Projectium

projectium

Let’s talk about keyless authorization from GitHub Actions in GCP using IdP. How should we auth into GCP from GitHub Actions (it doesn't matter if we use our runners or GitHub runners)? What if we use terraform to configure GCP and GitHub Actions for our automation operations? The easiest and not the most secure way — is to create a primary GCP Service Account key (JSON) with specific permission and authorization. This is a great way to start. Let’s see how it works: It is straightforward, but what if: The service account key will be compromised? We want to limit the origins from where the key can be used. We want to reissue a new service account key automatically. Google has a great solution — . Workload Identity Federation Traditionally, applications running outside Google Cloud have used to access Google Cloud resources. Service account keys are powerful credentials, and can represent a security risk if they are not managed correctly. service account keys With identity federation, you can use Identity and Access Management (IAM) to grant external identities , including the ability to impersonate service accounts. This lets you access resources directly, using a , and eliminates the maintenance and security burden associated with service account keys. IAM roles short-lived access token You can use workload identity federation with the following: SAML 2.0 Any identity provider that supports OpenID Connect (OIDC), such as Microsoft Azure Amazon Web Services (AWS) A is an entity that describes a relationship between Google Cloud and an external identity provider, such as the following: workload identity pool provider AWS Azure Active Directory On-premises Active Directory Federation Services (AD FS) Okta Kubernetes clusters But let’s look at how to use this keyless authorization paired with GitHub Actions. On the Google Cloud side We will perform all the actions using terraform, but you can also use the UI GCP. First, let’s create Workload Identity Pool: resource "google_iam_workload_identity_pool" "github_actions" {
  provider                  = google-beta
  project                   = "my-gcp-project"
  workload_identity_pool_id = "github-actions"
  display_name              = "GitHub Actions pool"
  description               = "Workload Identity Pool managed by Terraform"
  disabled                  = false
} Then we need to create a Workload Identity Pool Provider (many providers can be assigned to one Workload Identity Pool): resource "google_iam_workload_identity_pool_provider" "github_actions" {
  provider                           = google-beta
  project                            = "my-gcp-project"
  workload_identity_pool_id          = google_iam_workload_identity_pool.github_actions.workload_identity_pool_id
  workload_identity_pool_provider_id = "github-actions"
  display_name                       = "GitHub Actions provider"
  description                        = "Workload Identity Pool Provider managed by Terraform"
  attribute_condition                = "attribute.repository_owner==\"arslanbekov\""
  attribute_mapping                  = {
    "google.subject"             = "assertion.sub"
    "attribute.actor"            = "assertion.actor"
    "attribute.aud"              = "assertion.aud"
    "attribute.repository"       = "assertion.repository"
    "attribute.repository_owner" = "assertion.repository_owner"
  }
  oidc {
    allowed_audiences = []
    issuer_uri        = "https://token.actions.githubusercontent.com"
  }
} Notice the parameter , a condition by which we can that do not fall under our filter. attribute_condition prohibit actions You can see all available values in the official . GitHub documentation We also need a service account (don’t worry, we won’t need the keys anywhere); I did not describe it in the terraform code for simplicity; I think that you can easily do it yourself or using CLI gcloud: gcloud iam service-accounts create SA_NAME \
    --description="DESCRIPTION" \
    --display-name="DISPLAY_NAME" After we have an email service account, it will need to be given a role. roles/iam.workloadIdentityUser resource "google_service_account_iam_member" "wif-sa" {
  service_account_id = "projects/my-gcp-project/serviceAccounts/example-sa@my-gcp-project.iam.gserviceaccount.com"
  role               = "roles/iam.workloadIdentityUser"
  member             = "principalSet://iam.googleapis.com/${google_iam_workload_identity_pool.github_actions.name}/*"
} Don’t forget to replace and my-gcp-project example-sa@my-gcp-project.iam.gserviceaccount.com After applying this terraform in the output, you will have two values (please save them, you will need them later): output "pool_name" {
  description = "Pool name"
  value       = google_iam_workload_identity_pool.github_actions.name
}
output "provider_name" {
  description = "Provider name"
  value       = google_iam_workload_identity_pool_provider.github_actions.name
} Example output: pool_name     = "projects/${redacted_number}/locations/global/workloadIdentityPools/github-actions"
provider_name = "projects/${redacted_number}/locations/global/workloadIdentityPools/github-actions/providers/github-actions" On the GitHub side Let’s create 2 GitHub actions secrets: (need to specify the entire line, for example: ) GCP_WORKLOAD_IDENTITY_PROVIDER_NAME projects/123456789/locations/global/workloadIdentityPools/github-actions/providers/github-actions (service account email that we created earlier and indicated in the terraform) GCP_WORKLOAD_IDENTITY_SA_EMAIL GitHub workflow example: name: "Example Workload Identity"

on:
  push:
    branches:
      - "master"

jobs:
  run:
    name: "Workload Identity Job"
    permissions:
      id-token: write
      contents: read
    runs-on: "ubuntu-latest"
    steps:
      - name: "Auth in GCP"
        id: "auth"
        uses: "google-github-actions/auth@v1"
        with:
          token_format: "access_token"
          workload_identity_provider: ${{ secrets.GCP_WORKLOAD_IDENTITY_PROVIDER_NAME }}
          service_account: ${{ secrets.GCP_WORKLOAD_IDENTITY_SA_EMAIL }}

      - name: "Docker login"
        run: |
          echo '${{ steps.auth.outputs.access_token }}' | docker login -u oauth2accesstoken --password-stdin https://gcr.io Finally, we can authorize in the docker which will be in the output of the step . access_token id: auth If you need interaction with — then this is also available after authorization. gsutils Conclusions By switching to authorization through a , we can restrict access from specific origins for approval. We also stop caring about key rotation and can control the key's lifetime. workload identity provider Links (you can find all the code described by terraform in this article in this repository) https://github.com/arslanbekov/wif-gcp-terraform (example github-actions auth) https://github.com/arslanbekov/wif-gcp-github-actions They are also published . here

