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As the crypto landscape buzzes with activity, Bitcoin (BTC) surpasses the $70,000 mark, energized by the approaching halving event that historically signifies bullish cycles. Ethereum (ETH) also sees dramatic action, with significant whale transactions hinting at potential strategic moves amid market volatility. Amid these established giants, Furrever Token (FURR) shines in its presale phase, amassing substantial funds and demonstrating robust interest in emerging crypto opportunities. These events showcase the vibrant dynamics of the cryptocurrency market, where major movements by established tokens and promising starts by new entrants captivate investors’ attention and speculation. Bitcoin (BTC) Trading Over the $70,000 Mark as Halving Approaches At the time of writing, Bitcoin is trading at $70,396.30. The dynamic cryptocurrency has seen significant fluctuations over the past year, with its highest point reaching $73,835.57 on March 14, 2024. This peak contrasts starkly with its lowest value in the same period, which was $24,780.17 on June 15, 2023. Over the past year, Bitcoin has shown a remarkable increase of 132.99%, demonstrating its volatile yet upward trend in the market. Since its inception in January 2009, Bitcoin has revolutionized the concept of money and financial transactions, evolving from humble beginnings to a dominant force in global financial markets with a current market capitalization of $1.38 trillion. This shift underscores Bitcoin's growing popularity as an alternative to traditional fiat currencies like the U.S. dollar, which are subject to inflation and can depreciate over time. Bitcoin's valuation is driven purely by market supply and demand, without the influence of tangible assets, earnings, revenue, or cash flow. New bitcoins are introduced into circulation through mining, where miners are rewarded with bitcoins for verifying and adding new transaction blocks to the blockchain. The total supply of Bitcoin is finite, capped at 21 million BTC, which fundamentally influences its price dynamics. The first recorded transaction that set a price for Bitcoin occurred in late 2009 when 5,050 BTC were exchanged for $5.02 via PayPal, valuing each bitcoin at approximately $0.00099. This event marked the beginning of its monetary value, starting at just about one-tenth of a cent. Bitcoin undergoes a process known as "halving," where the reward for mining new blocks is halved approximately every four years, thus reducing the rate at which new bitcoins are generated. This mechanism is designed to control inflation and diminish the supply over time, theoretically supporting the price of Bitcoin. The next halving is anticipated in April 2024, where the mining reward will decrease from 6.25 BTC to 3.125 BTC. Although halvings reduce the supply of new bitcoins, their direct impact on Bitcoin’s price isn’t immediate or guaranteed. Historically, Bitcoin prices tend to hit a cyclical bottom about a year before a halving and rise steadily afterward for more than a year. This pattern suggests that while halvings are not a direct catalyst for price increases, they contribute to bullish trends over time due to decreased supply. As Bitcoin continues to navigate through these cycles of highs and lows, it remains a focal point for investors and analysts who speculate on its future amidst the evolving financial landscape. Ethereum (ETH) Whale Makes Estimated Profit of $68.5 Million In the ever-volatile cryptocurrency landscape, Ethereum (ETH) has once again been at the center of a tumultuous trading day. The leading altcoin experienced a sharp decline, dropping 3.1% to a low of $3,411, only to make a surprising rebound to a peak of $3,615. This rapid fluctuation occurred within a span of 24 hours, with the price eventually settling at $3,512. Despite these dramatic swings, the day closed with Ethereum's value relatively unchanged over the 24-hour period. Amidst this whirlwind of market activity, the actions of significant cryptocurrency investors, commonly known as crypto whales, have drawn considerable attention. According to a report by Spot On Chain, a notable transaction was observed where a whale, identified as "0x347," deposited an impressive 9,000 ETH into Binance. This deposit, valued at $32 million, represents the largest single transfer of Ethereum by this investor to date. Following this transaction, "0x347" now holds a total of 29,738 ETH, estimated to be worth around $106 million, realizing an estimated profit of $68.5 million. Concurrently, data from Lookonchain highlighted another whale engaging in substantial acquisitions, purchasing 10,309 ETH for approximately $35.82 million during the market's low point. This investor also executed a significant buy prior to a notable increase in Bitcoin’s price on April 8. These significant whale activities underscore a divide in market sentiment concerning Ethereum's immediate future. While some market participants interpret the recent price recovery as an indication of a bullish trend continuation, others are cautious, suggesting the rebound could be a temporary technical correction. As Ethereum continues to be a central topic of discussion and analysis within the crypto community, the contrasting strategies of key market players provide a clear picture of the speculative nature inherent in the cryptocurrency markets. Furrever Token (FURR) Secures Over $770,000 at Presale Furrever Token (FURR) is swiftly establishing itself as a prominent player in the cryptocurrency market, captivating a broad audience with its significant growth potential and compelling investor advantages. Its remarkable traction in presale events has been a critical factor in its rising appeal, attracting hefty investments in each phase. Currently, in its sixth stage of presale, Furrever Token (FURR) has successfully amassed over $770,000, underscoring its increasing popularity and solid support within the cryptocurrency community. Offering investors the potential for up to 15X returns on each presale stage, Furrever Token (FURR) emerges as a lucrative option for those seeking profitable opportunities within the crypto space. With a current price of $0.00048, FURR provides an excellent chance for investors to participate early in its anticipated growth trajectory. The strength of Furrever Token’s appeal is further enhanced by the vibrant community behind it. With more than 4,300 active members on its official Telegram channel, Furrever Token (FURR) thrives as a dynamic center for engaging discussions, collaborative efforts, and consistent updates, enriching the investment experience for all participants. Looking ahead, the strategic roadmap and development plans of Furrever Token (FURR) demonstrate its determined commitment to establishing a substantial foothold in the meme coin market. Plans to introduce innovative features, establish strategic partnerships, and launch targeted marketing initiatives are set to broaden its adoption and increase its market value significantly. In summary, Furrever Token (FURR) stands out as an exceptionally attractive investment opportunity, backed by solid fundamentals, a robust and active community, and a promising outlook for extensive growth. As Furrever Token (FURR) continues to advance towards its strategic goals, it is well-positioned to yield significant returns for early investors who capitalize on its potential. **Secure the Most Exclusive Presale Opportunity of 2024 Today!
\ Furrever Token Official Website  |   Visit Furrever Token PresaleJoin Official Telegram Group       |   Follow Official X Account As the crypto landscape buzzes with activity, Bitcoin ( BTC ) surpasses the $70,000 mark, energized by the approaching halving event that historically signifies bullish cycles. Ethereum ( ETH ) also sees dramatic action, with significant whale transactions hinting at potential strategic moves amid market volatility. Amid these established giants, Furrever Token (FURR) shines in its presale phase, amassing substantial funds and demonstrating robust interest in emerging crypto opportunities. These events showcase the vibrant dynamics of the cryptocurrency market, where major movements by established tokens and promising starts by new entrants captivate investors’ attention and speculation. BTC ETH Furrever Token (FURR) Furrever Token (FURR) Bitcoin (BTC) Trading Over the $70,000 Mark as Halving Approaches At the time of writing, Bitcoin is trading at $70,396.30. The dynamic cryptocurrency has seen significant fluctuations over the past year, with its highest point reaching $73,835.57 on March 14, 2024. This peak contrasts starkly with its lowest value in the same period, which was $24,780.17 on June 15, 2023. Over the past year, Bitcoin has shown a remarkable increase of 132.99%, demonstrating its volatile yet upward trend in the market. Since its inception in January 2009, Bitcoin has revolutionized the concept of money and financial transactions, evolving from humble beginnings to a dominant force in global financial markets with a current market capitalization of $1.38 trillion. This shift underscores Bitcoin's growing popularity as an alternative to traditional fiat currencies like the U.S. dollar, which are subject to inflation and can depreciate over time. Bitcoin's valuation is driven purely by market supply and demand, without the influence of tangible assets, earnings, revenue, or cash flow. New bitcoins are introduced into circulation through mining, where miners are rewarded with bitcoins for verifying and adding new transaction blocks to the blockchain. The total supply of Bitcoin is finite, capped at 21 million BTC, which fundamentally influences its price dynamics. The first recorded transaction that set a price for Bitcoin occurred in late 2009 when 5,050 BTC were exchanged for $5.02 via PayPal, valuing each bitcoin at approximately $0.00099. This event marked the beginning of its monetary value, starting at just about one-tenth of a cent. Bitcoin undergoes a process known as "halving," where the reward for mining new blocks is halved approximately every four years, thus reducing the rate at which new bitcoins are generated. This mechanism is designed to control inflation and diminish the supply over time, theoretically supporting the price of Bitcoin. The next halving is anticipated in April 2024, where the mining reward will decrease from 6.25 BTC to 3.125 BTC. Although halvings reduce the supply of new bitcoins, their direct impact on Bitcoin’s price isn’t immediate or guaranteed. Historically, Bitcoin prices tend to hit a cyclical bottom about a year before a halving and rise steadily afterward for more than a year. This pattern suggests that while halvings are not a direct catalyst for price increases, they contribute to bullish trends over time due to decreased supply. As Bitcoin continues to navigate through these cycles of highs and lows, it remains a focal point for investors and analysts who speculate on its future amidst the evolving financial landscape. Ethereum (ETH) Whale Makes Estimated Profit of $68.5 Million In the ever-volatile cryptocurrency landscape, Ethereum (ETH) has once again been at the center of a tumultuous trading day. The leading altcoin experienced a sharp decline, dropping 3.1% to a low of $3,411, only to make a surprising rebound to a peak of $3,615. This rapid fluctuation occurred within a span of 24 hours, with the price eventually settling at $3,512. Despite these dramatic swings, the day closed with Ethereum's value relatively unchanged over the 24-hour period. Amidst this whirlwind of market activity, the actions of significant cryptocurrency investors, commonly known as crypto whales, have drawn considerable attention. According to a report by Spot On Chain, a notable transaction was observed where a whale, identified as "0x347," deposited an impressive 9,000 ETH into Binance. This deposit, valued at $32 million, represents the largest single transfer of Ethereum by this investor to date. Following this transaction, "0x347" now holds a total of 29,738 ETH, estimated to be worth around $106 million, realizing an estimated profit of $68.5 million. Concurrently, data from Lookonchain highlighted another whale engaging in substantial acquisitions, purchasing 10,309 ETH for approximately $35.82 million during the market's low point. This investor also executed a significant buy prior to a notable increase in Bitcoin’s price on April 8. These significant whale activities underscore a divide in market sentiment concerning Ethereum's immediate future. While some market participants interpret the recent price recovery as an indication of a bullish trend continuation, others are cautious, suggesting the rebound could be a temporary technical correction. As Ethereum continues to be a central topic of discussion and analysis within the crypto community, the contrasting strategies of key market players provide a clear picture of the speculative nature inherent in the cryptocurrency markets. Furrever Token (FURR) Secures Over $770,000 at Presale Furrever Token (FURR) is swiftly establishing itself as a prominent player in the cryptocurrency market, captivating a broad audience with its significant growth potential and compelling investor advantages. Its remarkable traction in presale events has been a critical factor in its rising appeal, attracting hefty investments in each phase. Currently, in its sixth stage of presale, Furrever Token (FURR) has successfully amassed over $770,000, underscoring its increasing popularity and solid support within the cryptocurrency community. Furrever Token (FURR) Furrever Token (FURR) Offering investors the potential for up to 15X returns on each presale stage, Furrever Token (FURR) emerges as a lucrative option for those seeking profitable opportunities within the crypto space. With a current price of $0.00048, FURR provides an excellent chance for investors to participate early in its anticipated growth trajectory. The strength of Furrever Token’s appeal is further enhanced by the vibrant community behind it. With more than 4,300 active members on its official Telegram channel, Furrever Token (FURR) thrives as a dynamic center for engaging discussions, collaborative efforts, and consistent updates, enriching the investment experience for all participants. Looking ahead, the strategic roadmap and development plans of Furrever Token (FURR) demonstrate its determined commitment to establishing a substantial foothold in the meme coin market. Plans to introduce innovative features, establish strategic partnerships, and launch targeted marketing initiatives are set to broaden its adoption and increase its market value significantly. In summary, Furrever Token (FURR) stands out as an exceptionally attractive investment opportunity, backed by solid fundamentals, a robust and active community, and a promising outlook for extensive growth. As Furrever Token (FURR) continues to advance towards its strategic goals, it is well-positioned to yield significant returns for early investors who capitalize on its potential. **Secure the Most Exclusive Presale Opportunity of 2024 Today!
\ Furrever Token Official Website | Visit Furrever Token Presale Join Official Telegram Group | Follow Official X Account Furrever Token Official Website Furrever Token Official Website Visit Furrever Token Presale Visit Furrever Token Presale Join Official Telegram Group Join Official Telegram Group Follow Official X Account Follow Official X Account

