellen Analyse von Ozeandaten: Danksagungen und Referenzen

pyParaOcean, un sistema de análisis visual de datos oceánicos: reconocimientos y referencias

pyParaOcean, un système d'analyse visuelle des données océaniques : remerciements et références

pyParaOcean, महासागर डेटा के दृश्य विश्लेषण के लिए एक प्रणाली: आभार और संदर्भ

pyParaOcean、海洋データの視覚的分析システム: 謝辞と参考文献

pyParaOcean, 해양 데이터의 시각적 분석 시스템: 감사의 글 및 참고 자료

pyParaOcean, um sistema para análise visual de dados oceânicos: agradecimentos e referências

pyParaOcean, Система визуального анализа океанических данных: благодарности и ссылки

pyParaOcean, Okyanus Verilerinin Görsel Analizi İçin Bir Sistem: Teşekkür ve Referanslar

pyParaOcean, Hệ thống phân tích trực quan dữ liệu đại dương: Lời cảm ơn và tài liệu tham khảo

pyParaOcean，海洋数据可视化分析系统：致谢和参考文献

**SHERMAN OAKS, United States, May 14th, 2024/GamingWire/--**Xsolla, a global video game commerce company, published the Spring 2024 edition of "The Xsolla Report: The State of Play” today. Launched on the heels of the Game Developers Conference (GDC) 2024, this extensive report provides invaluable insights into the emerging trends and pivotal shifts impacting the gaming industry in the short and long term.  It sets the stage to significantly shape the future of mobile gaming, deepen academic connections within the gaming ecosystem, and redefine investment patterns. In an era where mobile gaming commanded a 49% share of the global market in 2023, this edition of "The State of Play" sheds light on the evolving landscape of mobile gaming. The report navigates through the advancements in global compliance and regulation, including the Digital Markets Act in Europe's new player engagement strategies, offering a glimpse into the potential future of mobile gaming monetization and distribution. It discusses the impact of cross-platform play and the importance of innovative monetization models, providing actionable insights for developers and industry stakeholders. "The State of Play" emphasizes the importance of academia in the growth and diversification of the gaming industry. It explores how educational programs and initiatives develop talent and promote diversity and inclusivity within the gaming community. This edition underlines the symbiotic relationship between the gaming industry and academic institutions, highlighting programs that significantly impact students and the industry. Berkley Egenes, Chief Marketing and Growth Officer at Xsolla, comments: "As we introduce the latest edition of 'The State of Play,' we're not merely sharing industry insights but advocating for a transformative vision: Equal Access for Everyone. This initiative goes beyond our commitment to innovation and growth within the gaming industry. It's about breaking down barriers to ensure that every developer, regardless of company size, has the opportunity to showcase their creativity and reach a global audience. Our focus is on providing platforms and tools that foster creative opportunities, international exposure, and the development of unique projects, ensuring that the future of gaming is accessible to all. We aim to empower every player and developer worldwide, ensuring the gaming landscape is as diverse and dynamic as its community." The report offers an in-depth analysis of the gaming industry's current investment climate, including funding trends, mergers, and acquisitions. It outlines the shifts in investment patterns, from the heights of the pandemic-induced boom to a more measured approach in 2024. "The State of Play" provides a roadmap for navigating the industry's financial aspects, offering insights into strategic investment opportunities and forecasting future trends. Featuring expert commentary from industry voices like Mukul Aurora, Co-founder of Appsoleut Games; Mariusz Gasiewski, CEO of Mobile Gaming and Apps Lead at Google; Karla Reyes, Founder and Studio Director, Anima Interactive, the