Keyless Authorization From GCP to GitHub Actions in GCP Using IdP

Before making any investments, you should ensure to conduct your own thorough research and due diligence. Thanks to environmental awareness and innovative blockchain technology, the eTukTuk Coin (or $TUK) is well on its way to shining in the crypto space. The transport industry urgently needs an eco revolution. goal is to make transportation more environmentally friendly and efficient. eTukTuk's eTukTuk Price Development Forecast Predictions on the crypto market require a mix of market research, familiarity with technological developments, and a feel for the investors. Many people are interested in eTukTuk because of its unusual combination of environmental friendliness and blockchain technology. So this is what every eTukTuk outlook looks like, giving you relevant information: There is hope for eTukTuk by the end of the year. Demand for the $TUK token is predicted to surge due to popularity, exchange listings, and the release of the Driver App. These indicators suggest that a rise to $0.075 is possible by the end of the year. If you want to buy eTukTuk, you should invest specifically. End of 2024: The trend is expected to have maintained its pace until the middle of the decade. Potential drivers include eTukTuk's power staking, Layer 2 sidechain, and charitable efforts. As the number of EV charging stations grows, $TUK could see an uptrend and reach $0.17 by 2025. End of 2025: Ten years after beginning its adventure, eTukTuk hopes to have a larger share of the thriving global EV charging station market. As more people use eco-friendly products and more charging stations are built, eTukTuk's value could rise to an incredible $0.88 by 2030. End of 2030: This eTukTuk prediction can be strengthened by the platform's commitment to ESG aspects and by Binance's reliable blockchain security. eTukTuk Price History eTukTuk, often known as $TUK, has an interesting history behind it. At its starting price of $0.005, $TUK was hardly an issue in the vast crypto landscape. His lofty goals quickly eclipsed his humble beginnings. At the start of 2021, the currency received a major boost from growing demand for eco-friendly products and services. By mid-year, the price had risen to $0.03. eTukTuk has been strengthened through alliances with electric car manufacturers and involvement in green energy initiatives. The under the influence of major market movements. Investors were worried about the sharp drop in price to $0.015. However, this low point did not last forever. The rebirth began in 2022. The release of innovative utility features and smart collaborations contributed to the rise to $0.06. value of cryptocurrencies temporarily fell in 2021 However, this extraordinary growth was not entirely due to market hysteria. $TUK's practical value, based on long-term sustainability and technological advancement, distinguished it and underpinned its strong future prospects. $TUK Price History – Overview The story of eTukTuk ($TUK) is one of perseverance, adaptability, and hope for a green future. The Tokenized Utility Key ($TUK) was initially offered as a visionary cryptocurrency and quickly found favor in the green tech community and crypto fans. Although the coin was a relative newcomer to the market, its association with environmentally friendly transportation and innovative technological solutions gave it instant legitimacy and appeal. Its development was marked by a series of ups and downs, reflecting its response to external market events and internal strategic decisions. These are crucial aspects of eTukTuk Coin price development: Initial launch at $0.005, setting the base value. Mid-2021 peak at $0.03 catalyzed by green initiatives. Late 2021 plunged to $0.015, reflecting overall market turmoil. 2022 notable increase to $0.06, driven by useful features and strategic partnerships. Presale started 2023 Payment methods BNB, USDT, USD Chain Binance, BNB Hard cap 2,000,000,000 Min Invest Not specified Max Invest Not specified eTukTuk Price Prediction 2024 Even though volatility in the crypto space is extremely high, experienced analysts are often able to identify patterns and predict future directions. The coin is used for this volatility. eTukTuk The global trend towards environmentally friendly transportation is an important factor in the $TUK price. eTukTuk has a leg up on the competition because it shares the same purpose as the growing movement of governments around the world to advocate for more sustainable transportation. The rising popularity of eco-friendly transportation options could help the eTukTuk market development. Price increases could also be triggered by ongoing and planned partnerships between internet giants and sustainable transport providers. Thanks to the support of the dedicated community, $TUK could see significant growth. However, trading cryptocurrencies always carries risks. The development of $TUK in 2023 will undoubtedly be influenced by external market forces, regulatory frameworks, and technological developments. Investors should maintain a healthy balance between optimism and risk. To do this, everyone must follow both large and small market developments with great interest. eTukTuk Price Prediction 2025 Changes in regulation, new technologies, and industry dynamics are expected to have a major impact on the crypto industry through 2025. The $TUK prediction of the future value against this background is therefore an exciting matter. There are a few key factors to consider in our $TUK outlook: The global trend towards electrification is expected to increase interest in eco-friendly public transport such as eTukTuk. It could have a significant impact on the value of $TUK if electric TukTuks become mainstream in major cities around the world. Global trend towards electrification: As blockchain technology evolves and penetrates deeper into everyday services, currencies with concrete real-world uses could gain an advantage over purely speculative tokens. Mainstream adoption: The attitude of major nations towards cryptocurrencies will have a significant impact on the value of $TUK. While supportive policies can be a strong advantage, strict policies could be a hindrance. Regulatory environment: $TUK's market position could be strengthened through partnerships with industry leaders in transportation, technology, and energy. Innovative partnerships: eTukTuk Price Prediction 2030 Predicting the state of the crypto industry in 2030 requires a combination of data analysis and foresight as we look deeper into the future. The next seven years hold numerous opportunities and threats for eTukTuk Coin ($TUK). The $TUK forecast for 2030 can be summarized as follows: The need for environmentally friendly transportation options will increase dramatically as cities continue to grow. Due to their environmental friendliness and compact design, eTukTuks have the potential to become an integral part of city life. Sustainable urbanization: The combination of the Internet of Things (IoT), artificial intelligence (AI), and blockchain could create better eTukTuks or taxis that can find their way independently. They could function even without a driver and as mobile hubs for a variety of modern businesses. The $TUK token could eventually become more than just a monetary asset in this system. Tech synergy: The expansion of eTukTuks beyond their current hubs will have a major impact on their global footprint. The market potential for $TUK would skyrocket if they become a global trend. Global footprint: The stability in the crypto industry increases as it develops. The price of $TUK could be more stable and even rise in the future. Maturity of the market: Possible Price Developments of eTukTuk The future development of the coin will be influenced by both the national and local economies. Its compatibility with green transportation ensures that it will be relevant during green innovations. eTukTuk The cost of eTukTuks is heavily influenced by factors such as market acceptance, regulatory climate, technological advancements, and global growth. Despite the high growth potential, investors should be aware of the risks due to the volatility inherent in the crypto market. Investors need to analyze trends, become familiar with the market, and stay abreast of developments. Year Possible high Possible low 2024 $0.02 $0.075 2025 $0.08 $0.17 2030 $0.3 $0.88 What Is eTukTuk? There is more to eTukTuk ($TUK) than you would expect in the world of cryptocurrencies. It is the economic backbone of the eTukTuk ecosystem and has its origins in the idea of ​​sustainable and environmentally friendly transport. Traditional TukTuks, which are three-wheeled motorized vehicles popular in certain Asian countries, are now electric. The $TUK Coin aims to be at the forefront of this movement by providing an advantage for switching from gasoline-powered to eTukTuks. The value of the coin lies in the fact that it connects different systems, including those of technology, ecology, and transportation. The eTukTuk Coin is a hub for sustainable transportation because it combines the decentralized nature of blockchain with a focus on the environment. The potential reach and impact of $TUK could expand significantly as more areas realize the environmental and economic benefits of eTukTuk. This also increases its value and popularity in the crypto world. Factors That Influence the $TUK Price Several variables influence the $TUK price. The most important factor is the pace of eTukTuk adoption, as increased demand can increase the importance of $TUK. Both the cryptocurrency environment and the regulation of electric cars can have a positive or negative impact on the price. $TUK price performance is very sensitive to macroeconomic factors and investor sentiment in the larger crypto market. Finally, developments in eTukTuk's technological infrastructure and collaborations can help or hurt the company's reputation in the market. Planned CEX/DEX Listing Listing on major centralized exchanges (CEX) and decentralized exchanges (DEX) is the first step in eTukTuk's main goal. The aim of this campaign is to increase eTukTuk's liquidity and make the currency accessible to a wider range of investors. Listing on CEX allows the company to reach a community of investors who value an old-school exchange layout with modern safeguards. A listing on the DEX, on the other hand, is in line with the spirit of decentralization as it enables transactions between individual users. These planned listings demonstrate not only eTukTuk's ambition but also its commitment to providing a stable trading environment to its user base. Tokenomics What sets apart from similar crypto coins is its . The supply, distribution, and utility of the $TUK token are determined by tokenomics, which is based on a well-structured and thoughtful design. Some basic features are: eTukTuk tokenomics eTukTuk's overall production is regulated to guarantee scarcity and stimulate demand, protecting the market from inflation and oversaturation. Limited supply: As an additional reward for long-term holding, holders can enjoy staking rewards or bonuses. Reward mechanism: The value of the coin goes beyond its intrinsic value as it can be exchanged for goods and services. Useful value: The token supply can be reduced over time through a “burn mechanism” in which the tokens are destroyed at regular intervals. This strategy aims to achieve a deflationary effect. Burn mechanism: Demand Through Social Media Signals It is impossible to overstate the importance of social media in today's crypto markets. eTukTuk's popularity can be attributed in large part to the discussions and attention it has received on various social media sites. Here are a few key points to remember: Well-known members of the crypto community can catapult $TUK into the spotlight and attract a large number of new investors with just one tweet. Influencer endorsement: Vibrant online communities, such as those found on social media sites like Twitter, Reddit, and Telegram, can boost word-of-mouth marketing. Community engagement: Participation in a trending hashtag or topic can increase awareness, which can lead to a price increase. Trending topics: Memes, movies, and other forms of viral material can serve as catalysts and increase buying pressure in the $TUK market. Viral content: Is eTukTuk the Best Cryptocurrency in 2024? The “best” coin in the volatile crypto market of 2024 will be determined by a number of variables. eTukTuk's performance, features, and community support have all garnered positive attention and indicate the coin's potential. Here is a quick overview: Given its strong growth, $TUK is a strong candidate for excellent results. Performance: Unlike purely speculative tokens, it has practical use and is relevant in the transportation industry. Usefulness: $TUK has a large and active community, which bodes well for the future of the coin. Community: $TUK does not rest on its laurels, but is constantly innovating, as the announced innovations and partnerships show. Innovation: However, potential investors should conduct their own due diligence and not rely solely on market performance before making any financial commitments. How to Buy $TUK – Guide Buying new coins can be an exciting adventure, but it can seem complicated to beginners. Below is a detailed guide on how to buy $TUK: Step 1: Choose your wallet Before you buy $TUK, you should get a wallet. To store and manage your cryptocurrency, you can use one of these two wallets: Software Wallets: These wallets can be installed on different devices. They are easier to use in most cases. Hardware Wallets: Hardware wallets provide additional protection for storing cryptocurrencies outside of the cloud. Step 2: Choose an Exchange The $TUK token can be traded on a number of exchanges. Make sure you choose a trustworthy exchange: Verification: For security reasons, most services require proof of identification before granting access. Security: Protect your data by always using two-factor authentication. Step 3: Fund Your Account After you sign up, you can fund your Exchange account with fiat currency or other cryptocurrencies: Bank Transfer: Direct bank transfers are an option on some exchanges. Credit/Debit Card: Credit/debit cards are a faster, but sometimes more expensive, payment method. Step 4: Buy $TUK Now, all you need to do is buy $TUK. How to secure your coins in just two steps: Go to $TUK in the market section. Choose the amount you want to purchase and complete the deal. Step 5: Keep the Money in Your Wallet It is strongly recommended that you remove your $TUK from the exchange, and store it in a separate wallet of your choice. That’s how it works: To start the transfer, enter the recipient address of your wallet. Double-check addresses before making a transaction. If you follow these instructions, getting $TUK shouldn't be too difficult. Always put security first and review any new services or platforms you plan to use. Conclusion Many members of the crypto community are fascinated by . There has been much talk about its development potential, fueled by its unique branding coupled with its widespread mode of transportation. eTukTuk ($TUK) The volatility of the market makes it difficult to predict the exact future of a cryptocurrency, but $TUK has some intriguing features that could contribute to its success. The development of the coin is heavily influenced by external forces and market trends as well as the unique features of the currency. Anyone who wants to make a $TUK investment or learn more about it should do their research to avoid making hasty judgments. Disclaimer: This article should not be taken as investment advice. Since cryptocurrency is a highly volatile investment product, please conduct your own research before investing.

Price Projections for eTukTuk ($TUK): An In-depth Analysis of Market Development

eTukTuk ($TUK) এর জন্য মূল্য অনুমান: বাজার উন্নয়নের একটি গভীর বিশ্লেষণ

Preisprognosen für eTukTuk ($TUK): Eine eingehende Analyse der Marktentwicklung

Proyecciones de precios para eTukTuk ($TUK): un análisis en profundidad del desarrollo del mercado

Projections de prix pour eTukTuk ($TUK) : une analyse approfondie du développement du marché

eTukTuk ($TUK) के लिए मूल्य अनुमान: बाज़ार विकास का गहन विश्लेषण

eTukTuk ($TUK) の価格予測: 市場開発の詳細な分析

eTukTuk($TUK) 가격 예측: 시장 개발에 대한 심층 분석

Projeções de preços para eTukTuk ($TUK): uma análise aprofundada do desenvolvimento do mercado

Прогноз цен на eTukTuk ($TUK): углубленный анализ развития рынка

eTukTuk ($TUK) Fiyat Tahminleri: Pazar Gelişiminin Derinlemesine Bir Analizi

Dự báo giá cho eTukTuk ($TUK): Phân tích chuyên sâu về phát triển thị trường

eTukTuk ($TUK) 的价格预测：市场发展的深入分析

Authors: (1) Maggie D. Bailey, Colorado School of Mines and National Renewable Energy Lab; (2) Douglas Nychka, Colorado School of Mines; (3) Manajit Sengupta, National Renewable Energy Lab; (4) Aron Habte, National Renewable Energy Lab; (5) Yu Xie, National Renewable Energy Lab; (6) Soutir Bandyopadhyay, Colorado School of Mines. Table of Links Abstract and Intro Data Bayesian Hierarchical Model (BHM) Solar Radiation Example Results Conclusion Appendix A: Simulation Study Appendix B: Regridding Coefficient Estimates References 4 Solar Radiation Example The model described in Sect. 3 was fit once for each location in a subset area of California, shown in the far right bottom panel of Figure 2, which includes coastal and inland areas. Additionally, the model was fit for four separate months (February, May, August, and November) across all years of overlapping data (1998-2009). Initially, all covariates are included in the model. However, not all covariates were found to be significant. In particular, the CanRCM4.ERA-Int was found to hold no significance for the months of February, May, and November for most locations and no significance for about half of the locations in the month of August. The seasonal covariate held no significance for any of the months, which is expected since the data was subset into a single month from each season. Because of this, the seasonal covariate was removed for all four months. 4.1 Posterior Distribution of Model Coefficients Often, the regridded data set resulting from kriging is used as the ground truth for further analysis. This section outlines the method for analyzing the uncertainty associated with the regridding and linear model prediction step and downstream effects by generating draws from the posterior distributions of each β in Eq. 1. 4.2 Prediction Coverage This study also considers the coverage of the posterior predictions, i.e. the fraction of days where the prediction interval contains the actual value observed. This portion of the analysis holds out a single year of data as a testing set, using the remaining years as a training set. There are 12 years of overlapping data resulting in 12 out-of-sample prediction results. The final coverage is the average coverage for the 12 folds. This paper is under CC 4.0 license. available on arxiv

Uncovering Solar Energy Projections: Bayesian Model Analysis Across Seasons

সৌর শক্তি অনুমান উন্মোচন: ঋতু জুড়ে Bayesian মডেল বিশ্লেষণ

Aufdeckung von Solarenergieprojektionen: Bayesianische Modellanalyse über die Jahreszeiten hinweg

Descubriendo proyecciones de energía solar: análisis del modelo bayesiano entre estaciones

Découvrir les projections de l'énergie solaire : analyse du modèle bayésien au fil des saisons

सौर ऊर्जा अनुमानों को उजागर करना: सभी मौसमों में बायेसियन मॉडल विश्लेषण

太陽エネルギー予測を明らかにする: 季節をまたいだベイジアン モデル分析

태양 에너지 예측 파악: 계절에 따른 베이지안 모델 분석

Descobrindo as projeções de energia solar: análise do modelo bayesiano entre as estações

Раскрытие прогнозов солнечной энергии: анализ байесовской модели по сезонам

Güneş Enerjisi Projeksiyonlarını Ortaya Çıkarmak: Mevsimlere Göre Bayes Modeli Analizi