Crypto Profit Potential: BTC Halving, ETH Whale Activity, and Furrever Token $770K Presale

ক্রিপ্টো লাভের সম্ভাবনা: বিটিসি হালভিং, ইটিএইচ হোয়েল অ্যাক্টিভিটি এবং ফুরেভার টোকেন $770K প্রিসেল

Krypto-Gewinnpotenzial: BTC-Halbierung, ETH-Wal-Aktivität und Furrever-Token-Vorverkauf im Wert von 770.000 USD

Potencial de ganancias criptográficas: reducción a la mitad de BTC, actividad de ballenas de ETH y preventa de $770 mil del token Furrever

Potentiel de profit cryptographique : réduction de moitié du BTC, activité des baleines ETH et prévente de 770 000 $ du jeton Furrever

क्रिप्टो लाभ क्षमता: बीटीसी हाफिंग, ईटीएच व्हेल गतिविधि, और फुर्रेवर टोकन $ 770K प्रीसेल

暗号通貨の利益の可能性: BTC の半減期、ETH クジラの活動、Furrever トークンの 77 万ドルのプレセール

암호화폐 수익 잠재력: BTC 반감기, ETH 고래 활동 및 Furrever 토큰 $770K 사전 판매

Potencial de lucro criptográfico: redução pela metade do BTC, atividade de baleias ETH e pré-venda de token Furrever de US$ 770 mil

Потенциал прибыли от криптовалюты: сокращение вдвое BTC, активность ETH Whale и токен Furrever, предварительная продажа на сумму 770 тысяч долларов

Kripto Kâr Potansiyeli: BTC Yarılanması, ETH Balina Faaliyeti ve Furrever Token 770 Bin Dolarlık Ön Satış

Tiềm năng lợi nhuận từ tiền điện tử: Giảm một nửa BTC, Hoạt động của cá voi ETH và Mã thông báo Furrever $ 770K Bán trước