Xsolla Report: The State of Play is now available for complimentary download. To secure your copy and gain invaluable insights into the gaming industry, visit our website:  xsolla.pro/txr-spring24 Contact PR Director Derrick Stembridge Xsolla d.stembridge@xsolla.com 919-971-7855 This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **SHERMAN OAKS, United States, May 14th, 2024/GamingWire/--**Xsolla, a global video game commerce company, published the Spring 2024 edition of "The Xsolla Report: The State of Play” today. Launched on the heels of the Game Developers Conference (GDC) 2024, this extensive report provides invaluable insights into the emerging trends and pivotal shifts impacting the gaming industry in the short and long term.  It sets the stage to significantly shape the future of mobile gaming, deepen academic connections within the gaming ecosystem, and redefine investment patterns. In an era where mobile gaming commanded a 49% share of the global market in 2023, this edition of "The State of Play" sheds light on the evolving landscape of mobile gaming. The report navigates through the advancements in global compliance and regulation, including the Digital Markets Act in Europe's new player engagement strategies, offering a glimpse into the potential future of mobile gaming monetization and distribution. It discusses the impact of cross-platform play and the importance of innovative monetization models, providing actionable insights for developers and industry stakeholders. "The State of Play" emphasizes the importance of academia in the growth and diversification of the gaming industry. It explores how educational programs and initiatives develop talent and promote diversity and inclusivity within the gaming community. This edition underlines the symbiotic relationship between the gaming industry and academic institutions, highlighting programs that significantly impact students and the industry. Berkley Egenes, Chief Marketing and Growth Officer at Xsolla, comments: "As we introduce the latest edition of 'The State of Play,' we're not merely sharing industry insights but advocating for a transformative vision: Equal Access for Everyone. This initiative goes beyond our commitment to innovation and growth within the gaming industry. It's about breaking down barriers to ensure that every developer, regardless of company size, has the opportunity to showcase their creativity and reach a global audience. Our focus is on providing platforms and tools that foster creative opportunities, international exposure, and the development of unique projects, ensuring that the future of gaming is accessible to all. We aim to empower every player and developer worldwide, ensuring the gaming landscape is as diverse and dynamic as its community." The report offers an in-depth analysis of the gaming industry's current investment climate, including funding trends, mergers, and acquisitions. It outlines the shifts in investment patterns, from the heights of the pandemic-induced boom to a more measured approach in 2024. "The State of Play" provides a roadmap for navigating the industry's financial aspects, offering insights into strategic investment opportunities and forecasting future trends. Featuring expert commentary from industry voices like Mukul Aurora, Co-founder of Appsoleut Games; Mariusz Gasiewski, CEO of Mobile Gaming and Apps Lead at Google; Karla Reyes, Founder and Studio Director, Anima Interactive, the Xsolla Report: The State of Play is now available for complimentary download. To secure your copy and gain invaluable insights into the gaming industry, visit our website: xsolla.pro/txr-spring24 xsolla.pro/txr-spring24 xsolla.pro/txr-spring24 Contact PR Director Derrick Stembridge Xsolla d.stembridge@xsolla.com 919-971-7855 This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Xsolla Releases Quarterly Insights Report On The Future Of Gaming And Game Development