Khám phá các dự báo về năng lượng mặt trời: Phân tích mô hình Bayes qua các mùa

揭示太阳能预测：跨季节的贝叶斯模型分析

*** The History of Science Funding The scientist has always been a risky employee in terms of ROI.  They are trying to uncover truths about the universe, and oftentimes, the universe gives them the bird. Because experimenting means risking a lot of money and time on a possibly worthless idea, a scientist is a forever intern, until they make a breakthrough, then they become senior associates. That said, the scientist has been trying to make his own money. With the Alchemists, this was literal. While they invented Chemistry and catalyzed the birth of worldwide gold rushes, turning lead into gold was, alas, a wrong idea believed for centuries. The search consumed the lifetime of one person after another, with only dreams of wealth to get them through. But their tenacity was a testament to their resilience and patience. Patience meant, for the time being, remaining in the employ of someone who could afford the risk of backing science in the past. And that meant a King because they had gold/money collected from taxes. Wizards and fortune tellers didn’t make much sense, and they were comedians come to give the King a jolly time before he went to war. Though, while throwing guts on a wall may be stupid, it was mysterious and if they were right, doubly impressive. And here was this (broke) scientist, insisting that they knew a REAL strategy to help the King win. What was their credibility? Circa 200BC, the scientist is Archimedes and the King is the political head of Syracuse. Being a leader in those times largely meant you needed to win wars and as history had taught Kings the utter uselessness of charlatans, the scientist was given a chance to make his case. Archimedes made his case alright. It that he sunk the ships of the Romans with ingenious cranes and catapults that threw heavy stones. has been recounted Since his time, patronage from Kings, Princes, Bankers and Presidents to scientists has been immense. The scientist is respected, and all great scientists, even Leonardo da Vinci who was mostly fawned over for his impressive artwork, took off some time to design weapons. For weapons help the King win and hence, keep the scientist with a job. Scientists soon became ever more pivotal in war efforts. Especially Physicists and Chemists, who have designed countless forms of military hardware to date. However, the emergence of scientific methods in Economics, Sociology, Psychology, Biology, all riding on the power of statistical analysis and logical inference, quickly made the scientist important in every field of human endeavour. The problem however remained. To get a loan, the scientist usually had to, and still has to, rely on government funding because the government remains the biggest fish able to take on sufficient risk in the financial markets. Hate it all you want, but without credit from the government, be it debt or real money, a lot of scientific work would have stalled. Best example - SpaceX. However, governments and their ability to debase money and finance things on debt, made the world wars last longer to destroy the world much harder. It also led to economic depressions and after the 2008 financial crisis, Satoshi Nakamoto, a genius computer scientist, decided to create Bitcoin – a monetary tool outside the purview of government currency debasement. Birth of a financial model divorced from the King Bitcoin has been controversial from its inception. Of course, Kings and governments hate it. It is cutting into their business. A big part of the private sector also loathes its nature of priding in technological exclusivity, so that six years later, a hard fork was implemented to create Ethereum, a cryptocurrency built to provide extra blockchain services such as smart contracts. Many other hard forks quickly followed from the faithless. However, Bitcoin is still mainstream and has recently become interesting to Big Finance. The Bitcoin ETF proves as much. After 15 years of laughing at a thing that keeps building bigger and better, even the harshest critic will shut up and start listening more intimately. Because they now know they are missing something. Energy scientists are also starting to pay attention, with some claiming that Bitcoin mining may be our . are doing well selling cheaper electricity packages that have been subsidized by Bitcoin mining on the side. fast track to an energy transition Energy engineers But these are late to the party. Computer scientists have been earning well in Bitcoin, Ethereum, Doge for decades. And if any scientist has been comfortably living outside the need to get big budget funding from governments, that has been the computer scientist, from way back in the 20th century. While the field of Computer Science is much younger than Physics, Astronomy, Biology, many computer scientists quickly learned to earn big from private enterprise and individualized work, without begging the Kings and their Big Bankers for a loan. Futuristic Science Funding itself through Bitcoin Bitcoin is money for the people, and science has evolved to also be something done as a public good, on an open source plan, by dedicated people who put in their own time and money to make something good happen. As I mentioned, Computer Scientists now exist on the internet who are eagerly improving the Bitcoin ecosystem using the power of collaboration and their earnest efforts. These mostly come from the land of software development and after getting orange-pilled, decide to stay at Bitcoiner-land and build the Bitcoin ecosystem. However, there has not been much of an on-ramp for other types of scientists and this is the opportunity existing, wide open, for anybody to take. Currently, many a scientist in the Bitcoin world is a computer scientist of the type who builds zero-knowledge proofs, rollups, and more layers of the Bitcoin blockchain. But many more scientific people are needed. To re-iterate, I feel more electrical and power engineering scientists should be at the forefront of how Bitcoin mining equipment can provide electricity access to those without, making investments in clean, stranded energy sources much easier to manage. What better job than to be the electric engineer who designs their own job by bringing electricity to your people? Every university student of electrical engineering, whatever the developing country, should learn about this. Because developing countries in Africa, Asia and Latin America are struggling with scaling electricity usage from otherwise cheap energy sources, because of the high premiums that locals just cannot pay. The knowledge of using Bitcoin to subsidize electricity access from stranded energy sources should be spread as far and as widely as possible. Now, as an aspiring quantum information scientist, I feel there is also a place for me in the Bitcoin ecosystem. While the public might know how quantum information science threatens to undo the good that Bitcoin has done by potentially hacking it, I find that building quantum-resistant algorithms is far from scratching the surface of quantum capabilities. For example, quantum technologies could provide for Bitcoin nodes, allowing even weak signals to be successfully transmitted and ensure blocks are mined. better signal sensors Quantum computing could help guide better on how Bitcoin infrastructure could be better set up across nations and continents for optimum usage. solutions in logistics Quantum networks could provide better ways to securely. transmit Bitcoin information Some problems in the Bitcoin ecosystem could be (Bounded-error Quantum Polynomial) problems, which are the purview of quantum information science and these should be discovered and worked on using quantum computers. BQP Most importantly, quantum computers will be orders of magnitude better at mining proof-of-work cryptocurrencies than classical computers. When Bitcoin befriends Quantum Mechanics Moore’s law is coming to an end. IBM has already built transistors with only 5 atoms. To get smaller, it becomes harder to manage thermal noise and other nanoscale problems. To manage thermal noise, even more cooling will be done, the way it is done for qubits. Also, a 1-atom transistor is effectively a qubit. This is the quantum realm, and like it or not, more people will study quantum mechanics in the not-too-distant future, and by extension, quantum computing. The classical computing world can only scale by a multiplicity of the same technology, which will consume even more energy. The efficiency gains of quantum technologies like quantum computers will be needed. While , the science of building qubits is ramping upwards and systems are getting more powerful. There might not be an exponential growth in qubits with their own Moore’s law, but a linear growth in qubits leads to exponential computational power. Which kind of is a Moore’s law! I have said quantum computing is very expensive You remember this graph; If approximately 14 billion dollars in 2023 can bring in (logical qubits are the real qubits as manipulated in mathematical theory), by linear extrapolation, $125 billion by 2030 as seen above will bring in 428 logical qubits. 48 logical qubits But 7 years from now, Bitcoin itself might have ballooned to $200,000 per bitcoin which is a $4.2 trillion market cap. By linear extrapolation, $3 trillion is needed in global investments to take us to 10,000 logical qubits. As we can see, Bitcoin alone can pay for it over a 7-year span. Why 10,000 qubits? My Research Project Over the past three months, I have been studying QCourse551-1 which was offered in a collaboration between QWorld and Classiq Technologies. My research project was Project 11 - Bitcoin Mining using Grover’s Search Algorithm. The motivation for my project came from that shows that 10,000 logical qubits alone can do Bitcoin mining faster than any classical system using the same energy consumption. a research paper This is because 10,000 qubits can encode the full double SHA256 hashing process that goes into mining Bitcoin, but since they use Grover’s search algorithm, they effectively achieve quadratic speed-up by square_root(N) where N is the number of elements to check in an unstructured database. ASICs mining for bitcoins also check an unstructured database of random hashes for the nonce value. The question is, which one is faster? Most ASIC miners today are checking terahashes or For example checks 120TH/s. That is 120 million hashes every second. 1 million hashes every second. this miner Can a quantum computer beat that? Of course. square_root( 120 million) = only 10954 checks per second. A 1,095,400 % efficiency increase. And the more the competition, the more the quantum computer tears ahead in the race. The is rocketing upwards of 600 million million hashes per second. This is outputting enough heat to make climate scientists worried sick. But what if we replaced all those clunky ASICs with state-of-the-art QPUs from the year 2032, how many comparative hashes per second would they clock? square_root( 600 million million) = 24 million checks per second. A . current hash rate 2.4 billion % increase in efficiency If the above is not an attractive proposition, I don’t know what is. Bitcoin should fund quantum computing research. It will take a while, but when it happens, it will be glorious. It will be a more canonical event than the Moon landing. In our research, we were checking a much-simplified system with only 8-bit inputs and outputs, modeled on a Grover’s operator working with under 25 qubits. Given our really low hashing power, the advantages of Grover’s search could not be gleamed first hand, and could only be inferred with a relevant complexity analysis of the runs/depth of the Classical computer (my laptop) vs the depth of our Grover’s circuit executed on the Classiq Aer simulator. While it would have been good to model the energetics and the complexity graph of what this means, unfortunately, we ran out of time. However, we had some deliverables to show for our work. Below is what we had including the depth of each circuit. a) The simplified MD5 classical algorithm We modeled a simplified version of the MD5 (a most primitive cousin of SHA256) hashing 95 printable ASCII characters to find the nonce for hex values with difficulty = 1. Note: Our hex values were only 2-digit because we are working with 8-bit values. So our possible difficulty 1 values are only in 16 possibilities i.e. 00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0a, 0b, 0c, 0d, 0e, 0f. 00 is also a difficulty 2 element. Hence the searching/hashing function is called simplified_MD5_8bit_difficulty_1or2. def simplified_MD5_8bit_difficulty_1or2():
    printables_list = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'
    for i in printables_list:
        x2 = ord(i)

        x3 = bin(x2)[2:]

        if len(str(x3))==7:
            x4 = x3+'0'
        if len(str(x3))==6:
            x4 = x3+'00'

        x5 = list(x4)

        a0 = 2*int((x5[0]))
        a1 = int(x5[1])

        b0 = 2*int((x5[2]))
        b1 = int(x5[3])

        c0 = 2*int((x5[4]))
        c1 = int(x5[5])

        d0 = 2*int((x5[6]))
        d1 = int(x5[7])

        a = a0 + a1

        b = b0 + b1

        c = c0 + c1

        d = d0 + d1
    
        a = ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b)
        
        print()

        d1 = d//2
        d0 = d%2
        d = d1*(2**7) + d0*(2**6)

        a1 = a//2
        a0 = a%2
        a = a1*(2**5) + a0*(2**4)

        b1 = b//2
        b0 = b%2
        b = b1*(2**3) + b0*(2**2)

        c1 = c//2
        c0 = c%2
        c = c1*(2**1) + c0*(2**0)

        decimal_result = d + a + b + c
        binary_result = bin(decimal_result)[2:]
        print(binary_result)
        hex_result = hex(decimal_result)
        print(hex_result)

        if hex_result == '0x0':
            difficulty_2.append(hex_result)
            collisions_difficulty_2.append(i)
        if hex_result == ('0x1' or '0x2' or '0x3' or '0x4' or '0x5' or '0x6' or '0x7' or '0x8' or '0x9' or '0xa' or '0xb' or '0xc' or '0xd' or '0xe' or '0xf'):
            difficulty_1.append(hex_result)
            collisions_difficulty_1.append(i)

    print('Difficulty_1 list = ',difficulty_1)
    print('Difficulty_2 list = ',difficulty_2)
    print('Colliding inputs for difficulty 1 = ',collisions_difficulty_1)
    print('Colliding inputs for difficulty 2 = ',collisions_difficulty_2)
    
difficulty_2 = []
difficulty_1 = []     
collisions_difficulty_1 = []
collisions_difficulty_2 = []

import numpy as np
simplified_MD5_8bit_difficulty_1or2() As can be seen, to find the difficulty 1 (and by extension the difficulty 2) elements, we have to reiterate through 40 lines of code (ignoring spaces and printing in the for loop). There are 96 printable ASCII characters to hash and check hence 96 * 40 which gives us a circuit depth of 3840. However, this is run through fast by my laptop. Less than a few seconds. b) The simplified MD5 quantum algorithm We modeled a quantum version of our simplified MD5 algorithm to simulate finding the difficulty 1 nonce values no matter what the input ASCII character to the circuit was. This is possible because all we need is a zero at the beginning of our hex value. Since our hash set was small (only 96 ASCII characters), we tuned the system to ignore the input ASCII value so it is redundant, and to aim to capture all possible zeros given the Arithmetic expression; a == ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b) which is also in the classical Python algorithm. Notice that the 4 functions being added together are derived from the . original MD5 algorithm Since we want difficulty 1 values, we add and to the above function and then synthesize it in the Classiq platform. d = 0 a = 0 (Note: This is a shortcut thanks to the tireless work of the Classiq team. The Python code was also built in the Classiq Python SDK but it had a last-minute glitch. Hence we only have working Python SDK code for the Grover’s operator, but not for the Grover’s operator with a difficulty-1 hash/search, which is what you had to see here ) Hence our input Arithmetic expression is; a == ((d ^ (b & (c ^ d))) + (c ^ (d & (b ^ c))) + (b ^ c ^ d) + (c ^ (b | ~(d))) + b) and d == 0 and    a == 0 The resulting circuit has a depth of only 913 The quantum circuit wins! Endnote While the project time ended, our work is still a work in progress, to continue until we build a functional 10,000 qubit quantum bitcoin miner. Voluntary efforts to support us technically are most welcome. Some of our code is still glitchy and we have only scratched the surface of the Classiq platform’s capabilities. An ingenious software tool that takes away the cumbersome tinkering with gates, as would have been needed for anybody building the same project using Qiskit. Special thanks to Classiq and QWorld for the opportunity. And our mentor - Dr. Eyal Cornfeld, for the guidance. ***