加密货币盈利潜力：BTC 减半、ETH 鲸鱼活动和 Furrever Token 77 万美元预售

Ever feel like your phone knows you better than your best friend? Does your newsfeed seem less like a window to the world and more like some weird fun-house mirror? Mine too. They may even be a little too good. And they’re only going to get better. But here's the secret: you can opt-out. A growing cadre of tech gurus, renegade artists and unstoppable activists are throwing a wrench in the system. Big Tech's algorithms are good. There was no escape. Breathe, eat, and sleep. That was all there was to life in the machine." - Aldous Huxley, Brave New World is not a word commonly used by the masses. The idea of independence feels like a betrayal. Talking about “freedom” sets off triggers. But autonomy is a dynamic force. Today it’s making a global comeback.  If you look closely you can spot it in  tech, art and activism. Autonomy It adapts and evolves with our ever-changing world. It’s a mindset that says “ ” I’m not going to be controlled by outside forces and external interests. Here’s some non-breaking news. The traditional work compact, in which employees traded hard work and loyalty in exchange for job security is shattered. Thought so. How many times have you gone job-hunting in your life? But consider this. boasts . He remixed the for the most recent Super Bowl in Las Vegas. DJ Steve Aoki more than 3 billion music streams NFL on CBS theme Here’s what he said at Gala Music: “In the 10 years I’ve been making music … six albums, and all those advances … in one drop last year in NFTs, I made more money.” - Steve Aoki Want to opt-out and join the revolution? Connect with techs and developers building a . Why give your personal information to some faceless corporation? web where you are in control Learn from artists minting their creations as . For the first time, it’s possible to cut out the middleman and take ownership of your work. Discover how activists bypass the financial gatekeepers. Now you can use crowdfunding to fuel your movements directly from the people. NFTs Keep reading to see how the coolest tools and platforms are putting you back in control. Let’s get this show on the road. Get ready for an empowered future. The Rise of Autonomy Early in my entrepreneurial journey, a millionaire mentor gave me some advice on autonomy. He said, “ How often are your decisions based on your own values and goals? Norm whoever controls your time controls your income. I suggest it be you who does it.” The concept drove my parents. “Get an education. Get a good job. Retire there.” In their day people often stayed with the same company for their entire career. They were even called “a company man” or “company gal.” Life goals consisted of building deep connections with colleagues and a strong sense of pride in your work. company loyalty https://www.youtube.com/watch?v=a4F9u8Z84YI&embedable=true Back then companies invested in their employees through training programs and full benefits. When I started in sales my big tech employer had a “no-layoff” policy (it ended while I was there). The global creator economy is projected to reach $104.2 billion by 2028, reflecting the increasing number of individuals leveraging online platforms to express themselves creatively and generate income independently. - Creator Economy Over the course of several decades, cracks began to appear in the model. Tech advancements led to increased job insecurity. One of the first job killers I remember was voicemail. Yep. It eliminated the need for receptionists to answer phones, take messages, and write them down on those little pink slips for salespeople returning to the office. Wage freezes and layoffs increased and the "good job" became less common. As the internet took hold in the 90’s globalization became sexy. The social contract between employer and employee collapsed, Companies chased cheap labor and maximum profits for shareholders -  wherever they could find them. But in public, they sought to maintain the illusion of the Golden Age. Their corporate PR departments were quite busy. Today despite the perception of freedom your autonomy is limited. own social media networks. They control the search engines and video production platforms. Their TOS and centralization secure their dominance. Maximize profits for the few. Sound familiar? This traditional model locks out millions of creators worldwide. Big tech giants “The goal of most people for the last half century was to get the good job that paid well with benefits..” - Anthony Spadafore, Director Pathfinders Autonomy Success Story My friend Sarah is a graphic designer with six years of experience. During the COVID-19 pandemic, she lost her job at a big ad agency. After chasing hundreds of job posts she decided to be her own boss. Now she works remotely as a freelance designer. Instead of being assigned clients, she sets her own schedule. Her boring projects got replaced by gigs that aligned with her interests. She earns more on her own than she did going into the office. And now she finds personal meaning in her work. The Back-to-Office Push and the Autonomy Challenge A remote work revolution is sweeping around the world. Set free from the confines of the physical office, employees are discovering a newfound sense of autonomy. Do you thrive in an environment tailored to your work style? Can you manage your schedule and responsibilities better with more flexibility? For most of us, the answer is YES! This newfound control increases productivity and boosts morale. Many companies are actively encouraging, some even demanding, a return to “in-office” work. Their  push-back against remote work poses a challenge to employee autonomy. But big business CEOs, government bureaucrats, and even Elon Musk don’t like it. Here's why: :  Remote work helps employees manage their work schedules around personal needs and responsibilities. If your productivity is solid and profits are up why poke the bear? A forced return to the office disrupts this balance and increases stress. Goodbye Flexibility, Hello Frustration : It’s the time and money stupid. We all have 24 hours in our day. Having to commute to the office, pay to park, and upgrade your wardrobe eats into personal time and monthly budgets. The impact on working parents or people with long commutes is huge. The Commute Crisis : Most employees I speak with enjoy the flexibility and convenience of working from home. Some never had this opportunity before COVID-19. Now they realize they can do their job more conveniently. Forcing them back into the office feels like a slap in the face. Morale Killer I think companies that are inflexible with remote work options will lose valuable talent. The job market is competitive. The creator economy allows people like my friend Sarah to level up their skills and create jobs for themselves. The pull of autonomy is strong. Collaboration: Tech, Art and Activism Disruption accelerates when tech, art and activism come together. From interactive installations to digital art platforms, technology has enabled artists to reach wider audiences. You can also spark important conversations about social issues and reform. Techpreneurs are launching tools that empower artists and activists. Modern artists are wielding technology to create art that raises awareness for social causes. Activist groups are reaching a global audience with every social media post. This is the future we're building. A world where creative expression meets social justice. And the power of technology is at the core. - Norm Bond Tech Creators Build Worlds Beyond Reality Tech developers and creators are building alternative platforms to traditional activities. Can you imagine stepping into a painting instead of just looking at it in a museum? You can with . Van Gogh Exhibit: The Immersive Experience The exhibition has been ! This blend of art and cutting-edge technology ranks among the 12 best immersive experiences in the world by CNN. touring since 2017 with +5,000,000 visitors Take a walk alongside Van Gogh during a visually rich journey. It’s a 20,000 square feet sound and light spectacular. The two-story projections leave visitors in awe. You discover the inspiration behind eight of Van Gogh’s iconic works. is one of my favorites. The Starry Night Artists Go Indie with Tech Blockchain technology provides innovative tools for artists to monetize their work through smart contracts, NFTs and crypto. This strategy allows creators to sell their work directly to collectors . Artists are creating digital art and minting it as NFTs. without the need for traditional galleries or intermediaries London's glitchy artist , averaging over $23,000 per sale and topping the charts. His work captivates collectors and redefines the limits of digital art. His signature style is a mesmerizing blend of flashing visuals and dark themes (his website includes a flashing media warning). XCOPY is minting millions with his dystopian NFTs Here’s the thing. XCOPY’s work isn't just visually arresting. Sure there are lots of flashy NFTs and dystopian styles. But his NFT’s spark conversations. He challenges the status quo in the art world. He’s using his creativity to provide a unique commentary on our digital age. . Is a sign of a future where artists control their destinies, or a blip in a system that still favors the powerful? Don’t just wait to see what happens. Take action on YOUR dream. Telling your own story and controlling distribution is an expression of autonomy XCOPY How Activists Use Tech to Win The murder of George Floyd was recorded and spread virally across social media. It ignited Black Lives Matter like a rocket. This wasn't just a tragic event. It was a spark that . The case study reveals how activists can use social media to amplify their message. Consider this. lit the fuse of a movement already simmering online In the month Floyd’s murder, was tweeted more than 400 thousand times. But in the month , the . before #BlackLivesMatter after hashtag spiked and was shared more than 100 million times Nordstrom posted #BlackLivesMatter to Facebook and received more than 5-million likes. The from the brand. engagement was nearly 90 times the level of typical posts Colleges and universities joined in. More than 107 shared . Healthcare systems, nonprofits, corporations, and sports teams also climbed on the #BlackLivesMatter juggernaut. #BlackLivesMatter The entire internet was ablaze with social posts, videos, CEO statements, and brand platitudes to support this trending social movement. It shook the system. The Future of Autonomy The dream of dumping the 9-to-5 and living life on your terms is more than just a hashtag. It’s a call to action. One fueled by a deep desire for autonomy – the freedom to create, work, and live by your own rules. Don’t let it just be a pipe dream. The total value locked (TVL) in DeFi protocols surpassed $300 billion in 2023, demonstrating the growing interest in alternative financial systems that promote individual control over assets and transactions. - DeFi Pulse Revolutionary art movements, tech-driven innovation, and modern activism are converging. This shift provides a path to an exciting future. Break free from traditional structures. Now is your chance to build something empowering. It's a world where you can set the pace, call the shots, and shape your days around your passions. There are lots of challenges ahead. Working together is often necessary for maximum impact. Finding the right balance between maintaining individual artistic vision and working within a collective - can be difficult. But let's keep it real. Breaking free from traditional structures means leaving the financial security that these systems can provide. Artists, activists and techs still have bills to pay. Do you have alternative means of funding your work in place? With the rise of new technologies, there’s a need to ensure that ethical practices are followed. You need to address concerns regarding data privacy. There are questions regarding intellectual property rights to be sorted. The environmental impact of tech-driven projects like crypto and NFTs are a dealbreaker for some people. These challenges will have to be met to increase adoption and scale. They require innovative solutions, resilience and funding. To build a more equitable and creative future demands continuous learning and adaptation. Conclusion We’ve covered a lot. You know the journey toward autonomy is an exciting yet challenging one. This trend in tech, art and activism is attracting more people looking for a way out. The digital age provides new tools to take control of your life. It has never been easier to express yourself and reach a target audience. Artists can leverage blockchain technology. Tech creators can build alternative platforms. Activists can fuel movements without traditional limits. Working collectively can exponentially grow results for both individuals and organizations. But the road ahead is not all roses and sunshine. Challenges include navigating concerns about the environment. Ethical considerations riddle the increased use of AI and ML. Building tools that engage marginalized communities is critical in a market-based economy. Long-established traditional systems are not going to just roll over and embrace change. So take the long view if you want to embrace this autonomy trend. If you’re an artist, technologist or activist it’s time to redefine the possible. I’m in. Let’s create and cause the change you want to see. right here on HN to connect. Visit my profile