Welcome to Daylight Savings Recovery Week – a period of days when most of the world expresses shock, dismay, despair, and the other 5 stages of grief about waking up an hour early and generally being cranky. Like so many things in 2024, the act of saving daylight (whatever that might actually mean) has become increasingly polarizing (and somewhat partisan) with both citizens and politicians clamoring for an end to the madness of changing our clocks twice a year. on a bill signed in 2022 that would keep daylight savings time permanently in place. Colorado is awaiting federal approval would permanently end daylight savings time. A new bill in California got a bill passed in the Senate that would put an end to DST, but it wasn’t taken up in the house. In the US Congress, a senator from Oklahoma , another Republican – from Utah this time – reintroduced a bill to make DST permanent, or at least allow states to do so if they chose. Meanwhile, over in the House In all, there are that relate to the abolishment of, or standardization of, daylight saving time. 500 bills (between state and federal legislatures) currently in play I’m sharing this by way of making two points: Lots of people have: an opinion on daylight saving time the authority to try to change it the desire to change it now There is no consistency, no unified plan on how to do this. The result of this can be described by words ranging from “absurd” to “disastrous.” “Absurd” because it’s entirely possible that, in the current situation, one state would abolish DST, while another would permanently stay on it. Meaning you could travel from Ohio at 6:00 am (with a bill currently under consideration to remain on DST), drive 1 hour to Pennsylvania where it would be 8:00 am (because they’ve continued to change their clocks seasonally); and arrive 4 hours later in New York, where the time is… no, not 12:00 pm, but instead, it would be 11:00 am. I’m probably exaggerating a little because, as I understand it, states can pass all the DST bills they want but it will require a federal ruling to allow that law to take effect. Or something like that. I’m neither a lawyer nor a politician so I’m not 100% sure. Let’s face it, it’s 2024. The push to remote work that started with the pandemic has caused many of us to feel that time is mostly meaningless. Things get done, people are awake (or asleep) whenever it suits them, and the number on the clock has less bearing on our lives than it once did. Nevertheless, the current rash of polarized opinions related to changing daylight savings time could make life (or at least certain times of day) very hard for folks who observe solar-centric, rather than clock-centric, times. , which helps developers easily incorporate and display so-called “Jewish” times (zmanim) into their websites. But here’s a quick primer for those who haven’t followed along: I’ve been writing about my work using PHP Zmanim It’s difficult to understate the importance of accurate time calculations on the Jewish religious day, week, and life. It figures into everything from when a fast starts and ends; to when it’s too late (or too early) to say certain prayers; to the moment when simply owning (let alone eating) bread is forbidden. “What time are afternoon prayers?” To understand that, we have to first understand what defines “day” (versus “night”); how to understand what demarcation separates “morning” from “afternoon”; how to effectively divide the day into parts; and so much more. Here’s a sampling of sun-specific times: Before sunrise, (the moment when the top edge of the disk of the sun touches the horizon) comes “dawn”, the moment when light can first be seen shining over the horizon. No prayers can happen before dawn. At a specific time between dawn and sunrise, it’s permissible to put on tallit and tefillin. a “seasonal hour” is determined by taking the amount of daylight (from sunrise to sunset) and dividing it into 12 equal parts. Afternoon prayers cannot be said until 6.5 seasonal hours after sunrise. My point