Hash Rate Analysis Projections: Why Bitcoin Should Embrace Quantum Information Science

হ্যাশ রেট বিশ্লেষণ অনুমান: কেন বিটকয়েন কোয়ান্টাম তথ্য বিজ্ঞানকে আলিঙ্গন করা উচিত

Prognosen zur Hash-Rate-Analyse: Warum Bitcoin die Quanteninformationswissenschaft nutzen sollte

Proyecciones del análisis de la tasa de hash: por qué Bitcoin debería adoptar la ciencia de la información cuántica

Projections d'analyse du taux de hachage : pourquoi Bitcoin devrait adopter la science de l'information quantique

हैश दर विश्लेषण अनुमान: बिटकॉइन को क्वांटम सूचना विज्ञान को क्यों अपनाना चाहिए

ハッシュレート分析の予測: ビットコインが量子情報科学を採用すべき理由

해시레이트 분석 예측: 비트코인이 양자 정보 과학을 수용해야 하는 이유

Projeções de análise de taxa de hash: por que o Bitcoin deveria adotar a ciência da informação quântica

Прогнозы анализа скорости хэширования: почему Биткойн должен использовать квантовую информатику

Hash Rate Analizi Projeksiyonları: Bitcoin Neden Kuantum Bilgi Bilimini Kucaklamalı?

Dự đoán phân tích tỷ lệ băm: Tại sao Bitcoin nên áp dụng khoa học thông tin lượng tử

哈希率分析预测：为什么比特币应该拥抱量子信息科学

Despite not exhibiting the immediate bullish response many had anticipated in the wake of the Bitcoin Spot ETF , Bitcoin's price has seen a remarkable surge of 100% over the past year. approvals However, the ETF approvals set a precedent for as an asset class, and with the next Bitcoin halving expected midway through April this year, there is plenty of reason to be positive about this year’s price development. Bitcoin Investors Expecting a Bullish 2024? Most predictions by institutions and experts within and outside the crypto sphere forecast that BTC will soar greatly in 2024. I have collected a large number of public calls and predictions, which I will present below. Institutional Forecasts CoinFund: $250,000 - $500,000 CoinFund, a prominent venture capital firm in the crypto space, holds one of the most bullish predictions. In an email to CNBC, Seth Ginns, the company's managing partner, mentioned that BTC could rally up to $500,000 in 2024. He cited factors such as an inverse correlation with the dollar and yields, BTC Spot ETF inflows, and the potential approval of Ether Spot ETFs later in the year. “Bitcoin has a strong inverse correlation with the dollar and real yields, and both are now going down. We also expect the follow through inflows post-launch of the BTC spot ETF, as well as growing excitement around the likely approval of ETH (ether) spot ETFs later in 2024, will be quite meaningful.” Bernstein: $150,000 Private wealth management firm Bernstein predicts that BTC could reach $150,000 by 2025. This estimate was based on the possibility of an ETF approval, which has now materialized. According to a note shared by Bernstein's analyst Gautam Chhugani last year, ETFs are expected to attract approximately 10% of Bitcoin's supply. “You may not like Bitcoin as much as we do, but a dispassionate view of Bitcoin as a commodity suggests a turn of the cycle.” Pantera Capital: $148,000 Pantera Capital conducted an analysis of the shift in the stock-to-flow ratio. The findings revealed that the upcoming halving in 2024 is poised to exert a more substantial influence on diminishing the new supply percentage (47%) compared to the 2020 halving (43%). “The next halving is expected to occur on April 20, 2024.  Since most bitcoins are now in circulation, each halving will be almost exactly half as big a reduction in new supply.  If history were to repeat itself, the next halving would see bitcoin rising to $35k before the halving and $148k after.” CoinShares: $141,000 Following the approval of a Bitcoin Spot ETF, the price might rally up to $141,000 within the next 12 months based on a Fund Flows Model published by CoinShares' Head of Research James Butterfill. The foundation of the model rests on a cautious assumption, as indicated by Galaxy's , suggesting that spot Bitcoin ETFs could constitute a market valued at $14.4 trillion. If merely 10% of the investors holding these funds decide to allocate 1% of their wealth, the resulting capital influx would exceed $14.4 billion in the first year. report Matrixport: $125,000 In a note sent out last year, Matrixport predicted that BTC's price will rally up to $125,000 by the end of 2024 based on previous cycle price actions. “Historically, the years when Bitcoin mining rewards were halved were generally bullish: 2012 (+186%), 2016 (+126%), and 2020 (+297%). As miners tended to hoard Bitcoins before each halving, prices increased by +200%, which would project Bitcoin reaching $125,000.” Standard Chartered: $100,000 Last year, Standard Chartered doubled down on its prediction that BTC’s price will hit $100,000 by the end of this year. Some of the factors cited as potential bullish catalysts by the bank include “turmoil in the banking sector in March this year that helped to “re-establish Bitcoin’s use as a decentralized scarce digital asset,” approval of a Bitcoin Spot ETF and the expected Bitcoin halving event. ETC Group: $100,000 According to ETC’s Group 2024 outlook, Bitcoin’s price could hit $100,000 this year. This prediction is based on several factors, including the halving, Bitcoin’s on-chain metrics, and the adoption curve. “Bitcoin’s on-chain metrics imply that BTC’s supply is significantly scarcer, and accumulation has been more pronounced this cycle than during the last cycle.” Bitwise: $80,000 Bitwise expects BTC's upward trajectory to continue into 2024, with the price surging past $80,000, based on two major catalysts: Bitcoin Spot ETF approvals and the halving event. “Bitcoin outperformed all major asset classes in 2023, rising 128% while the S&P 500 returned 21%, gold returned 12%, and bonds returned 2%.2 We expect that trend to continue in 2024, with bitcoin trading above $80,000 and setting a new all-time high.” VanEck: All-time High One of the 15 crypto market predictions by VanEck is that Bitcoin will climb back to its all-time high in Q4 2024. The main argument supporting this prediction is the political developments expected to take place; around 45% of the world’s population, including the U.S. will go into elections this year. “Bitcoin will climb a Presidential-sized wall of worry. The percentage of the global population voting in legislative and presidential elections will hit an all-time high above 45% in 2024. This high level of important elections augurs high volatility and the prospect of significant changes.” Messari: $600,000 in the long term In the 2023 crypto thesis report, Messari acknowledges that it might be difficult to predict BTC’s price in a shorter time frame. However, based on the ‘digital gold’ narrative, this crypto analytics firm predicts BTC’s price could eventually soar up to $600,000. “We probably won’t see another 100x for bitcoin, but the asset could easily outperform other established asset classes once again in 2024. Eventual parity with gold would yield a price per BTC of over $600,000.” Expert Insights Cathie Wood (ARK Invest CEO): $1.5 million by 2030 Following the approval of 11 Bitcoin Spot ETFs earlier this month, including the ARK 21Shares Bitcoin ETF, Cathie Wood recently reiterated in a CNBC interview that her base case scenario is BTC surging to $600,000 by the end of this decade or going higher to $1.5 million under more optimistic conditions. Arthur Hayes (Former BitMEX CEO): $1 million The former BitMEX CEO has consistently maintained a highly optimistic outlook on BTC. In a recent tweet, he predicted that BTC's price will reach $1 million, driven by a potential shift in policy by the Fed, which, in turn, could trigger a rally in risk-on assets. “At this point, there is no excuse not to be long crypto. How many more times must they tell you that the fiat in your pocket is a filthy piece of trash? Believe in the Lord, and he shall set you free.” Samson Mow (JAN3com CEO): $1 million A Bitcoin enthusiast since the early days, Samson Maw predicts that Bitcoin's price could soon surge to $1 million. This projection is based on various fundamental factors that he argues are yet to be priced in, including ETF approvals, the halving, increased nation-state adoption, renewed quantitative easing (QE), and the multiplier and Veblen effect. Michael Saylor (Microstrategy CEO): $350,000 As of December 2023, MicroStrategy owned a total of 189,150 Bitcoins, establishing itself as one of the largest BTC hodlers. The company’s CEO and Bitcoin enthusiast, Michael Saylor, anticipates that Bitcoin's price will increase tenfold once responsible custodians begin managing Bitcoin. This shift is already underway, especially with the approval of spot ETFs. Tim Draper (Venture Capitalist): $250,000 In an interview with Coin Bureau towards the end of last year, American Venture Capitalist Tim Draper reiterated his prediction that BTC’s price will soon hit $250,000. This estimate is based on Bitcoin’s underlying value proposition as an asset that can also be used for transactional purposes and its decentralized nature. “Having a bank and government decide on what currency is good is not as effective as having a bitcoin where there is a trusted third party, which is hundreds of thousands of nodes making sure the transaction was done properly.” Max Keiser (Bitcoin Community): $220,000 For a long time, Max Keiser has been calling for $220,000, and following last year’s price action and the looming macro uncertainty, he recently doubled down on this prediction. “Keep in mind, Bitcoin trades at these levels AFTER social unrest and societal breakdown are irreversibly causing havoc. You have to prepare for higher prices AND economic collapse.” Tone Vays (Crypto Analyst): $200,000 A renowned analyst in the crypto space, Vays has previously predicted that BTC could surge to $200,000 or higher sometime in 2025. However, in a recent analysis, he that BTC is likely to retrace back to around $39,000 before it can regain momentum. forecasted Mike Novogratz (Galaxy Digital CEO): All-time high Back in 2021, Galaxy Digital's CEO predicted that Bitcoin's price would reach $500,000 this year. And in a more recent interview with CNBC, Mike Novogratz stated that he expects the next milestone to be breaking the all-time high of $69,000 this year and wouldn't be surprised if BTC's price climbed even higher. Plan B: $16K-$65 range Based on the stock-to-flow model, Plan B estimates that Bitcoin’s price will hover around $16K-$65K during the 2024 halving, after which it will trade between $65K-$524K for the next four years. At 2012 halving most bitcoin was <$16. At 2016 halving most BTC was in $256-$1024 range. At 2020 halving most BTC was in $4k-16k range. At 2024 halving most BTC will be in $16k-$65k range. I would not be surprised if, in the next 4 years, most BTC will transfer in the $65k-$524k range. Larry Fink (Blackrock CEO): No specific target, bullish on fundamentals BlackRock's CEO, Larry Fink, is swiftly gaining recognition as a Bitcoin bull, and with the company securing approval from the SEC, it's unsurprising that his sentiments toward BTC are notably bullish. Here are some of his insights on Bitcoin from a recent interview with Fox: If you’re fearful of your government or worrying your government is devaluing the currency, then you could see this as a great potential long-term store of value. It’s like digital gold. It is a ledger, but it’s an international ledger that’s cross-border. It’s bigger than any government. The advent of the Bitcoin ETF is an example that we’re legitimizing this asset class. Stan Druckenmiller (American Billionaire): Bitcoin is worth an investment Speaking during the Robin Hood NYC 2023 Fireside Chat, Stan Druckenmiller expressed his opinion about Bitcoin, likening it to gold’s appeal as a store of value. “I’m 76 years old. I own gold. I was surprised that Bitcoin got going. But, you know – it’s clear that the young people look at it as a store of value because it’s a lot easier to do stuff with. The young people have all the money… So, I like them both [BTC and gold]. I don’t own any Bitcoin, to be frank. But I should.” Stay Updated - Follow me on Twitter! Enjoyed these insights? There's more where that came from. Follow me on for daily tidbits and updates. Twitter/X Also published . here