Breaking Rules: Autonomy in Tech, Art and Activism

নিয়ম ভাঙা: প্রযুক্তি, শিল্প এবং সক্রিয়তায় স্বায়ত্তশাসন

Regeln brechen: Autonomie in Technologie, Kunst und Aktivismus

Rompiendo reglas: autonomía en tecnología, arte y activismo

Briser les règles : autonomie dans la technologie, l'art et l'activisme

नियम तोड़ना: तकनीक, कला और सक्रियता में स्वायत्तता

ルールの破り: テクノロジー、アート、アクティビズムにおける自律性

규칙 위반: 기술, 예술 및 행동주의의 자율성

Quebrando regras: autonomia em tecnologia, arte e ativismo

Нарушение правил: автономия в технологиях, искусстве и активизме

Kuralları Çiğnemek: Teknolojide, Sanatta ve Aktivizmde Özerklik

Phá vỡ các quy tắc: Quyền tự chủ trong công nghệ, nghệ thuật và hoạt động

打破规则：科技、艺术和行动主义的自主性

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 7: Robot Learning Algorithm Details Object and skill learning details We utilize pre-trained R3M as the feature extractor. Our training procedure aims to learn a latent representation of an input image for inferring the correct object-skill pair in the given scene. The feature embedding model is a fully-connected neural network that further encodes the outputs of the foundation model. Model parameters and training hyperparameters are summarized in Table 7. Collecting human data using a BRI system is expensive. To enable few-shot learning, the feature embedding model is trained using a triplet loss [80], which operates on three input vectors: anchor, positive (with the same label as the anchor), and negative (with a different label). Triplet loss pulls inputs with the same labels together by penalizing their distance in the latent space, as well as pushes inputs with different labels away. The loss function is defined as: where a is the anchor vector, p the positive vector, n the negative vector, f the model, and α = 1 is our separation margin. We test our algorithm in the following generalization settings: Generalization test set. For position generalization, we randomize the initial positions of all objects in the scene with fixed orientation and collect 20 different trajectories. For the pose generalization, we randomize both the initial positions and orientations of all objects. Position and pose. . The context-level generalization refers to placing the target object in different environments, defined by different backgrounds, object orientations, and the inclusion of different objects in the scene. We collect 20 different trajectories with these variations. Context . The instance generalization aims to assess the model’s capability to generalize across different types of objects present in the scene. For our target task (MakePasta), we collect 20 trajectories with 20 different kinds of pasta with different shapes, sizes, and colors. Instance . To understand the separability of latent representations generated by our object-skill learning model, we visualize the 1024-dimensional final image representations using the t-SNE data visualization technique [81]. Results for the MakePasta pose generalization test set are shown in Figure 7. The model can well separate each of the different stages of the task, allowing us to retrieve the correct object-skill pair for an unseen image. Latent representation visualization One-shot parameter learning details We empirically found that using DINOv2’s ViT-B model, alongside a 75x100 feature map and a 3x3 sliding window, with cosine similarity as the distance metric, resulted in the best performance for our image resolutions. Design choices. We test the generalization ability of our algorithm on 1008 unique training and test pairs, encompassing four types of generalizations including 8 position trials, 8 orientation trials, 32 context trials, and 960 instance trials. Generalization test set The position and orientation generalizations, shown in Fig. 8 and Fig. 9 respectively, are tested in isolation, e.g. when the position is varied, the orientation is kept the same. Position and orientation The context-level generalization, shown in Fig. 10, refers to placing the target object in different environments, e.g. the training image might show the target object in the kitchen while the test image shows the target object in a workspace. Here, we allow for position and orientation to vary as well. Context To test our algorithm’s capability of instance-level generalization, shown in Fig. 11, we collected a set of four different object categories, each containing five unique object instances. Our object categories consist of mug, pen, bottle, and medicine bottle, whereas the bottle and medicine bottle categories consist of images from both the top-down and side views. We test all permutations within each object category including train and test pairs with different camera views. Here, we allow for position and orientation to vary as well. Instance We test our method against baselines on 1080 unique training and test pairs, encompassing four types of generalizations including 8 position trials, 8 orientation trials, 32 context trials, 960 instance trials, 48 trials where we vary all four generalizations simultaneously, and 24 trials from the SetTable task. Test set for comparing our method against baselines Finally, we test our algorithm’s ability to generalize when all four variables differ between the training and test image, shown in Fig. 12. Here, the only object category we use is a mug. Position, orientation, context, and instance simultaneously. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 7

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 6: Decoding Algorithms Details For both SSVEP and MI, we select a subset of channels and discard the signals from the rest, as shown in Figure 6. They correspond to the visual cortex for SSVEP, and the motor and visual areas for MI (with peripheral areas). For muscle tension (jaw clenching), we retain all channels. . To predict the object of interest, we apply Canonical Correlation Analysis (CCA) as shown in [77] to the collected SSVEP data. As each potential object of interest is flashing at a different frequency, we are able to generate reference signals Yf for each frequency fi: SSVEP n where f is the sampling frequency and N is the number of samples. s s By calculating the maximum correlation ρfn for each frequency f used for potential objects of interest, we are then able to predict the output class by finding argmax (ρf ) and matching the result to the object of interest with that frequency. n fn n Furthermore, we are able to return a list of predicted objects of interest in descending order of likelihood by matching each object to a list of descending maximum correlations ρf . n Motor imagery. To perform MI classification, we first band-pass filter the data between 8Hz - 30Hz, as that is the frequency range that includes the µ-band and β-band signals relevant to MI. The data is then transformed using the Common Spatial Pattern (CSP) algorithm. CSP is a linear transformation technique that applies a rotation to the data to orthogonalize the components where the over-timestep variance of the data differs the most across classes. We can then use the logvariance of each time series after rotation as features and perform QDA. Thereafter, we extract features by taking the normalized variance of this transformed data (called “CSP-space data”). We then perform Quadratic Discriminant Analysis (QDA) on this data. To calculate our calibration accuracy, we perform K-fold cross validation with KCV = 4, but we use the entire calibrate dataset to fit the classifier for deployment at task-time. Facial muscle tension results in a very significant high-variance signal across almost all channels that is very detectable using simple variance-based threshold filters without having to perform any frequency filters. Recall that we record three 500ms-long trials for each class (“Rest”, “Clench”). In short, for each of the calibration time-series, we take the variance of the channel with the median variance; call this variance m. Then, we just take the mid-point between the maximum m between the rest samples, and the minimum m between the clench samples, and have this be our threshold variance level. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 6

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 5: Experimental Procedure . In our experiments, we use the 128-channel HydroCel Geodesic SensorNet from Magstim EGI, which has sponge tips in its electrode channels. Prior to experiments, the EEG net is soaked in a solution containing dissolved conductive salt (Potassium Chloride) and baby shampoo for 15 minutes. After the soaking, the net is worn by the experiment subject, and an impedance check is done. This impedance check entails ensuring that the impedance of each channel electrode is ≤ 50.0 kΩ, by using a syringe to add more conductive fluid between the electrodes and the scalp. We then carefully put on a shower cap to minimize the drying of conductive fluid over the course of the experiment. EEG device preparation Before commencing the experiments, subjects are given instructions on how to execute the SSVEP, MI, and muscle tension (jaw-clenching) tasks. For SSVEP, they are instructed to simply focus on the flickering object of interest without getting distracted by the other objects on the screen. For MI, similar to datasets such as BCI Competition 2003 [75], and as per extensive literature review [76], we instruct subjects to either imagine continually bending their hands at the wrist (wrist dorsiflexion) or squeezing a ball for the hand actions (“Left”, “Right”), and to imagine depressing a pedal with both feet (feet dorsiflexion) for the “Legs” action. For the “Rest” class, as is common practice in EEG experiments in general, we instruct users to focus on a fixation cross displayed on the screen. Subjects were told to stick with their actions of choice throughout the experiment, for consistency. For muscle tension, subjects were told to simply clench their jaw without too much or too little effort. Instructions to subjects. . For SSVEP, subjects are told in writing on the screen to focus on the object of interest. Thereafter, a scene image of the objects with flickering masks overlaid on each object is presented, and we immediately begin recording the EEG Data over this period of time. For MI, the cues are different during calibration and task-time. During calibration, subjects are presented with a warning symbol (.) on screen for 1 second, before being presented with the symbol representing the action they are to imagine (<-: “Left”, ->: “Right”, v: “Legs”, +: “Rest”), which lasts on screen for 5500 ms. We record the latter 5000 ms of EEG data. After which, there is a randomized period of rest the lasts between 0.5 and 2 seconds, before the process repeats for another randomly chosen action class. This is done in 4 blocks of 5 trials per action, for a total of 20 trials per action. This procedure is again similar to datasets like BCI Competition 2003 [75], that use non-linguistic cues and randomization of rest / task. At task-time, similar to SSVEP, subjects are told in writing on the screen to perform MI to select a robot skill to execute. Thereafter, a written mapping of a class symbol ({<-, ->, v, +}) to skill ({pick from top, pick from side, ...}) is presented, and we begin recording EEG Data after a 2-second delay. For muscle tension, there is also a calibration phase, similar to MI, which entails collecting three 500ms-long trials for each class (“Rest”, and “Clench”) at the start of each experiment. The cues are written on the screen in words. At task time, when appropriate, written prompts are also presented on the screen (e.g. “clench if incorrect”), followed by a written countdown, after which the user has a 500ms window to clench (or not). Interface This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 5