is not to bog you down with complex (and, to the non-religious reader, seemingly whimsical) calculations. My point is that there is a significant population of folks (a rough estimate is 3.1 million people) for whom this DOES matter. And that doesn’t even begin to consider that the 5 salah (Muslim prayers) times are equally dependent on the sun. How does the current kerfluffle about DST factor in? let’s consider (all times are given for my location in Cleveland Ohio. Your Sunlight May Vary!): On Nov 5, the, time “falls” back – so that what WOULD have been an 8 am sunrise becomes 7 am. But sunrise, and sunlight, continue moving forward. In fact, the day gets progressively later all the way to Dec 31, when it hits 7:53 am Sunrise sits at that point for another 8 days, until it starts rolling back (getting earlier) starting January 8. But consider that without that shift in time, sunrise on November 6 would proceed forward – eventually stopping just shy of 9 am. For the observant Jewish person, that means dawn comes at almost 7:30 am. The earliest time for tallit and tefillin would be 7:56 am. A typical morning service runs between 30-45 minutes, meaning that most folks won’t get out of services until 8:45 am. For a person who has an early start to their day, or has to travel to the office; or get kids to school, this makes attending prayers almost impossible. On the other hand, what happens if we don’t ever go back to daylight saving? On March 9 the clocks “spring forward” so that the 6:47 am sunrise becomes 7:45 am on March 10. But sunrise continues to get earlier, all the way until June 8, when it hits 5:52 am. And sunrise sits at that time for 12 days, all the way until it finally starts to move forward again on June 20. But without the DST adjustment, sunrise would come at 4:52 am. You might expect, at this point, that I’d do a big reveal and show that sunSET is impacted. That’s not the case. Back in November, sunset goes from 6:18 pm on November 4 to 5:17 pm the following day. It keeps getting earlier until December 5 when it hits 4:56 pm. Now, I’ll freely admit that I think sunset at 5 pm kind of sucks. But I’m willing to accept it far more than having to manage prayers at almost 9 am. Meanwhile, on the other side of the equation, the latest sunset hits on June 20, at 9:04 pm. If you are completely turned around at that point, join the club. I had to do a few exports from and some manual sorting to get it all straight: timeanddate.com If we never sprang forward into daylight saving time: The earliest sunrise would be 4:49 am (on Jun 13) The latest sunrise would be 7:51 am (Jan 9) The earliest sunset would be 4:56 pm (Dec 4) The latest sunset would be 8:04 pm (Jul 4) Meanwhile, if we never fell back – meaning permanent daylight saving time: The earliest sunrise would be 5:49 am (Jun 13) The latest sunrise would be 8:51 am (Jan 9) The earliest sunset would be 5:56 pm (Dec 4) The latest sunset would be 9:04 pm (Jul 4) It’s worth clarifying what DST is giving us now: The earliest sunrise is 5:49 am (Jun 13) The latest sunrise is 7:59 am (Nov 5) The earliest sunset is 4:56 pm (Dec 4) The latest sunset is 9:04 pm (Jul 4) With all of that said, my opinion is that the DST system doesn’t offer very much benefit for the trouble. Moreover, as an orthodox Jew – for whom prayers (and therefore the cycles of sunrise and sunset) matter more than the time on a clock – remaining on standard (non-DST) time would be my choice. On the one hand, I’m in luck. While, under Federal law, states can opt out of DST, they cannot choose to remain permanently on DST without approval. On the other, it seems this is the less popular choice. In recent years, to make DST permanent have been considered in various states across the country. 19 states have passed those bills, which would immediately take effect the moment the federal government approves it. over 650 bills But for the moment, things appear to be staying put. I guess I’ll try not to lose any sleep over it. Share this: Also published here