Bitcoin Price Projections 2024: Insights From Experts and Institutional Forecasts

Scientific American Supplement, No. 799, April 25, 1891 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . A PROJECTING APPARATUS FOR BALANCES OF PRECISION. here A PROJECTING APPARATUS FOR BALANCES OF PRECISION. The luminous projection apparatus illustrated herewith, when adapted to a balance of precision, permits of effecting weighings very rapidly. For the same approximation, the velocity of oscillation becomes five or six times greater, and, by the method employed, the last centigrammes and the milligrammes and their fractions are estimated directly, with immediate verification. As the apparatus is independent of the parts of the balance, it can be placed on all the existing laboratory balances of precision. The modification introduced into the balance consists in the displacing of the center of gravity of the beam in such a way as to diminish the sensitiveness, and consequently to obtain a much greater velocity, and then, by optical means, to considerably increase the amplitude of the oscillations. Instead of the oscillations being observed through the microscope, they are projected upon a divided screen forming a dial, the division of which is seen by transmitted light. The apparatus consists of a small achromatic objective placed at the extremity of the tube of a microscope, in which there is a divided screen that receives the enlarged image of the reticule fixed upon the needle. Upon this reticule are projected the rays (condensed by a powerful lens) that come from a luminous source placed behind the balance. The focusing is done by means of a rack and pinion. The luminous source employed is a gas burner with reflector. This is placed in a walnut box in order to prevent any projection of heat upon the balance. This burner, thus isolated, is lighted for but one or two minutes at a maximum, at the end of each weighing. So, on fixing a thermometer in the cage, we find that no variation, ever so slight, occurs in the temperature. In order to effect a weighing, the gas being turned down to a taper, we proceed as with an ordinary balance until the extremity of the needle no longer emerges from the lower dial. Then we count the difference of the number of the divisions made by the needle to the right and left of zero. This difference, multiplied by the approximate value, in milligrammes, of each division of this dial (value given by the instrument) immediately gives the number of centigrammes and milligrammes that must be added to the weights already placed upon the pan of the balance in order to obtain an equilibrium, to about a half division of the lower dial. The value of each division of this dial varies from 3 to 10 milligrammes according as the balance shows 0.1 or 0.5 milligramme. As the dial has 10 divisions on each side of the central mark, we thus estimate, without tentatives, the three last centigrammes or the last decigramme, according to the sensitiveness. At this moment the doors of the cage are closed, in order to prevent draughts of air, the gas is turned on by means of a regulating cock, and the balance is manipulated by first lowering the beam and then bringing the pans to a standstill. We then read the difference of the divisions traversed to the left and right upon the luminous dial through the image of the reticule. The images are reversed upon the dial, but practice soon causes this petty difficulty to disappear. This number of divisions indicates the number of milligrammes and fractions of a milligramme by which it is necessary to shift the counterpoise on its arm in order to obtain a perfect equilibrium, which latter is verified by a simple reading. Every half division of the dial corresponds, as to weight, to the sensitiveness indicated for the instrument. With a little practice a weighing effected as above described takes but a quarter or a fifth of the time that it does with an ordinary balance.— Revue Industrielle. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2004). Scientific American Supplement, No. 799, April 25, 1891. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/11649/pg11649-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

A PROJECTING APPARATUS FOR BALANCES OF PRECISION

Identifying Axion Conversion in Compact Star Magnetospheres With Radio-Wave Polarization Signatures

The Data Revolution: AI Takes the Wheel

How to Streamline CI/CD With Octopus and TeamCity

অক্টোপাস এবং টিমসিটির সাথে কীভাবে সিআই/সিডি স্ট্রিমলাইন করবেন

So optimieren Sie CI/CD mit Octopus und TeamCity

Cómo optimizar CI/CD con Octopus y TeamCity

Comment rationaliser le CI/CD avec Octopus et TeamCity

ऑक्टोपस और टीमसिटी के साथ CI/CD को कैसे सुव्यवस्थित करें

Octopus と TeamCity で CI/CD を効率化する方法

Octopus와 TeamCity를 사용하여 CI/CD를 간소화하는 방법

Como simplificar CI/CD com Octopus e TeamCity

Как оптимизировать CI/CD с помощью Octopus и TeamCity

Octopus ve TeamCity ile CI/CD Nasıl Kolaylaştırılır

Cách hợp lý hóa CI/CD với Octopus và TeamCity

如何使用 Octopus 和 TeamCity 简化 CI/CD

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Muhammed Yusuf Kocyigit, Boston University;
(2) Anietie Andy, University of Pennsylvania;
(3) Derry Wijaya, Boston University. Table of Links Abstract and Intro
Related Works
Data
Method
Analysis and Results
Conclusion
Limitations
Ethics Statement and References
Appendix: Toxicity Measurement
Appendix: Correlation Over Time
Appendix: Wikidata
Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385. This paper is available on arxiv under CC 4.0 license. Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. Table of Links Abstract and Intro Related Works Data Method Analysis and Results Conclusion Limitations Ethics Statement and References Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Wikidata Appendix: Hyperparameter Sensitivity Abstract and Intro Abstract and Intro Related Works Related Works Data Data Method Method Analysis and Results Analysis and Results Conclusion Conclusion Limitations Limitations Ethics Statement and References Ethics Statement and References Appendix: Toxicity Measurement Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Correlation Over Time Appendix: Wikidata Appendix: Wikidata Appendix: Hyperparameter Sensitivity Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385.

A Novel Method for Analysing Racial Bias: Ethics Statement and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Andrea Roncoli, Department of Computer, Science (University of Pisa);
(2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago);
(3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory);
(4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute);
(5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). Table of Links Abstract and Intro Data and Methods Results Conclusions Acknowledgments and Disclosure of Funding, and References Additional Plots 3 Results Latent space organization Isomaps are two-dimensional projections of the multi-dimensional latent space [35]. Figure 1 shows the difference in the latent space structure without (top row) and with (bottom row) DA. Ellipses in the top right isomap highlight how the two distributions are encoded in different regions of the latent space. Without the MMD loss, the model encodes samples with very different values of Ωm close to each other, if they originate from different simulations (circles and triangles of different colors are overlapping). This scenario leads to the fragility of the regressor, which cannot learn to output different values for the same latent space encodings. On the contrary, the DA-GNN (bottom right plot) correctly encodes the samples in a domain-invariant way. Visually, circle and triangle distributions are overlapping, which indicates domain mixing. Furthermore, the direction in the color gradient shows that the DA-GNN encodes information such that the regressor can now more correctly predict cosmological parameters based on the encodings of both simulations. [5] In [41], authors get slightly better results for the same domain, and slightly worse for the cross-domain tests. We impute these differences to choices such as batch sizes and optimization techniques we took due to computational and time constraints. [6] In Appendix A, the IllustrisTNG counterpart of this plot is presented. This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrea Roncoli, Department of Computer, Science (University of Pisa); (2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago); (3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory); (4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute); (5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). This paper is available on arxiv under CC 4.0 license. available on arxiv Authors: Authors: (1) Andrea Roncoli, Department of Computer, Science (University of Pisa); (2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago); (3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory); (4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute); (5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). Table of Links Abstract and Intro Abstract and Intro Data and Methods Data and Methods Results Results Conclusions Conclusions Acknowledgments and Disclosure of Funding, and References Acknowledgments and Disclosure of Funding, and References Additional Plots Additional Plots 3 Results Latent space organization Isomaps are two-dimensional projections of the multi-dimensional latent space [35]. Figure 1 shows the difference in the latent space structure without (top row) and with (bottom row) DA. Ellipses in the top right isomap highlight how the two distributions are encoded in different regions of the latent space. Without the MMD loss, the model encodes samples with very different values of Ωm close to each other, if they originate from different simulations (circles and triangles of different colors are overlapping). This scenario leads to the fragility of the regressor, which cannot learn to output different values for the same latent space encodings. On the contrary, the DA-GNN (bottom right plot) correctly encodes the samples in a domain-invariant way. Visually, circle and triangle distributions are overlapping, which indicates domain mixing. Furthermore, the direction in the color gradient shows that the DA-GNN encodes information such that the regressor can now more correctly predict cosmological parameters based on the encodings of both simulations. [5] In [41], authors get slightly better results for the same domain, and slightly worse for the cross-domain tests. We impute these differences to choices such as batch sizes and optimization techniques we took due to computational and time constraints. [6] In Appendix A, the IllustrisTNG counterpart of this plot is presented.