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 4: Task Definitions For systematic evaluation of task success, we provide formal definitions of our tasks in the format of BEHAVIOR Domain Definition Language (BDDL) language [69, 71]. BDDL is a predicate logic-based language that establishes a symbolic state representation built on predefined, meaningful predicates grounded in physical states [71]. Each task is defined in BDDL as an initial and goal condition parametrizing sets of possible initial states and satisfactory goal states, as shown in the figures at the end of the appendix. Compared to scene- or pose-specific definitions which are too restricted, BDDL is more intuitive to humans while providing concrete evaluation metrics for measuring task success. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 4

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 3: System Setup The robot we use in our tabletop manipulation task is a standard Franka Emika robot arm with three RealSense cameras. For mobile manipulation, we use a Tiago++ model from PAL Robotics, with an omnidirectional base, two 7-degrees-of-freedom arms with parallel-yaw grippers, a 1-degree-of-freedom prismatic torso, two SICK LiDAR sensors (back and front of the base), and an ASUS Xtion RGB-D camera mounted on the robot’s head, which can be controlled in yaw and pitch. All sensors and actuators are connected through the Robot Operating System, ROS [74]. The code runs on a laptop with an Nvidia GTX 1070 that sends the commands to the onboard robot computer to be executed. Robot platform. A list of primitive skills along with their parameters can be found in Table 6, eight for Franka (16 tasks) and five for Tiago (four tasks). Human users can accomplish all 20 tasks, which are long-horizon and challenging, using these skills. Primitive skills list. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 3

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 2: Comparison between Different Brain Recording Devices We use the EGI NetStation EEG system which uses rapid application 128-channel saline-based EGI SensorNets. Here we justify our choice of using non-invasive, saline-based EEG as the recording device for brain signals. A comparison of different brain reading devices (gel-based EEG, dry EEG, MEG, fMRI, fNIRS, implant) and their advantages and disadvantages are shown in Table 5, using our device as the baseline. Two noticeable alternatives are functional magnetic resonance imaging (fMRI) and invasive implants. fMRI measures the small changes in blood flow that occur with brain activity, which has a very high spatial resolution hence fine-grained information such as object categories and language [31] can be decoded from it. But fMRI suffers from low temporal resolution, and the recording device is extremely costly and cannot be used in daily scenarios. Brain implants have a very good signal-to-noise ratio and have great potential. However, the main concern is that it requires surgery to be applied, and health-related risks are not negligible. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 2

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 1

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Abstract We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots using electroencephalography (EEG). Our novel system demonstrates success in an expansive array of 20 challenging, everyday household activities, including cooking, cleaning, personal care, and entertainment. The effectiveness of the system is improved by its synergistic integration of robot learning algorithms, allowing for NOIR to adapt to individual users and predict their intentions. Our work enhances the way humans interact with robots, replacing traditional channels of interaction with direct, neural communication. Project website: https://noir-corl.github.io/ Brain-Robot Interface; Human-Robot Interaction Keywords: 1 Introduction Brain-robot interfaces (BRIs) are a pinnacle achievement in the realm of art, science, and engineering. This aspiration, which features prominently in speculative fiction, innovative artwork, and groundbreaking scientific studies, entails creating robotic systems that operate in perfect synergy with humans. A critical component of such systems is their ability to communicate with humans. In human-robot collaboration and robot learning, humans communicate their intents through actions [1], button presses [2, 3], gaze [4–7], facial expression [8], language [9, 10], etc [11, 12]. However, the prospect of direct communication through neural signals stands out to be the most thrilling but challenging medium. We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent BRI system with non-invasive electroencephalography (EEG). The primary principle of this system is hierarchical shared autonomy, where humans define high-level goals while the robot actualizes the goals through the execution of low-level motor commands. Taking advantage of the progress in neuroscience, robotics, and machine learning, our system distinguishes itself by extending beyond previous attempts to make the following contributions. First, NOIR is general-purpose in its diversity of tasks and accessibility. We show that humans can accomplish an expansive array of 20 daily everyday activities, in contrast to existing BRI systems that are typically specialized at one or a few tasks or exist solely in simulation [13–22]. Additionally, the system can be used by the general population, with a minimum amount of training. Second, the “I” in NOIR means that our robots are intelligent and adaptive. The robots are equipped with a library of diverse skills, allowing them to perform low-level actions without dense human supervision. Human behavioral goals can naturally be communicated, interpreted, and executed by the robots with parameterized primitive skills, such as Pick(obj-A) or MoveTo(x,y). Additionally, our robots are capable of learning human intended goals during their collaboration. We show that by leveraging the recent progress in foundation models, we can make such a system more adaptive with limited data. We show that this can significantly increase the efficiency of the system. The key technical contributions of NOIR include a modular neural signal decoding pipeline for human intentions. Decoding human intended goals (e.g., “pick up the mug from the handle”) from neural signals is extremely challenging. We decompose human intention into three components: What object to manipulate, How to interact with the object, and Where to interact, and show that such signals can be decoded from different types of neural data. These decomposed signals naturally correspond to parameterized robot skills and can be communicated effectively to the robots. In 20 household activities involving tabletop or mobile manipulations, three human subjects successfully used our system to accomplish these tasks with their brain signals. We demonstrate that few-shot robot learning from humans can significantly improve the efficiency of our system. This approach to building intelligent robotic systems, which utilizes human brain signals for collaboration, holds immense potential for the development of critical assistive technologies for individuals with or without disabilities and to improve the quality of their life. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities

NOIR: দৈনন্দিন কার্যকলাপের জন্য নিউরাল সিগন্যাল পরিচালিত বুদ্ধিমান রোবট

NOIR: Mit neuronalen Signalen betriebene intelligente Roboter für alltägliche Aktivitäten

NOIR: Robots inteligentes operados por señales neuronales para actividades cotidianas

NOIR : robots intelligents actionnés par des signaux neuronaux pour les activités quotidiennes

NOIR: रोजमर्रा की गतिविधियों के लिए न्यूरल सिग्नल संचालित इंटेलिजेंट रोबोट

NOIR: 日常活動のための神経信号で動作するインテリジェント ロボット

느와르(NOIR): 일상 활동을 위한 신경 신호로 작동되는 지능형 로봇

NOIR: Robôs Inteligentes Operados por Sinais Neurais para Atividades Diárias

NOIR: интеллектуальные роботы, управляемые нейронными сигналами, для повседневной деятельности

NOIR: Günlük Faaliyetler için Nöral Sinyalle Çalışan Akıllı Robotlar

NOIR: Robot thông minh vận hành bằng tín hiệu thần kinh cho các hoạt động hàng ngày

NOIR：用于日常活动的神经信号操作智能机器人

Political Story Opinion By Jesse Wood “The only thing necessary for the triumph of evil is for good men to do nothing.” – Edmund Burke. Israel has time to online bully climate activists with autism disorder, but not to answer for their war crimes. But the Israel IDF refuses to answer for their war crimes, violations of international law, or ethnic cleansing of Palestinian civilians via collective punishment and maximized collateral damage. See: https://x.com/jrhwood/status/1715362471228452998?s=20 Yesterday, I called them out very publicly on this, and they remained silent. They chose to neither refute nor justify my accusations of war crimes. The comment in X (formerly Twitter) received a tremendous amount of attention and support through organic online engagement. See: https://x.com/payette_lake/status/1715685481953083749?s=20 Then there were reports it was not being shown properly and failed to load in the X browser client and on Apple IOS. This is not an accusation of censorship, but I have yet to receive a response from X.com Support although I reached out to them both publicly and via direct message. I am truly sickened by Israel's flagrant violation of international law, blatant war crimes, and genocide, or ethnic cleansing, of the 2.2 million strong Palestinian civilian population with collective punishment and maximized collateral damage. Israel is not Hamas. They aren't Islamic Jihadists. Now, it is time for them to start acting like it. See: https://x.com/PalestineRCS/status/1714956642314338699?s=20 They need to immediately cease bombing babies, women, children, the elderly, churches, bakeries, humanitarian volunteer aid workers (e.g., ), and the West Bank  (e.g., ). PalestineRCS Dr Shtayyeh “The line separating good and evil passes not through states, nor between classes, nor between political parties either -- but right through every human heart -- and through all human hearts. This line shifts. Inside us, it oscillates with the years. And even within hearts overwhelmed by evil, one small bridgehead of good is retained” ― Aleksandr Solzhenitsyn, The Gulag Archipelago 1918–1956 It is completely unacceptable, and unequivocally evil and villainous. And they deserve the heavy condemnation they received from the international community at the United Nations summit in Cairo, which said just that. Even Putin, who is currently raging a war in Ukraine, and the Iran Ayatollah Qassim, who is an Islamic Republic, think Israel is too hardcore for them. Hypocrisy, and a performative contradiction yes, but a lie? No! Genocide and the Ethnic cleansing, of the Palestinian civilian population with collective punishment and maximized collateral damage, is a pure evil genocide of the innocent, that Israel IDF carried out to justify an open-air prison apartheid state in Gaza and the West Bank. "Mankind must put an end to war before war puts an end to mankind." - John F. Kennedy 🇮🇱🕊️💔🕊️🇵🇸 Also published here

Israel Has Time to Bully Climate Activists With Autism, but Not to Answer for Their War Crimes!