What Daylight Are We Saving? Time Data Series

Systems Interoperability Types: The Results of Our Tertiary Study and What It All Means

OFX, aka , is an open standard for creating 2D visual effects and video compositing. It operates in a plugin-like application development model. Essentially, it serves as both a Host - an application providing a set of methods, and a Plug-in - an application or module implementing this set. OFX Image Processing API This configuration offers the potential for unlimited expansion of the host application's functionality. DaVinci Resolve and Metal Applications such as Final Cut X and DaVinci Resolve Studio, starting from version 16, fully support Apple Metal pipelines. Similar to OpenCL and Cuda, in the case of OFX, you can obtain a descriptor or handler of a platform-specific command queue. The host system also takes responsibility for allocating a pool of such queues and balancing calculations on them. Moreover, it places the source and target image clip data in GPU memory, significantly simplifying the development of extensible functionality. OFX Version Support in Resolve With Resolve, things are slightly more complicated. DaVinci announces support for OFX v1.4, albeit with some limitations. Specifically, some methods for working with interface functions are not available for use. To determine which method is available, OFX allows you to examine the supported suite through key/value queries. Publishing methods in the plugin code are based on . But we will use the OpenFXS C++ shell adapted for C++17. For convenience, I've compiled everything into one repository: taken from the open-source . C calls dehancer-external Dehancer project OFXS Concept In this project, I will be using OpenFXS, a C++ extension to OpenFX that was originally written by and has become popular over time in commercial and open-source video processing projects. Bruno Nicoletti The original was not adapted to modern C++ dialects, so I updated it to make it compatible with . OpenFXS C++17 OFX, and consequently OFXS, is a standalone software module that is dynamically loaded by the host program. Essentially, it is a dynamic library that is loaded when the main application starts. OpenFXS, like OFX, must publish method signatures. Hence, we use one C method from the code. To start developing in OpenFXS, you need to agree to a few common sets of classes that are used to create new functionality in your application. Typically, in a new project, you need to inherit from these classes and implement or override some virtual methods. To create your own plugin on the host system, let's start by familiarizing ourselves with the following public classes and the same method: is a basic template for creating a plugin's data structure suite and control panel (although it can be left empty). The inherited class creates a singleton object that registers a set of parameters and presets in the host system, with which the developer registers his module; OFX::PluginFactoryHelper - base container class for creating and storing structure properties; OFX::ParamSetDescriptor - a container of properties used when manipulating graphic data when calling data processing procedures. Used by the host application to save the context of processing parameters in the internal database and work with plugin properties defined for each of its instances; OFX::ImageEffectDescriptor - a set of settings that allows you to manipulate the registered data structure; OFX::ParamSet - a set of settings for effects on graphical data, inherited from OFX::ParamSet; OFX::ImageEffect - in the child class, it is necessary to implement data stream processing: images or videos; OFX::MultiThread::Processor - method of registering a plugin (factory) in the host application; OFX::Plugin::getPluginIDs False Color One feature that distinguishes the process of shooting video from simply capturing an image in a photo is the dynamic change of scenes and lighting of both scenes as a whole and areas in the image. This determines the way exposure is controlled during the shooting process. In digital video, there is a control monitor mode for operators in which the exposure level of areas is mapped into a limited set of zones, each tinted with its own color. This mode is sometimes called "predator" or False Color mode. The scales are usually referenced to the IRE scale. Such a monitor allows you to see the exposure zones and avoid significant mistakes when setting camera shooting parameters. Something similar in meaning is used when exposing in photography - zoning according to Adams, for example. You can measure a specific target with an exposure meter and see in which zone it is located, and in real-time we see the zones, neatly tinted for ease of perception. The number of zones is determined by the objectives and capabilities of the control monitor. For instance, a monitor used with cameras can incorporate up to 6 zones. Arri Alexa Adding Extensions Before proceeding with the example, we need to add some simple proxy classes to implement OpenFXS as a platform for processing source data, such as Metal textures. These classes include: : A proxy class for OFX clip data. imetalling::Image : A functor for transferring data from the clip buffer into a Metal texture. From DaVinci, you can extract a buffer of any structure and packaging of image channel values into the plugin, and it should be returned in a similar form. imetalling::Image2Texture To make working with the stream format in OFX easier, you can request the host to prepare data of a specific type in advance. I will use floats packed in RGBA - red/green/blue/alpha. : A reverse functor for transforming a stream into a host system buffer. As you can see, there is potential for significant optimization of calculations if you teach the Metal computing cores to work not with the texture, but directly with the buffer. imetalling::ImageFromTexture We inherit the OFXS base classes and write our functionality without going into the details of how the Metal core works: : Here, we implement the stream transformation and initiate the processing. imetalling::falsecolor::Processor : This will be our specific part of the suite description for the plugin. We need to implement several mandatory calls related to setting up the structure and create an instance of the OFX::ImageEffect class with specific functionality, which we divide into two subclasses in the implementation: Interaction and Plugin. imetalling::falsecolor::Factory : Implementation of the interactive part of working with effects. Essentially, this is the implementation of only virtual methods from OFX::ImageEffect related to processing changes in plugin parameters. imetalling::falsecolor::Interaction : Implementation of thread rendering, that is, launching imetalling::Processor. imetalling::falsecolor::Plugin Additionally, we will need several utility classes built on top of Metal to logically separate the host code and the kernel code on MSL. These include: : A base class that obscures work with the Metal command queue. The main parameter will be the name of the kernel in the MSL code, and the executor of the kernel call. imetalling::Function : A general class for transforming a source texture into a target texture, extending Function to simply set the parameters for calling the MSL kernel. imetalling:Kernel : Bypass kernel. imetalling::PassKernel : Our main functional class, a "predator" emulator that posterizes (downsamples) to a specified number of colors. imetalling::FalseColorKernel The kernel code for the "predator" mode could look like this: static constant float3 kIMP_Y_YUV_factor = {0.2125, 0.7154, 0.0721};
constexpr sampler baseSampler(address::clamp_to_edge, filter::linear, coord::normalized);
 