Adaptive Graph Neural Networks for Cosmological Data Generalization: Results

While artificial intelligence (AI) shows potential to increase profitability across most industries, its adoption is also resulting in a widening global skills gap, making upskilling workforces in AI and machine learning (ML) imperative for progress. A report by KPMG shows that among leading businesses prioritizing technology investment for profitability, a resounding 68% identify AI and machine learning as indispensable to their short-term plans. This means the need for AI skilling is such that it requires employees, employers, business organizations, and educational institutions all to pitch in. In short, building the AI-skilled workforce has to be a truly global effort. Enhancing skills in AI and Machine Learning is no longer optional but a necessity
Ruban Phukan, co-founder and CEO of GoodGist “Enhancing skills in AI and Machine Learning is no longer optional but a necessity,” Ruban Phukan, co-founder and CEO of GoodGist, an autonomous learning startup, told The Tech Panda. And the reason for this necessity, he says, is that AI saves time. “In an era where time is the scarcest resource and the boundaries between work and personal life increasingly blur, leveraging AI to optimize time is one of the most significant steps individuals can take to boost both their professional and personal productivity. This reality underscores the importance of upskilling,” Phukan explains. According to a Slack survey, workplace adoption of AI tools has sped up 24% over the previous quarter, with one out of four desk workers saying they have used AI tools at their work as of January 2024, as compared to one in five as of September 2023. “Upskilling, however, transcends mere familiarity with AI usage. It’s a critical response to the profound shifts in the business landscape and job market triggered by the rapid evolution of technology,” he adds. According to the World Economic Forum, nearly 1 billion people worldwide will need to upskill by 2030, with Korn Ferry warning that failing to do so could result in an $8.5 trillion loss in potential revenue. This is a stark backdrop to McKinsey’s projection that 87% of companies will experience a skills gap in the coming years, making upskilling not just imperative but urgent as time is of the essence here. Job postings for AI are high, but where is the talent? As AI gets integrated into all kinds of sectors, companies are starting to compete for AI talent. AI job postings surged 42% since ChatGPT’s debut, as Big Tech companies like Amazon, Google, Apple, and others like global logistics firm UPS, are beginning to shift resources to channel money into hiring machine learning engineers and data scientists. Companies are actively in search of skilled professionals to fill roles from technical leadership to applied research. These include eBay, Krutrim, Accenture, Dentsu, Gramener and others. According to an AIM report on generative AI salaries in India, large companies are offering INR 1 crore or more annually for senior roles, while startups and mid-size companies are ready to give out approx. INR 30-40 lakh. However, are these companies finding the talent they seek? As per new research, about three in four employers are struggling to hire for the AI skills they seek even though they are willing to boost salaries 43% in marketing and 35-42% across HR, finance, and legal roles. In fact, with the acceleration in AI adoption, companies are beginning to rely on education partnerships and gig work to reach the talent required. This is why skilling programs in AI are afoot. In March, IIM Lucknow and Emeritus launched the “Digital Transformation and AI for Business Leaders” Program to shape the next generation of AI leaders, an 11-month program designed to help leaders gain comprehensive insights into the intricacies of digital transformation and AI. Similarly, Physics Wallah (PW), an Indian EdTech platform, launched PW Institute of Innovation (PW IOI), a four-year residential program in Computer Science and AI to address the critical challenges faced by the Indian software industry, strategically located in Bengaluru, the Silicon Valley of India. The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach
Vishwa Mohan, CIO at PW & President of PW IOI Vishwa Mohan, CIO at PW & President of PW IOI, said, “It is indeed concerning that despite the exponential growth witnessed in the Indian software industry, the employability rate of Indian engineering graduates has remained relatively low, with only marginal improvement over the years. Additionally, the significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach.” Employees want AI skilling from employers At the same time, employee expectations are starting to include skilling and training in AI. According to a study by EPAM India, 93% of modern engineers seek upskilling opportunities in AI, Cloud, and other new-age technologies, while 92% look for opportunities to enhance their problem-solving skills to identify and implement the best solutions. Also, 91% of respondents stated that future developmental and on-job training opportunities are their top expectations from their workplaces. Regarding their perspective for the future, 68% and 46% of modern engineers, respectively, believe that Data Analytics and AI will be the demand in the future, enabling enhancing consumer experience and collaboration. Some companies have started working on these expectations in a bid to retain talent. For example, Findability Sciences, an enterprise AI solution has trained over 100 of its data analysts and scientists across its Boston, India, and Japan operations with intensive training in problem formulation and prompt design and engineering skills. Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on
Mandar Kulkarni, AVP of Pre-sales Mandar Kulkarni, AVP of Pre-sales, said, “Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on.”?? The aim is to arm its team with the latest skills to cater to the evolving requirements of its existing and incoming customers in the generative AI arena. “This training is more than an investment in our team; it’s an investment in our clients,” says Anurag Srivastava, the Global Delivery Head. In a tech-driven business landscape, AI and machine learning expertise empower professionals to navigate disruption and contribute significantly to short-term viability and long-term success, hence its necessity as a skill is becoming inevitable. Every ecosystem has to pool in resources to meet the AI demand that will only go up in the future. Navanwita Sachdev, Editor, The Tech Panda While artificial intelligence (AI) shows potential to increase profitability across most industries, its adoption is also resulting in a widening global skills gap, making upskilling workforces in AI and machine learning (ML) imperative for progress. artificial intelligence A report by KPMG shows that among leading businesses prioritizing technology investment for profitability, a resounding 68% identify AI and machine learning as indispensable to their short-term plans. report by KPMG This means the need for AI skilling is such that it requires employees, employers, business organizations, and educational institutions all to pitch in. In short, building the AI-skilled workforce has to be a truly global effort. Enhancing skills in AI and Machine Learning is no longer optional but a necessity Ruban Phukan, co-founder and CEO of GoodGist Enhancing skills in AI and Machine Learning is no longer optional but a necessity Enhancing skills in AI and Machine Learning is no longer optional but a necessity Enhancing skills in AI and Machine Learning is no longer optional but a necessity Ruban Phukan, co-founder and CEO of GoodGist “Enhancing skills in AI and Machine Learning is no longer optional but a necessity,” Ruban Phukan , co-founder and CEO of GoodGist , an autonomous learning startup, told The Tech Panda . And the reason for this necessity, he says, is that AI saves time. Ruban Phukan GoodGist The Tech Panda “In an era where time is the scarcest resource and the boundaries between work and personal life increasingly blur, leveraging AI to optimize time is one of the most significant steps individuals can take to boost both their professional and personal productivity. This reality underscores the importance of upskilling,” Phukan explains. According to a Slack survey , workplace adoption of AI tools has sped up 24% over the previous quarter, with one out of four desk workers saying they have used AI tools at their work as of January 2024, as compared to one in five as of September 2023. a Slack survey “Upskilling, however, transcends mere familiarity with AI usage. It’s a critical response to the profound shifts in the business landscape and job market triggered by the rapid evolution of technology,” he adds. According to the World Economic Forum , nearly 1 billion people worldwide will need to upskill by 2030, with Korn Ferry warning that failing to do so could result in an $8.5 trillion loss in potential revenue. This is a stark backdrop to McKinsey’s projection that 87% of companies will experience a skills gap in the coming years, making upskilling not just imperative but urgent as time is of the essence here. According to the World Economic Forum Korn Ferry warning McKinsey’s projection Job postings for AI are high, but where is the talent? Job postings for AI are high, but where is the talent? As AI gets integrated into all kinds of sectors, companies are starting to compete for AI talent. AI job postings surged 42% since ChatGPT’s debut, as Big Tech companies like Amazon, Google, Apple, and others like global logistics firm UPS, are beginning to shift resources to channel money into hiring machine learning engineers and data scientists. AI job postings Companies are actively in search of skilled professionals to fill roles from technical leadership to applied research. These include eBay, Krutrim, Accenture, Dentsu, Gramener and others. According to an AIM report on generative AI salaries in India, large companies are offering INR 1 crore or more annually for senior roles, while startups and mid-size companies are ready to give out approx. INR 30-40 lakh. These include an AIM report However, are these companies finding the talent they seek? As per new research , about three in four employers are struggling to hire for the AI skills they seek even though they are willing to boost salaries 43% in marketing and 35-42% across HR, finance, and legal roles. In fact, with the acceleration in AI adoption, companies are beginning to rely on education partnerships and gig work to reach the talent required. research This is why skilling programs in AI are afoot. In March, IIM Lucknow and Emeritus launched the “Digital Transformation and AI for Business Leaders” Program to shape the next generation of AI leaders, an 11-month program designed to help leaders gain comprehensive insights into the intricacies of digital transformation and AI. IIM Lucknow Emeritus Similarly, Physics Wallah (PW), an Indian EdTech platform, launched PW Institute of Innovation (PW IOI), a four-year residential program in Computer Science and AI to address the critical challenges faced by the Indian software industry, strategically located in Bengaluru, the Silicon Valley of India. Physics Wallah The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach Vishwa Mohan, CIO at PW & President of PW IOI The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach The significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach Vishwa Mohan, CIO at PW & President of PW IOI Vishwa Mohan, CIO at PW & President of PW IOI, said, “It is indeed concerning that despite the exponential growth witnessed in the Indian software industry, the employability rate of Indian engineering graduates has remained relatively low, with only marginal improvement over the years. Additionally, the significant increase in the demand-supply gap for skilled AI and data science professionals further highlights the need for a comprehensive transformation in the learning approach.” Employees want AI skilling from employers Employees want AI skilling from employers At the same time, employee expectations are starting to include skilling and training in AI. According to a study by EPAM India, 93% of modern engineers seek upskilling opportunities in AI, Cloud, and other new-age technologies, while 92% look for opportunities to enhance their problem-solving skills to identify and implement the best solutions. Also, 91% of respondents stated that future developmental and on-job training opportunities are their top expectations from their workplaces. study Regarding their perspective for the future, 68% and 46% of modern engineers, respectively, believe that Data Analytics and AI will be the demand in the future, enabling enhancing consumer experience and collaboration. Some companies have started working on these expectations in a bid to retain talent. For example, Findability Sciences , an enterprise AI solution has trained over 100 of its data analysts and scientists across its Boston, India, and Japan operations with intensive training in problem formulation and prompt design and engineering skills. Findability Sciences Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Mandar Kulkarni, AVP of Pre-sales Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on Mandar Kulkarni, AVP of Pre-sales Mandar Kulkarni, AVP of Pre-sales, said, “Empowering our team with these new skills is a strategic move that will pay dividends for our clients and the company. The future of AI is generative, and we are now more than ready to take it head-on.”?? The aim is to arm its team with the latest skills to cater to the evolving requirements of its existing and incoming customers in the generative AI arena. “This training is more than an investment in our team; it’s an investment in our clients,” says Anurag Srivastava, the Global Delivery Head. In a tech-driven business landscape, AI and machine learning expertise empower professionals to navigate disruption and contribute significantly to short-term viability and long-term success, hence its necessity as a skill is becoming inevitable. Every ecosystem has to pool in resources to meet the AI demand that will only go up in the future. Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Editor, The Tech Panda Navanwita Sachdev, Navanwita Sachdev, Navanwita Sachdev, Editor, The Tech Panda