You can jump to any section of y The National Cislunar Science & Technology Strateg here. The longer term: a new sphere of human activity Cislunar space is vast. Its volume of space is more than 2,000 times larger than the entire region of space within geosynchronous orbit, and the furthest region of Cislunar space is more than twelve times the distance of geosynchronous orbit from Earth. Spacecraft can operate in a variety of trajectories in Cislunar space including Lunar orbit, trans-lunar trajectories, and in families of orbits in the large regions around Earth-Moon Lagrange points. A critical region of Cislunar space is the surface of the Moon itself. The Moon is tidally-locked, meaning one hemisphere (the “near side”) always faces the Earth, while the “far side” cannot be seen from Earth. This allows direct communication from Earth to the near side, but keeps the far side in the Shielded Zone of the Moon. The polar regions of the Moon are especially resource rich. Special locations include topographic peaks that are in near-continuous sunlight, and extremely cold, permanently shadowed regions that may preserve substantial deposits of ancient ice and other useful chemical compounds only found in trace amounts in other parts of the Moon. In addition to government space programs, many other kinds of entities (spanning commercial, nonprofit, and academic actors) are also expected to be operating in Cislunar space in the years ahead. The vision articulated in this document is designed to engage and be relevant to all space-faring entities as part of advancing a sustainable “Cislunar ecosystem.” To implement this vision, it will be necessary for the U.S. government to collaborate with other entities in the Cislunar ecosystem on a broad range of capabilities, standards, and infrastructure. The United States continues to engage the international community to uphold and strengthen a rulesbased international order for space, including in Cislunar space. Multiple international agreements already govern Cislunar space, including activities on the Lunar surface. This strategy is designed to be fully consistent with those agreements as well as other non-legally binding instruments that are applicable to Cislunar space. Particularly relevant to this strategy are the Outer Space Treaty of 1967,[5] the Rescue Agreement of 1968, [6] the Registration Convention,[7] the Liability Convention,[8] the ITU Radio Regulations,[9] the Artemis Accords,[10] additional efforts of the United Nations Committee on Peaceful Uses of Outer Space (COPUOS),[11] and other ongoing multilateral discussions and international treaties. 5 Treaty on Principles Governing the Activities of States in the Exploration and Use of Outer Space, Including the Moon and other Celestial Bodies, United Nations Office for Outer Space Affairs [6] Agreement on the Rescue of Astronauts, the Return of Astronauts, and the Return of Objects Launched into Outer Space, United Nations (UN) Office for Outer Space Affairs [7] Convention on Registration of Objects Launched into Outer Space, UN Office for Outer Space Affairs [8] Convention on International Liability for Damage Caused by Space Objects, UN Office for Outer Space Affairs [9] ITU Radio Regulations, World Radiocommunication Conferences [10] The Artemis Accords, Principles for Cooperation in the Civil Exploration and Use of the Moon, Mars, Comets, and Asteroids for Peaceful Purposes, NASA [11] Committee on the Peaceful Uses of Outer Space, United Nations Office for Outer Space Affairs This content was initially published on in November 2022. whitehouse.gov This content has been broken-up into bite sized sections, each with unique headlines and AI-generated lead images, in line with the provisions of Creative Commons 3.0.