inline float when_eq(float x, float y) {
  return 1.0 - abs(sign(x - y));
}
 
static inline float4 sampledColor(
        texture2d<float, access::sample> inTexture,
        texture2d<float, access::write> outTexture,
        uint2 gid
){
  float w = outTexture.get_width();
  return mix(inTexture.sample(baseSampler, float2(gid) * float2(1.0/(w-1.0), 1.0/float(outTexture.get_height()-1))),
             inTexture.read(gid),
             when_eq(inTexture.get_width(), w) // whe equal read exact texture color
  );
}
 
kernel void kernel_falseColor(
        texture2d<float, access::sample> inTexture [[texture(0)]],
        texture2d<float, access::write> outTexture [[texture(1)]],
        device float3* color_map [[ buffer(0) ]],
        constant uint& level [[ buffer(1) ]],
        uint2 gid [[thread_position_in_grid]])
{
  float4  inColor = sampledColor(inTexture,outTexture,gid);
  float luminance = dot(inColor.rgb, kIMP_Y_YUV_factor);
  uint      index = clamp(uint(luminance*(level-1)),uint(0),uint(level-1));
  float4    color = float4(1);
 
  if (index<level)
    color.rgb = color_map[index];
 
  outTexture.write(color,gid);
} Initialization of the OFX Plugin We will begin by defining the class In this class, we will set a single parameter - the status of the monitor (either on or off). This is necessary for our example. imetalling::falsecolor::Factory. We will inherit from and overload five methods: OFX::PluginFactoryHelper : This method is invoked to globally configure the instance when the plugin is first loaded. Overloading this method is optional. load() : This method is invoked when an instance is unloaded, for instance, to clear memory. Overloading this method is also optional. unload() : This is the second method that the OFX host calls when the plugin is loaded. It is virtual and must be defined in our class. In this method, we need to set all the properties of the plugin, regardless of its context type. For more details about the properties, refer to the code. describe(ImageEffectDescriptor&) ImageEffectDescriptor : Similar to the method, this method is also called when the plugin is loaded and must be defined in our class. It should define properties associated with the current context. describeInContext(ImageEffectDescriptor&,ContextEnum) describe The context determines the type of operations the application works with, such as filter, paint, transition effect, or frame retimer in a clip. : This is the most crucial method that we overload. We return a pointer to an object of type . In other words, our in which we have defined all the functionalities, both with regard to user events in the host program and rendering (transforming) the source frame into the target one: createInstance(OfxImageEffectHandle, ContextEnum) ImageEffect imetalling::falsecolor::Plugin OFX::ImageEffect *Factory::createInstance(OfxImageEffectHandle handle,OFX::ContextEnum) {
     return new Plugin(handle);
   } Handling Events At this stage, if you compile a bundle with the OFX module, the plugin will already be available in the host application, and in DaVinci, it can be loaded onto the correction node. However, to work fully with a plugin instance, you need to define at least the interactive part and the part associated with processing the incoming video stream. To do this, we inherit from the class and overload virtual methods: OFX::ImageEffect - This method allows us to define the logic for handling the event. The event type is determined by the value of OFX::InstanceChangedArgs::reason and can be: eChangeUserEdit, eChangePluginEdit, eChangeTime - the event occurred as a result of a property being edited by the user, changed in a plugin or host application, or as a result of a change in the timeline. changedParam(const OFX::InstanceChangedArgs&, const std::string&) The second parameter specifies the string name that we defined at the plugin initialization stage, in our case, it is one parameter: . false_color_enabled_check_box - This method allows us to define the logic for reacting to an event, and return a state that determines whether something has changed and whether rendering makes sense. The method must return false or true. This is a way to optimize and reduce the number of unnecessary calculations. isIdentity(...) You can read the implementation of interactive interaction with OFX in the code. As you can see, we receive pointers to the clips: the source one and the memory area in which we will put the target transformation. Interaction.cpp Implementation of Rendering Launch We will add another logical layer on which we will define all the logic for launching the transformation. In our case, this is the only method for overriding so far: - Here, you can find out the properties of the clips and decide how to render them. Also, at this stage, the Metal command queue and some useful attributes associated with the current timeline properties become available to us. render(const OFX::RenderArguments& args) Processing At the launch stage, an object with useful properties became available to us: we have at least a pointer to the video stream (more precisely, a memory area with frame image data), and, most importantly, a queue of Metal commands. Now, we can construct a generic class that will bring us closer to a simple form of reusing kernel code. The OpenFXS extension already has such a class: OFX::ImageProcessor; we just need to overload it. In the constructor, it has the OFX::ImageEffect parameter, i.e., in it, we will receive not only the current state of the plugin parameters but everything necessary for working with the GPU. At this stage, we just need to overload the method and initiate the processing of kernels already implemented on Metal. processImagesMetal() Processor::Processor(
            OFX::ImageEffect *instance,
            OFX::Clip *source,
            OFX::Clip *destination,
            const OFX::RenderArguments &args,
            bool enabled
    ) :
            OFX::ImageProcessor(*instance),
            enabled_(enabled),
            interaction_(instance),
            wait_command_queue_(false),
            /// grab the current frame of a clip from OFX host memory
            source_(source->fetchImage(args.time)),
            /// create a target frame of a clip with the memory area already specified in OFX
            destination_(destination->fetchImage(args.time)),
            source_container_(nullptr),
            destination_container_(nullptr)
    {
 