The Talent Quest: Companies' High Stakes in the AI Workforce Race

As data technology rapidly advances, understanding and mastering various tools and techniques becomes increasingly important. To this end, we are preparing to launch a Demo demonstration plan on the Apache SeaTunnel community on how to use connectors, inviting you all who are passionate about data synchronization technology to share your knowledge and practical experience! In this issue, we have invited Mr. Gao Jun, a PMC member of the community, to record a demo tutorial in the video which themed How to sync data from MySQL to Doris. If you are interested in this plan, feel free to email me(xiyan@whaleops.com) to participate in the Demo recording! Whether you are a data engineer, developer, or technology enthusiast, you are welcome to showcase your technical talents. If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! Demo Goals Our goal is to create a platform for sharing and learning, helping community members better understand and apply various data connectors through specific Demo demonstrations and corresponding documentation. These demos can help beginners learn quickly and also provide a stage for senior experts to showcase innovative solutions. https://youtu.be/_xB9BidoNOA?embedable=true https://youtu.be/T8AZVxuzfeQ?embedable=true https://youtu.be/kzAvfqnO4yc?embedable=true MySQL to Doris JDBC MySQL Connector Description Read data from external data sources via JDBC. Supported MySQL Versions 5.5/5.6/5.7/8.0 Supported Engines SparkFlinkSeaTunnel Zeta Dependencies Spark/Flink engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. SeaTunnel Zeta engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. Key Features of MySQL Connector Batch processing
Stream processing
Exactly once
Column projection
Parallel processing
Support for user-defined sharding
Support for reading from multiple tables Supports query SQL and can achieve projection effect. Supported Data Source Information Database Dependencies Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Data Type Mapping Source Options Tips If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. Task Example Simple: This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. # Defining the runtime environment
env {
  # You can set flink configuration here
  execution.parallelism = 2
  job.mode = "BATCH"
}
source{
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        query = "select * from type_bin limit 16"
    }
}
transform {
    # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/transform-v2/sql
}
sink {
    Console {}
} Parallel: Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        # Parallel sharding reads fields
        partition_column = "id"
        # Number of fragments
        partition_num = 10
    }
} Parallel Boundary: It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        partition_column = "id"
        # Read start boundary
        partition_lower_bound = 1
        # Read end boundary
        partition_upper_bound = 500
        partition_num = 10
    }
} Above is the code and documentation explanation shown in the video. You can combine it with the explanations in the video for hands-on practice. If you are interested in recording, please continue reading below. How to submit your Demo recording Participating in the Demo recording is simple! First, choose a connector you are familiar with and prepare a 5 to 10-minute video demonstration. Make sure your video clearly shows how to configure and use the connector, what problems it solves, and any possible best practices. Once you have your demo video ready, you can email me via debra.chen12@gmail.com. Remember to include relevant descriptive information, such as the connector you’re demonstrating and an overview of the video content. That’s how you can participate in the Demo recording, take action now! As data technology rapidly advances, understanding and mastering various tools and techniques becomes increasingly important. To this end, we are preparing to launch a Demo demonstration plan on the Apache SeaTunnel community on how to use connectors, inviting you all who are passionate about data synchronization technology to share your knowledge and practical experience! In this issue, we have invited Mr. Gao Jun, a PMC member of the community, to record a demo tutorial in the video which themed How to sync data from MySQL to Doris . If you are interested in this plan, feel free to email me(xiyan@whaleops.com) to participate in the Demo recording! Whether you are a data engineer, developer, or technology enthusiast, you are welcome to showcase your technical talents. How to sync data from MySQL to Doris If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! If you are a user and want to see synchronization scenarios Demo, please scroll down to the bottom and leave a comment, and we will prioritize producing the Demo with the highest demand for synchronization scenarios! Demo Goals Our goal is to create a platform for sharing and learning, helping community members better understand and apply various data connectors through specific Demo demonstrations and corresponding documentation. These demos can help beginners learn quickly and also provide a stage for senior experts to showcase innovative solutions. https://youtu.be/_xB9BidoNOA?embedable=true https://youtu.be/_xB9BidoNOA?embedable=true https://youtu.be/T8AZVxuzfeQ?embedable=true https://youtu.be/T8AZVxuzfeQ?embedable=true https://youtu.be/kzAvfqnO4yc?embedable=true https://youtu.be/kzAvfqnO4yc?embedable=true MySQL to Doris JDBC MySQL Connector JDBC MySQL Connector JDBC MySQL Connector Description Read data from external data sources via JDBC. Supported MySQL Versions 5.5/5.6/5.7/8.0 5.5/5.6/5.7/8.0 Supported Engines SparkFlinkSeaTunnel Zeta SparkFlinkSeaTunnel Zeta SparkFlinkSeaTunnel Zeta Dependencies Spark/Flink engine Spark/Flink engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/plugins/ directory. SeaTunnel Zeta engine SeaTunnel Zeta engine You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. You need to ensure that the jdbc driver jar is placed in the ${SEATUNNEL_HOME}/lib/ directory. Key Features of MySQL Connector Batch processing Stream processing Exactly once Column projection Parallel processing Support for user-defined sharding Support for reading from multiple tables Batch processing Stream processing Exactly once Column projection Parallel processing Support for user-defined sharding Support for reading from multiple tables Supports query SQL and can achieve projection effect. Supports query SQL and can achieve projection effect. Supports query SQL and can achieve projection effect. Supported Data Source Information Database Dependencies Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Please download the support list corresponding to ‘Maven’ and copy it to ‘SEATNUNNEL_HOME/plugins/jdbc/lib/’. Data Type Mapping Source Options Tips If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. If partition_column is not set, it will run in single concurrency, and if partition_column is set, it will be executed in parallel according to the concurrency of tasks. Task Example Simple: This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. This example queries type_bin ‘table’ 16 data in your test “database” in single parallel and queries all of its fields. You can also specify which fields to query for final output to the console. # Defining the runtime environment
env {
  # You can set flink configuration here
  execution.parallelism = 2
  job.mode = "BATCH"
}
source{
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        query = "select * from type_bin limit 16"
    }
}
transform {
    # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/transform-v2/sql
}
sink {
    Console {}
} # Defining the runtime environment
env {
  # You can set flink configuration here
  execution.parallelism = 2
  job.mode = "BATCH"
}
source{
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        query = "select * from type_bin limit 16"
    }
}
transform {
    # If you would like to get more information about how to configure seatunnel and see full list of transform plugins,
    # please go to https://seatunnel.apache.org/docs/transform-v2/sql
}
sink {
    Console {}
} Parallel: Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table Read your query table in parallel with the shard field you configured and the shard data You can do this if you want to read the whole table source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        # Parallel sharding reads fields
        partition_column = "id"
        # Number of fragments
        partition_num = 10
    }
} source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        # Parallel sharding reads fields
        partition_column = "id"
        # Number of fragments
        partition_num = 10
    }
} Parallel Boundary: It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured It is more efficient to specify the data within the upper and lower bounds of the query It is more efficient to read your data source according to the upper and lower boundaries you configured source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        partition_column = "id"
        # Read start boundary
        partition_lower_bound = 1
        # Read end boundary
        partition_upper_bound = 500
        partition_num = 10
    }
} source {
    Jdbc {
        url = "jdbc:mysql://localhost:3306/test?serverTimezone=GMT%2b8"
        driver = "com.mysql.cj.jdbc.Driver"
        connection_check_timeout_sec = 100
        user = "root"
        password = "123456"
        # Define query logic as required
        query = "select * from type_bin"
        partition_column = "id"
        # Read start boundary
        partition_lower_bound = 1
        # Read end boundary
        partition_upper_bound = 500
        partition_num = 10
    }
} Above is the code and documentation explanation shown in the video. You can combine it with the explanations in the video for hands-on practice. If you are interested in recording, please continue reading below. How to submit your Demo recording Participating in the Demo recording is simple! First, choose a connector you are familiar with and prepare a 5 to 10-minute video demonstration. Make sure your video clearly shows how to configure and use the connector, what problems it solves, and any possible best practices. Once you have your demo video ready, you can email me via debra.chen12@gmail.com. Remember to include relevant descriptive information, such as the connector you’re demonstrating and an overview of the video content. That’s how you can participate in the Demo recording, take action now!

How to Sync Data From MySQL to Doris Using SeaTunnel 

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Methodology

Authors:
(1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu;
(2) Eric Bier, Palo Alto Research Center, USA bier@parc.com;
(3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com;
(4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov;
(5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References 2 Related Work Floor plans are crucial for many applications. Software approaches to floor plan creation depend on data availability and data format. Our work builds on previous research on data collection and floor plan computation. Data collection. Indoor environments can be captured in many formats, including RGBD images, point clouds, and triangle meshes. Zhang et al. [29] uses panoramic RGBD images as input and reconstructs geometry using structure grammars, while [21] uses a 3D scan to extract plane primitives and generates models using heuristics. A single image is used in some deep learning methods [8,11,13,14,30] to generate cuboid-based layouts for a single room. Detailed semi-constrained floor plan computations for a complete house require processing a 3D scan of the house [15]; the complete scan increases accuracy, but increases computing requirements and time. Pintore and Gobbetti [23] proposed a technique to create floor plans and 3D models using an Android device camera, leveraging sensor data and statistical techniques. Chen et al. [7] introduced an augmented reality system utilizing the Microsoft Hololens for indoor layout assessment, addressing intuitive evaluation and efficiency challenges. In our approach, we begin with a triangle mesh from a HoloLens 2, using its Spatial Mapping software [18], which has been surveyed by Weinmann et al. [27]. Floor plan computations Early methods [1,3,22,28] relied on image processing techniques, such as histograms and plane fitting, to create floor plans from 3D data. While [22] creates a floor plan by detecting vertical planes in a 3D point cloud, [3] uses planar structure extraction to create floor plans. These techniques rely on heuristics and were prone to failure due to noise in the data. There has been much progress in floor plan computation using graphical models [4,9,12]. Such models [10] are also used to recover layouts and floor plans from crowd-sourced images and location data. One interactive tool [16] creates desirable floorplans by conforming to design constraints. Pintore et al. [24] characterizes several available input sources (including the triangle meshes that we use) and output models and discusses the main elements of the reconstruction pipeline. It also identifies several systems for producing floor plans, including FloorNet [15], and Floor-SP [6]. Monszpart et al. [19] introduced an algorithm that exploits the observation that distant walls are generally parallel to identify dominant wall directions using k-means. Our approach also utilizes k-means, but does so to identify walls in all directions, not just the dominant ones. Cai et al. [5] uses geometric priors, including point density, indoor area recognition, and normal information, to reconstruct floorplans from raw point clouds. In contrast to Arikan et al. [2], which employed a greedy algorithm to find plane normal directions and fit planes to points with help from user interaction, our approach is automatic. It also differs from the work of [20], which focuses on removing clutter and partitioning the interior into a 3D cell complex; our method specifically divides the building into separate walls. Our work is related to [22] and [26]. In [22], floor plan generation starts with a laser range data point cloud, followed by floor and ceiling detection using a height histogram. The remaining points are projected onto a ground plane, where a density histogram and Hough transform are applied to generate the line segments that form a floor plan. In projecting to 2D, their method risks losing information that may be useful for creating 3D models or detailed floor plans. Similarly, [26] utilizes a histogram-based approach to detect ceilings and floors. Their method involves identifying taller wall segments to create a 2D histogram, and then employing heuristics based on histogram point density to compute the floor plan. Our approach differs from [22] and [26] by aligning the mesh with global coordinate axes and not relying on laser data or a point cloud. Working primarily with 3D data throughout the pipeline, it benefits from enhanced information and generates both a 3D model and a floor plan. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References 2 Related Work Floor plans are crucial for many applications. Software approaches to floor plan creation depend on data availability and data format. Our work builds on previous research on data collection and floor plan computation. Data collection. Indoor environments can be captured in many formats, including RGBD images, point clouds, and triangle meshes. Zhang et al. [29] uses panoramic RGBD images as input and reconstructs geometry using structure grammars, while [21] uses a 3D scan to extract plane primitives and generates models using heuristics. A single image is used in some deep learning methods [8,11,13,14,30] to generate cuboid-based layouts for a single room. Detailed semi-constrained floor plan computations for a complete house require processing a 3D scan of the house [15]; the complete scan increases accuracy, but increases computing requirements and time. Pintore and Gobbetti [23] proposed a technique to create floor plans and 3D models using an Android device camera, leveraging sensor data and statistical techniques. Chen et al. [7] introduced an augmented reality system utilizing the Microsoft Hololens for indoor layout assessment, addressing intuitive evaluation and efficiency challenges. In our approach, we begin with a triangle mesh from a HoloLens 2, using its Spatial Mapping software [18], which has been surveyed by Weinmann et al. [27]. Data collection. Floor plan computations Early methods [1,3,22,28] relied on image processing techniques, such as histograms and plane fitting, to create floor plans from 3D data. While [22] creates a floor plan by detecting vertical planes in a 3D point cloud, [3] uses planar structure extraction to create floor plans. These techniques rely on heuristics and were prone to failure due to noise in the data. Floor plan computations There has been much progress in floor plan computation using graphical models [4,9,12]. Such models [10] are also used to recover layouts and floor plans from crowd-sourced images and location data. One interactive tool [16] creates desirable floorplans by conforming to design constraints. Pintore et al. [24] characterizes several available input sources (including the triangle meshes that we use) and output models and discusses the main elements of the reconstruction pipeline. It also identifies several systems for producing floor plans, including FloorNet [15], and Floor-SP [6]. Monszpart et al. [19] introduced an algorithm that exploits the observation that distant walls are generally parallel to identify dominant wall directions using k-means. Our approach also utilizes k-means, but does so to identify walls in all directions, not just the dominant ones. Cai et al. [5] uses geometric priors, including point density, indoor area recognition, and normal information, to reconstruct floorplans from raw point clouds. In contrast to Arikan et al. [2], which employed a greedy algorithm to find plane normal directions and fit planes to points with help from user interaction, our approach is automatic. It also differs from the work of [20], which focuses on removing clutter and partitioning the interior into a 3D cell complex; our method specifically divides the building into separate walls. Our work is related to [22] and [26]. In [22], floor plan generation starts with a laser range data point cloud, followed by floor and ceiling detection using a height histogram. The remaining points are projected onto a ground plane, where a density histogram and Hough transform are applied to generate the line segments that form a floor plan. In projecting to 2D, their method risks losing information that may be useful for creating 3D models or detailed floor plans. Similarly, [26] utilizes a histogram-based approach to detect ceilings and floors. Their method involves identifying taller wall segments to create a 2D histogram, and then employing heuristics based on histogram point density to compute the floor plan. Our approach differs from [22] and [26] by aligning the mesh with global coordinate axes and not relying on laser data or a point cloud. Working primarily with 3D data throughout the pipeline, it benefits from enhanced information and generates both a 3D model and a floor plan. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Related Work

Supernormal Stimuli: Are We Amusing Ourselves to Death?

অতিপ্রাকৃতিক উদ্দীপনা: আমরা কি মৃত্যুর জন্য মজা করছি?

Übernatürliche Reize: Amüsieren wir uns zu Tode?

Estímulos sobrenaturales: ¿nos estamos divirtiendo hasta la muerte?

Stimuli supranormaux : sommes-nous en train de nous amuser à mourir ?