U.S. Outlines Ambitious Plans for Human Activities on the Moon 

is an open, distributed social network protocol standardized by the World Wide Web Consortium. Its first version was released in 2018, so it is not new. It is used by several social networks, including , which has recently seen a significant increase in popularity when Elon Musk bought Twitter and made changes that did not please many. ActivityPub Mastodon In addition, Tumblr and Facebook's alternative to Twitter, Threads, have also signaled that they will support the protocol in the future. Despite not being a new protocol, ActivityPub is now becoming increasingly popular. As the European Union and the United States have long been concerned about the market monopoly of large social networks, I can imagine that in the future, under government pressure, the existing largest social networks such as Facebook and Twitter will also support the protocol and become part of the . (The Fediverse is a universe of social networks consisting of providers supporting ActivityPub.) Fediverse I believe that in the future, we will not choose Twitter or Facebook because they will be closed and we will have no other choice. The strength of each platform will come from who has better artificial intelligence to filter the incoming massive amount of data, to sift through, select, and summarize the most important information for the users. Of course, to do this, one must know the user very well, which is also in the user's interest. In return, artificial intelligence can provide much more relevant advertisements. In an open, accessible system, the competition will no longer be among social networks, but among artificial intelligence-based algorithms. Let's see how ActivityPub works. The basic functionality is nicely summarized by this very simple diagram. ActivityPub Explained The protocol has two main components: the inbox and the outbox. These are two API endpoints that can be accessed through the HTTP protocol. When a user wants to communicate something with the outside world (e.g., post some content), they send it to the outbox. Here, the system adds it to an appropriate list, which followers can access by reading the outbox. If someone from the outside world wants to send something to the user (e.g., a non-public post that can only be seen by certain users), they send it to the inbox in encrypted form. The inbox is like an email inbox, where incoming content is collected and where the user can access it. One can retrieve the location of the inbox and outbox using the WebFinger protocol. This can also be tested with the help of the , a super tool for experimenting with and understanding the protocol. ActivityPub Explorer A username in ActivityPub is similar to an email address. For example, my Mastodon username is @thebojda@me.dm. This means that my user information is stored on the dm.me server under the name thebojda. When entered into ActivityPub Explorer, it will query the following URL according to the WebFinger protocol to retrieve the data: https://me.dm/.well-known/webfinger?resource=acct:thebojda%40me.dm The response is a JSON object that looks like this: {
 "subject":"acct:thebojda@me.dm",
 "aliases":[
 "https://me.dm/@thebojda",
 "https://me.dm/users/thebojda"
 ],
 "links":[
 {
 "rel":"http://webfinger.net/rel/profile-page",
 "type":"text/html",
 "href":"https://me.dm/@thebojda"
 },
 {
 "rel":"self",
 "type":"application/activity+json",
 "href":"https://me.dm/users/thebojda"
 },
 {
 "rel":"http://ostatus.org/schema/1.0/subscribe",
 "template":"https://me.dm/authorize_interaction?uri={uri}"
 }
 ]
} It can be determined from here that the ActivityPub information will be accessible at the URL. If we enter this into the ActivityPub Explorer, it will return the following JSON: https://me.dm/users/thebojda {
 "@context": [
 "https://www.w3.org/ns/activitystreams",
 "https://w3id.org/security/v1",
 {
 "manuallyApprovesFollowers": "as:manuallyApprovesFollowers",
 "toot": "http://joinmastodon.org/ns#",
 "featured": {
 "@id": "toot:featured",
 "@type": "@id"
 },
 "featuredTags": {
 "@id": "toot:featuredTags",
 "@type": "@id"
 },
 "alsoKnownAs": {
 "@id": "as:alsoKnownAs",
 "@type": "@id"
 },
 "movedTo": {
 "@id": "as:movedTo",
 "@type": "@id"
 },
 "schema": "http://schema.org#",
 "PropertyValue": "schema:PropertyValue",
 "value": "schema:value",
 "discoverable": "toot:discoverable",
 "Device": "toot:Device",
 "Ed25519Signature": "toot:Ed25519Signature",
 "Ed25519Key": "toot:Ed25519Key",
 "Curve25519Key": "toot:Curve25519Key",
 "EncryptedMessage": "toot:EncryptedMessage",
 "publicKeyBase64": "toot:publicKeyBase64",
 "deviceId": "toot:deviceId",
 "claim": {
 "@type": "@id",
 "@id": "toot:claim"
 },
 "fingerprintKey": {
 "@type": "@id",
 "@id": "toot:fingerprintKey"
 },
 "identityKey": {
 "@type": "@id",
 "@id": "toot:identityKey"
 },
 "devices": {
 "@type": "@id",
 "@id": "toot:devices"
 },
 "messageFranking": "toot:messageFranking",
 "messageType": "toot:messageType",
 "cipherText": "toot:cipherText",
 "suspended": "toot:suspended",
 "focalPoint": {
 "@container": "@list",
 "@id": "toot:focalPoint"
 }
 }
 ],
 "id": "https://me.dm/users/thebojda",
 "type": "Person",
 "following": "https://me.dm/users/thebojda/following",
 "followers": "https://me.dm/users/thebojda/followers",
 "inbox": "https://me.dm/users/thebojda/inbox",
 "outbox": "https://me.dm/users/thebojda/outbox",
 "featured": "https://me.dm/users/thebojda/collections/featured",
 "featuredTags": "https://me.dm/users/thebojda/collections/tags",
 "preferredUsername": "thebojda",
 "name": "Laszlo Fazekas",
 "summary": "Software developer, contributing writer",
 "url": "https://me.dm/@thebojda",
 "manuallyApprovesFollowers": false,
 "discoverable": false,
 "published": "2023-03-02T00:00:00Z",
 "devices": "https://me.dm/users/thebojda/collections/devices",
 "publicKey": {
 "id": "https://me.dm/users/thebojda#main-key",
 "owner": "https://me.dm/users/thebojda",
 "publicKeyPem": "-----BEGIN PUBLIC KEY----- MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxlvpgcOxBikiXfm9snZt EB3Y2BSnBW3s8nd1d4Z1wCzaNfa/woB6RDQJoQYEISbMpkEyWhDzI4jRDafpn5/j YiyyGka9U2KZHv1to0Ej9uVwUcQOnsq9iUtJGhOWCP27blKGTLKFEHtPL2Y4hp4Z kYzlh1x0aAyw8YC7/nbS8WDAeZNS7R3ET7Syhp3LKiCMmL1aCLSoOQJ5DdxVikMJ rJ9bzlUjxzCsm1aNBB0i269t4fD1evBO8QDhEAOnAZ6wLEV74j9SCjYMvKRV8z5i PQbhbKntXjn1XhbkIkj1D+yGRYYfnm1XyWTuaM4mDllvuOyqJ8GQVFADLc6KCQYz 7QIDAQAB -----END PUBLIC KEY----- "
 },
 "tag": [],
 "endpoints": {
 "sharedInbox": "https://me.dm/inbox"
 },
 "icon": {
 "type": "Image",
 "mediaType": "image/jpeg",
 "url": "https://media.me.dm/accounts/avatars/109/955/144/019/799/820/original/fe8e930e0f1467ac.jpeg"
 }
} Here, you can find all the user-specific information about me, but what is most important, here are the URLs of the inbox and outbox endpoints, as well as the public key for encrypting messages sent to me. When querying the content of the outbox, the following JSON is returned: {
 "@context": "https://www.w3.org/ns/activitystreams",
 "id": "https://me.dm/users/thebojda/outbox",
 "type": "OrderedCollection",
 "totalItems": 1,
 "first": "https://me.dm/users/thebojda/outbox?page=true",
 "last": "https://me.dm/users/thebojda/outbox?min_id=0&page=true"
} As the posts are divided into pages, here we can see the URLs of the first and last pages. By accessing the URL of the first page, we can view my entries, of which there is currently only one. {
 "@context": [
 "https://www.w3.org/ns/activitystreams",
 {
 "ostatus": "http://ostatus.org#",
 "atomUri": "ostatus:atomUri",
 "inReplyToAtomUri": "ostatus:inReplyToAtomUri",
 "conversation": "ostatus:conversation",
 "sensitive": "as:sensitive",
 "toot": "http://joinmastodon.org/ns#",
 "votersCount": "toot:votersCount"
 }
 ],
 "id": "https://me.dm/users/thebojda/outbox?page=true",
 "type": "OrderedCollectionPage",
 "prev": "https://me.dm/users/thebojda/outbox?min_id=109955178005562038&page=true",
 "partOf": "https://me.dm/users/thebojda/outbox",
 "orderedItems": [
 {
 "id": "https://me.dm/users/thebojda/statuses/109955178005562038/activity",
 "type": "Create",
 "actor": "https://me.dm/users/thebojda",
 "published": "2023-03-02T18:47:47Z",
 "to": [
 "https://www.w3.org/ns/activitystreams#Public"
 ],
 "cc": [
 "https://me.dm/users/thebojda/followers"
 ],
 "object": {
 "id": "https://me.dm/users/thebojda/statuses/109955178005562038",
 "type": "Note",
 "summary": null,
 "inReplyTo": null,
 "published": "2023-03-02T18:47:47Z",
 "url": "https://me.dm/@thebojda/109955178005562038",
 "attributedTo": "https://me.dm/users/thebojda",
 "to": [
 "https://www.w3.org/ns/activitystreams#Public"
 ],
 "cc": [
 "https://me.dm/users/thebojda/followers"
 ],
 "sensitive": false,
 "atomUri": "https://me.dm/users/thebojda/statuses/109955178005562038",
 "inReplyToAtomUri": null,
 "conversation": "tag:me.dm,2023-03-02:objectId=1406005:objectType=Conversation",
 "content": "My &quot;ars poetica&quot;: How to Change the World?! Pocket Guide for People With a Messiah Complex <a href=\"https://medium.com/geekculture/how-to-change-the-world-pocket-guide-for-people-with-a-messiah-complex-dd2d16bb92a\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">https://medium.com/geekculture/how-to-change-the-world-pocket-guide-for-people-with-a-messiah-complex-dd2d16bb92a</a>",
 "contentMap": {
 "en": "My &quot;ars poetica&quot;: How to Change the World?! Pocket Guide for People With a Messiah Complex <a href=\"https://medium.com/geekculture/how-to-change-the-world-pocket-guide-for-people-with-a-messiah-complex-dd2d16bb92a\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">https://medium.com/geekculture/how-to-change-the-world-pocket-guide-for-people-with-a-messiah-complex-dd2d16bb92a</a>"
 },
 "attachment": [],
 "tag": [],
 "replies": {
 "id": "https://me.dm/users/thebojda/statuses/109955178005562038/replies",
 "type": "Collection",
 "first": {
 "type": "CollectionPage",
 "next": "https://me.dm/users/thebojda/statuses/109955178005562038/replies?only_other_accounts=true&page=true",
 "partOf": "https://me.dm/users/thebojda/statuses/109955178005562038/replies",
 "items": []
 }
 }
 }
 }
 ]
} Based on these examples, it is easy to implement a minimal client that can read the public posts of others or can publish public posts to the outside world. I will not go into the exact structure of the individual JSON files and the functioning of the inbox here. Those can be found in the . ActivityPub documentation As can be seen, ActivityPub is a relatively simple standard that is easy to understand and implement. Its only weakness is that it uses the HTTP protocol, which requires a central server. This gave rise to alternative protocols such as Nostr, which is Jack Dorsey's (the founder of Twitter) favorite project. However, I believe there is no need for this, and it only causes further fragmentation of social networks. Instead, the ActivityPub protocol should be extended with support for already existing decentralized protocols, which would allow the Fediverse to develop towards an even more open and decentralized direction. In the following, I will demonstrate how this can be achieved. One perfect solution for decentralizing descriptive data and feeds is to use or (I have a about these protocols). In the case of Ethereum Swarm, the feed identifier is a Swarm address, while in the case of IPNS, it is a public key. Swarm Feeds IPNS full article Using these, an ActivityPub user would look like this: {swarm address}@swarm or {IPNS address}@IPNS. The user description could be queried from here. For backward compatibility, gateway servers could also be used such as {swarm address}@gateway.ethswarm.org, which would return the user's description stored on Swarm through the Webfinger protocol, allowing existing systems (like Mastodon) to read this data without any modifications. The implementation of the outbox would also be done through Swarm or IPNS feeds. In order to maintain backward compatibility, a "decentralized_outbox" field would need to be introduced, where a Swarm feed (bzz://...) or IPNS (ipns://...) address would be located. In order to maintain backward compatibility, an additional gateway address could be included in the original "outbox" field so that existing clients can read these feeds without any changes. Implementation of an inbox can be done in several ways. The Ethereum Swarm has a messaging system called , but there are other alternatives as well, such as . Here, the PSS or Waku address would be placed in a "decentralized_inbox" field, while the original inbox field would contain a gateway address. PSS Waku With these few small extensions, ActivityPub can be made completely decentralized and "web3 compatible", without the need for introducing new protocols like Nostr. According to Nostr's , the need for the new protocol and relay system is due to the potential censorship of users by ActivityPub servers and the lack of an incentive system. However, this problem is completely solved by the above extension. GitHub repo Both IPNS and Swarm are censorship resistant, with Swarm also providing complete anonymity. Additionally, Swarm has its own incentive system, while FileCoin is available for IPNS. I believe that the future clearly belongs to ActivityPub, and developers should focus on advancing and evolving ActivityPub instead of creating new protocols, in order to finally establish a comprehensive and truly unified Fediverse.