      /// Set OFX rendering arguments to GPU
      setGPURenderArgs(args);
 
      /// Set render window
      setRenderWindow(args.renderWindow);
 
      /// Place source frame data in Metal texture
      source_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, source_);
 
      /// Create empty target frame texture in Metal
      destination_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, destination_);
 
      /// Get parameters for packing data in the memory area of the target frame
      OFX::BitDepthEnum dstBitDepth = destination->getPixelDepth();
      OFX::PixelComponentEnum dstComponents = destination->getPixelComponents();
 
      /// and original
      OFX::BitDepthEnum srcBitDepth = source->getPixelDepth();
      OFX::PixelComponentEnum srcComponents = source->getPixelComponents();
 
      /// show a message to the host system that something went wrong
      /// and cancel rendering of the current frame
      if ((srcBitDepth != dstBitDepth) || (srcComponents != dstComponents)) {
        OFX::throwSuiteStatusException(kOfxStatErrValue);
      }
 
      /// set in the current processor context a pointer to the memory area of the target frame
      setDstImg(destination_.get_ofx_image());
    }
 
    void Processor::processImagesMetal() {
 
      try {
 
        if (enabled_)
          FalseColorKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();
        else
          PassKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();

        ImageFromTexture(_pMetalCmdQ,
                         destination_,
                         destination_container_->get_texture(),
                         wait_command_queue_);
 
      }
      catch (std::exception &e) {
        interaction_->sendMessage(OFX::Message::eMessageError, "#message0", e.what());
      }
    } Building the Project To build the project, you will need CMake, and it must be at least version 3.15. Additionally, you will require Qt5.13, which aids in the easy and convenient assembly of the bundle with the plugin installer in the system directory. To initiate cmake, you must first create a build directory. After creating the build directory, you can execute the following command: cmake -DPRINT_DEBUG=ON -DQT_INSTALLER_PREFIX=/Users/<user>/Develop/QtInstaller -DCMAKE_PREFIX_PATH=/Users/<user>/Develop/Qt/5.13.0/clang_64/lib/cmake -DPLUGIN_INSTALLER_DIR=/Users/<user>/Desktop -DCMAKE_INSTALL_PREFIX=/Library/OFX/Plugins .. && make install Afterward, the installer, called , will appear in the directory that you specified in the parameter. Let's go ahead and launch it! Once the installation is successful, you can start DaVinci Resolve and begin using our new plugin. IMFalseColorOfxInstaller.app PLUGIN_INSTALLER_DIR You can find and select it in the OpenFX panel on the color correction page, and add it as a node. External links False Color OFX Plugin Code The Open Effects Association - OFX header file version and OFXS library code under Resolve + examples Download DaVinci Resolve