अलौकिक उत्तेजनाएँ: क्या हम अपने आप को मौत तक मनोरंजन कर रहे हैं?

超常刺激：私たちは死ぬほど楽しんでいるのか？

초자연적 자극: 우리는 죽을 때까지 즐겁게 지내고 있나요?

Estímulos supranormais: estamos nos divertindo até a morte?

Сверхъестественные стимулы: развлекаемся ли мы до смерти?

Olağanüstü Uyarıcılar: Kendimizi Ölesiye Eğlendiriyor muyuz?

Kích thích siêu thường: Chúng ta có đang vui vẻ đến chết không?

超常刺激：我们是不是正在自娱自乐？

How to Create a Steel Base Plate Design Web App Using Python and Viktor

পাইথন এবং ভিক্টর ব্যবহার করে কীভাবে একটি স্টিল বেস প্লেট ডিজাইন ওয়েব অ্যাপ তৈরি করবেন

So erstellen Sie mit Python und Viktor eine Web-App zum Entwerfen von Stahlgrundplatten

Cómo crear una aplicación web de diseño de placa base de acero usando Python y Viktor

Comment créer une application Web de conception de plaques de base en acier à l'aide de Python et Viktor

पायथन और विक्टर का उपयोग करके स्टील बेस प्लेट डिज़ाइन वेब ऐप कैसे बनाएं

Python と Viktor を使用してスチールベースプレート設計 Web アプリを作成する方法

Python과 Viktor를 사용하여 강철 베이스 플레이트 디자인 웹 앱을 만드는 방법

Como criar um aplicativo Web de design de placa de base de aço usando Python e Viktor

Как создать веб-приложение для проектирования стальной опорной пластины с помощью Python и Viktor

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The USA and Russia could unite to stall the growth of Bitcoin with a series of bans and legal actions. But this might birth a group of ardent cypherpunks from those frustrated Bitcoiners. These could hit back by counter-banning Russia and the USA! Bitcoin at the Mercy of USA, Russia With the heated war in Ukraine, Russia and the United States of America are not best buds. But they agree on one thing. Bitcoin is eating into the plans of their fiat currencies. Why? Because with Bitcoin around, moves like QE in either country could facilitate the flow of rubles or dollars into Bitcoin, an international asset, and not state-bound assets like housing. This thus undermines the traditional economic plans on the ground. Also, Bitcoiners are pretty loud in their criticisms and their voices shift some volatility from BTC into dollars and rubles. Therefore, to curb this wild beast and its wild bull swings: 1. The FBI has warned American citizens against using Unregistered Cryptocurrency (Bitcoin) Money Transmitting Services. 2. Russia will impose strict restrictions on crypto assets such as Bitcoin to strengthen the ruble's dominance. And they are not bluffing. US authorities have already made some arrests, nabbing the founders of the crypto wallet called Sparrow in line with the aforementioned warning. If Only They Just Accepted Bitcoin In a world where more numbers of powerful people are willing to give away their power like Nelson Mandela, Bitcoin would be adopted by the USA and Russia and the wars would likely end. Because fighting could destroy BTC servers, sending trillions of dollars worth of coins into a black hole in cyberspace. Not a good idea. Dollars and Rubles can always be printed back. Gold cannot be destroyed by missiles. BTCs vulnerability really tames the itch to launch missiles. I am not sure what the people in the military-industrial complex would do once their jobs and funds dry up, but they could take a leaf from the people who worked on horse-drawn carriages. Alas, polite calls like mine, for governments to come join the BTC bandwagon and finally pollute the air with unifying ASICS instead of disunifying weapons (nobody asks how much carbon is emitted by weapons but it is HUGE), are to no avail. So one day, Bitcoiners and their currency (yes. It is theirs) shall be pushed to the wall. And they will act. The Rise of Anarcho Bitcoiners A child can punish its parents. The geek shall inherit the earth. The code is mightier than the sword. In terms of power projection, Bitcoin is akin to the child while Russia and the USA are the parents/elders. But if that child is like Matilda, it is way smarter than its gullible parents. So it can punish them. If the US government were to persecute one too many Bitcoiners in the name of KYC, fully licensed P2P trading and confiscation of bitcoins without explicit proof of breaking some law (after due process), Bitcoiners could lock the government out! Certainly, the FBI’s warning comes on the back of stopping money laundering services from continuing a lucrative trade of moving their money through crypto, hence "cleaning it." Spikes in price value could especially make good cover for such illegality. As when the money spikes in value, the criminals could attribute their gains to crypto. However, the government could also partake in the illegality of trying to meddle with a currency that is not its own. Doesn't sound illegal? Well, it’s not exactly a good idea to meddle with a cypherpunk digital tool. They could come back to bite you. Say Bitcoin continues rising in value, so the itch to enact something like Executive Order 6102. Maybe, more modestly, the USA and Russia acquire some BTC to hodl yet they persecute Bitcoiners still. Worse, the USA does a successful 51% attack. To pay back some of that ballooning debt. Andreas Antonopolous has explained well before how worthless it would be. They would change 1 block, then the community would sync their miners and kick them out of the chain. But here's what is even more interesting. The community could decide to hard fork Bitcoin and confiscate back all the coins that were stolen from them!! What? Yes. You heard that right. A bunch of nerds with laptops and smartphones in their hands could take back their crypto taken away by the US government, Russian government, or any government. Simply by answering an anonymized online poll. If a node-holding majority consent, boom. The mother of all hard forks can be effected. Like a nuclear holocaust, except it is digital and shall only be targeting .gov wallets. Like the collapse of the Soviet Union, no single bullet would be fired to effect the collapse of government hegemony enacted as confiscation schemes when it comes to crypto. See, it would be the rise of Anarcho Bitcoiners. Call them AnBis. Liks Ancaps and Ancoms, except they have real power in their smartphones. The government does not have to collapse. It would not be an anti-statist move. “Its right to hold our crypto simply has to be undermined a little, or aligned as per our rules”, would say Satoshi Nakamoto if he ever resurfaced. In 2009, something miraculous happened. Satoshi Nakamoto created the first decentralized, market-owned currency that no government could control or stop. Especially after all the other cryptocurrencies out there also emerged on the scene, which act like a shield around BTC. Also now, the hash rate really cannot possibly be overtaken. But if it is overtaken, that too will be good. It will help annoy and amalgamate Bitcoiners into a solid unit. As Andreas explains, it will be quite good. Secret papers can be intercepted. Secret messages and signatures across the web, a waste of time. If the hate for surveillance and oppression has been well cultivated in AnBis by nation-states, one press of a button, in sync, and one or two bad parents could be officially locked out of the respective crypto community's cryptocurrency. Because it is theirs. Always was, always will be decentralized. If it gets centralized, it stops being crypto. I mean, there won't be anything cryptic about it anymore. The USA and Russia could unite to stall the growth of Bitcoin with a series of bans and legal actions. But this might birth a group of ardent cypherpunks from those frustrated Bitcoiners. These could hit back by counter-banning Russia and the USA! Bitcoin at the Mercy of USA, Russia With the heated war in Ukraine, Russia and the United States of America are not best buds. But they agree on one thing. Bitcoin is eating into the plans of their fiat currencies. Why? Because with Bitcoin around, moves like QE in either country could facilitate the flow of rubles or dollars into Bitcoin, an international asset, and not state-bound assets like housing. This thus undermines the traditional economic plans on the ground. Also, Bitcoiners are pretty loud in their criticisms and their voices shift some volatility from BTC into dollars and rubles. Therefore, to curb this wild beast and its wild bull swings: 1. The FBI has warned American citizens against using Unregistered Cryptocurrency (Bitcoin) Money Transmitting Services. warned 2. Russia will impose strict restrictions on crypto assets such as Bitcoin to strengthen the ruble's dominance. will impose And they are not bluffing. US authorities have already made some arrests, nabbing the founders of the crypto wallet called Sparrow in line with the aforementioned warning. nabbing the founders of the crypto wallet called Sparrow If Only They Just Accepted Bitcoin In a world where more numbers of powerful people are willing to give away their power like Nelson Mandela, Bitcoin would be adopted by the USA and Russia and the wars would likely end. Because fighting could destroy BTC servers, sending trillions of dollars worth of coins into a black hole in cyberspace. Not a good idea. Dollars and Rubles can always be printed back. Gold cannot be destroyed by missiles. BTCs vulnerability really tames the itch to launch missiles. I am not sure what the people in the military-industrial complex would do once their jobs and funds dry up, but they could take a leaf from the people who worked on horse-drawn carriages. Alas, polite calls like mine , for governments to come join the BTC bandwagon and finally pollute the air with unifying ASICS instead of disunifying weapons (nobody asks how much carbon is emitted by weapons but it is HUGE), are to no avail. like mine So one day, Bitcoiners and their currency (yes. It is theirs) shall be pushed to the wall. And they will act. The Rise of Anarcho Bitcoiners A child can punish its parents . A child can punish its parents The geek shall inherit the earth. The geek shall inherit the earth. The code is mightier than the sword. The code is mightier than the sword. In terms of power projection, Bitcoin is akin to the child while Russia and the USA are the parents/elders. But if that child is like Matilda, it is way smarter than its gullible parents. So it can punish them. If the US government were to persecute one too many Bitcoiners in the name of KYC, fully licensed P2P trading and confiscation of bitcoins without explicit proof of breaking some law (after due process), Bitcoiners could lock the government out! Certainly, the FBI’s warning comes on the back of stopping money laundering services from continuing a lucrative trade of moving their money through crypto, hence "cleaning it." Spikes in price value could especially make good cover for such illegality. As when the money spikes in value, the criminals could attribute their gains to crypto. However, the government could also partake in the illegality of trying to meddle with a currency that is not its own. Doesn't sound illegal? Well, it’s not exactly a good idea to meddle with a cypherpunk digital tool. They could come back to bite you. Say Bitcoin continues rising in value, so the itch to enact something like Executive Order 6102. Maybe, more modestly, the USA and Russia acquire some BTC to hodl yet they persecute Bitcoiners still. Worse, the USA does a successful 51% attack. To pay back some of that ballooning debt. Andreas Antonopolous has explained well before how worthless it would be. They would change 1 block, then the community would sync their miners and kick them out of the chain. But here's what is even more interesting. The community could decide to hard fork Bitcoin and confiscate back all the coins that were stolen from them!! explained well before What? Yes. You heard that right. A bunch of nerds with laptops and smartphones in their hands could take back their crypto taken away by the US government, Russian government, or any government. Simply by answering an anonymized online poll. If a node-holding majority consent, boom. The mother of all hard forks can be effected. Like a nuclear holocaust, except it is digital and shall only be targeting .gov wallets. Like the collapse of the Soviet Union, no single bullet would be fired to effect the collapse of government hegemony enacted as confiscation schemes when it comes to crypto. See, it would be the rise of Anarcho Bitcoiners. Call them AnBis. Liks Ancaps and Ancoms, except they have real power in their smartphones. The government does not have to collapse. It would not be an anti-statist move. “Its right to hold our crypto simply has to be undermined a little, or aligned as per our rules”, would say Satoshi Nakamoto if he ever resurfaced. our our In 2009, something miraculous happened. Satoshi Nakamoto created the first decentralized, market-owned currency that no government could control or stop. Especially after all the other cryptocurrencies out there also emerged on the scene, which act like a shield around BTC. Also now, the hash rate really cannot possibly be overtaken. But if it is overtaken, that too will be good. It will help annoy and amalgamate Bitcoiners into a solid unit. As Andreas explains, it will be quite good. Secret papers can be intercepted. Secret messages and signatures across the web, a waste of time. If the hate for surveillance and oppression has been well cultivated in AnBis by nation-states, one press of a button, in sync, and one or two bad parents could be officially locked out of the respective crypto community's cryptocurrency. Because it is theirs. Always was, always will be decentralized. If it gets centralized, it stops being crypto. I mean, there won't be anything cryptic about it anymore.

USA, Russia, and Very Frustrated Bitcoiners

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