An Easy Way to Develop Your Own Apple Metal Plugin and Integrate It Into DaVinci Resolve

আপনার নিজস্ব অ্যাপল মেটাল প্লাগইন বিকাশ করার এবং এটিকে DaVinci সমাধানে সংহত করার একটি সহজ উপায়

Eine einfache Möglichkeit, Ihr eigenes Apple Metal Plugin zu entwickeln und es in DaVinci Resolve zu integrieren

Una forma sencilla de desarrollar su propio complemento Apple Metal e integrarlo en DaVinci Resolve

Un moyen simple de développer votre propre plugin Apple Metal et de l'intégrer dans DaVinci Resolve

अपना स्वयं का Apple मेटल प्लगइन विकसित करने और इसे DaVinci रिज़ॉल्यूशन में एकीकृत करने का एक आसान तरीका

独自の Apple Metal プラグインを開発して DaVinci Resolve に統合する簡単な方法

나만의 Apple Metal 플러그인을 개발하고 DaVinci Resolve에 통합하는 쉬운 방법

Uma maneira fácil de desenvolver seu próprio plug-in Apple Metal e integrá-lo ao DaVinci Resolve

Простой способ разработать собственный плагин Apple Metal и интегрировать его в DaVinci Resolve

Kendi Apple Metal Eklentinizi Geliştirmenin ve DaVinci Resolve'ye Entegre Etmenin Kolay Yolu

Một cách dễ dàng để phát triển Plugin Apple Metal của riêng bạn và tích hợp nó vào DaVinci Resolve

开发您自己的 Apple Metal 插件并将其集成到 DaVinci Resolve 中的简单方法

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It reflects Xsolla's dedication to creating solutions that address content creators' immediate needs and foster a more supportive and productive environment for their creations through an embedded finance partnership with Paysafe and a card for creators partnership with Mastercard. "We are proud to strengthen our partnership with Xsolla to provide value and exceed customer expectations with our payment solutions, further enhancing gamers' playing and payment experience globally," said Aaron Gale, VP of Sales & Account Management, Video Games at Paysafe. "Every gaming experience starts with a payment, and we are dedicated to turning these transactions into part of the overall experience, to fuel the fun and champion customer engagement and loyalty." For more details about Xsolla Wallet, please visit: xsolla.pro/wallet About Xsolla Xsolla is a global video game commerce company with a robust and powerful set of tools and services designed specifically for the industry. 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Headquartered and incorporated in Los Angeles, California, with offices in London, Berlin, Seoul, Beijing, Kuala Lumpur, Tokyo, and cities around the world, Xsolla supports major gaming titles like Valve, Twitch, Roblox, Epic Games, Take-Two, KRAFTON, Nexters, NetEase, Playstudios, Playrix, miHoYo, and more. For additional information and to learn more, please visit: xsolla.com About Paysafe Paysafe Limited (“Paysafe”) (NYSE: PSFE) ( ) is a leading payments platform with an extensive track record of serving merchants and consumers in the global entertainment sectors. Its core purpose is to enable businesses and consumers to connect and transact seamlessly through industry-leading capabilities in payment processing, digital wallet, and online cash solutions. 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Xsolla Debuts Xsolla Wallet To Provide Access To Embedded Finance Solutions And Instant Earnings

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