A Brief Introduction of ActivityPub: The Future of Social Networks

অ্যাক্টিভিটিপাবের সংক্ষিপ্ত ভূমিকা: সামাজিক নেটওয়ার্কের ভবিষ্যত

Eine kurze Einführung in ActivityPub: Die Zukunft sozialer Netzwerke

Una breve introducción de ActivityPub: el futuro de las redes sociales

Une brève introduction d'ActivityPub : l'avenir des réseaux sociaux

एक्टिविटीपब का संक्षिप्त परिचय: सामाजिक नेटवर्क का भविष्य

ActivityPub の簡単な紹介: ソーシャル ネットワークの未来

ActivityPub에 대한 간략한 소개: 소셜 네트워크의 미래

Uma breve introdução ao ActivityPub: o futuro das redes sociais

Краткое введение в ActivityPub: будущее социальных сетей

ActivityPub'a Kısa Bir Giriş: Sosyal Ağların Geleceği

Giới thiệu ngắn gọn về Hoạt độngPub: Tương lai của Mạng xã hội

ActivityPub简介：社交网络的未来

As of today, the narrative of Rollups has gradually become ingrained in people's minds. On one hand, it inherits the security, user base, and ecosystem of the Mainnet while reducing transaction fees and effectively boosting performance, greatly enhancing the user experience. On the other hand, the business model of Layer2, such as transaction fee commissions, is becoming increasingly clear. As the primary direction for Layer2 scaling, the Rollup track has seen frequent developments lately: Linea announced the opening of Alpha access to the Mainnet, Base officially launched its Mainnet, zkSync's bridge transaction user participation exceeded 2 million... To gain a better understanding of the overall development of the Layer2 track, Opside has compiled statistics on some of the mainstream Layer2 solutions on the market (Polygon, Arbitrum, Optimism, zkSync, Linea, Base, etc.) and conducted an analysis of various dimensions. Below are the specific data performances of major Rollups in August: Transaction Activity In terms of transaction activity, zkSync stands out with an astonishing average daily transaction count of 929,445. This indicates that zkSync's ecosystem has achieved significant success in attracting users and transactions. On the other hand, Metis shows relatively weaker performance with an average daily transaction count of only 116,017, which could be attributed to Metis' high transaction fees. User Costs From a user cost perspective, Metis' users seem to incur relatively high gas fees, with an average daily GasFee of 17.001 Gwei. In stark contrast, Optimism has very low user costs, with an average daily GasFee of only 0.131 Gwei. This could make Optimism more attractive, especially for users looking to reduce transaction costs. However, Optimism has not found an appropriate balance yet. Maybe the low GasFee has led to losses. Performance of revenues and expenses In terms of revenues, Metis shows outstanding performance with an average daily revenue of 62.591 ETH, which is the highest among the analyzed solutions. However, Optimism leads in terms of expenditure, with a monthly total expenditure of 1,272.512 ETH. This may require Optimism to further optimize its expenditure strategy to improve net revenues. Net Income Metis also performs well in net income, with an average daily net income of 61.617 ETH and a monthly total net income of 1,910.115 ETH. In contrast, Optimism has a negative net income, with an average daily net income of -26.050 ETH and a monthly total net income of -807.558 ETH. This calls for measures to improve net income for Optimism to maintain its sustainability, such as appropriately increasing GasFee.

August Rollups Data Analysis: A Closer Look at Transaction Activity

Análisis de datos acumulativos de agosto: una mirada más cercana a la actividad de las transacciones

Analyse des données des cumuls d'août : un examen plus approfondi de l'activité des transactions

अगस्त रोलअप डेटा विश्लेषण: लेन-देन गतिविधि पर एक नज़दीकी नज़र

8 月のロールアップ データ分析: トランザクション アクティビティを詳しく見る

Análise de dados de rollups de agosto: uma análise mais detalhada da atividade de transação

Phân tích dữ liệu tổng hợp tháng 8: Xem xét kỹ hơn về hoạt động giao dịch

八月汇总数据分析：仔细观察交易活动

I dream of hiking from Munich to Venice and trekking across the beautiful Alps. But as I still have to live my everyday life, my journey has to consist of multiple stages, with weeks, months, or even years between each adventure. That is okay, as it’s more about the journey than the destination. However, I always wanted a way to visually retrace these paths, to see how far I’ve come and how close I am to my goal. I wanted a way to celebrate the progress I’ve made and to motivate myself to take the journey further. Fortunately, many outdoor and sports apps, like Adidas Running, Komoot, Strava, and others, graciously allow us to export our activities as GPX files. However, there’s nowhere to go with those GPX files. That is where and come into play. I recently stumbled upon Folium, a powerful Python library for creating interactive maps. It can easily incorporate geographic data, such as the GPX files, and allows for customization and exploration. Drawing upon the wealth of GPS data I’ve gathered, I started experimenting with Folium to craft a map that brings my outdoor excursions to life. Python Folium After some research and a lot of testing, I came up with that allows me to revisit my past outdoor activities: a map So, if you are anything like me and have a treasure trove of GPS data without any purpose, you may wonder how I got there. So, in this article, I will explain how to breathe life into your GPX files. Let’s embark on an expedition of exploration and mapping! Getting Started with Jupyter Notebook To begin this adventure, we’ll be using Jupyter Notebook. Why Jupyter Notebook? It’s a fantastic interactive computing environment that allows us to combine code, visualizations, and text, making it perfect for experimenting with our data and the Folium library. If you haven’t installed Jupyter Notebook yet, follow the instructions on their . Once installed, you can create a new Jupyter Notebook and get ready for your journey. official website Parsing GPX Files for Trail Data Next, we need the raw material for our map — the GPX files. is a widely used file format that stores location data, such as latitude, longitude, elevation, and time, making it ideal for tracking outdoor activities. GPX (GPS Exchange Format) If you’re an avid hiker, runner, cyclist, or skier, chances are you already have tracked various excursions using an outdoor or sports app. Plenty of those apps allow you to export your activities in GPX format. So gather those GPX files, and let’s get started! In our , we’ll use the gpxpy library to parse the GPX files and extract the essential trail data, such as latitude, longitude, elevation, speed, and distance. The parse_gpx() function will do all the heavy lifting for us: Python code ### READ GPX FILES

import gpxpy
import pandas as pd
import numpy as np
import haversine as hs
from pathlib import Path


def parse_gpx(file_path):
 # parse gpx file to pandas dataframe
 gpx = gpxpy.parse(open(file_path), version='1.0')

 data = []
 points = []
 for track in gpx.tracks:
 for segment in track.segments:
 for point_idx, point in enumerate(segment.points):
 points.append(tuple([point.latitude, point.longitude]))

 # calculate distances between points
 if point_idx == 0:
 distance = np.nan
 else:
 distance = hs.haversine(
 point1=points[point_idx-1],
 point2=points[point_idx],
 unit=hs.Unit.METERS
 )

 data.append([point.longitude, point.latitude,point.elevation, point.time, segment.get_speed(point_idx), distance])

 columns = ['Longitude', 'Latitude', 'Elevation', 'Time', 'Speed', 'Distance']
 gpx_df = pd.DataFrame(data, columns=columns)

 return points, gpx_df


activities = {}
frames = []
for activity_type in ACTIVITY_TYPES:
 activities[activity_type] = {}

 pathlist = Path(activity_type).glob('**/*.gpx')
 for path in pathlist:
 # exclude hidden directories and files
 # will lead to performance issues if there are lots of hidden files
 if any(part.startswith('.') for part in path.parts):
 continue

 activity_group = path.parts[1]
 activity_name = path.parts[2]

 if activity_group not in activities[activity_type]:
 activities[activity_type][activity_group] = []

 points, gpx_df = parse_gpx(path)

 gpx_df['Elevation_Diff'] = np.round(gpx_df['Elevation'].diff(), 2)
 gpx_df['Cum_Elevation'] = np.round(gpx_df['Elevation_Diff'].cumsum(), 2)
 gpx_df['Cum_Distance'] = np.round(gpx_df['Distance'].cumsum(), 2)
 gpx_df['Gradient'] = np.round(gpx_df['Elevation_Diff'] / gpx_df['Distance'] * 100, 1)

 activities[activity_type][activity_group].append({
 'name': activity_name.replace('.gpx', '').replace('_', ' '),
 'points': points,
 'gpx_df': gpx_df
 })
 frames.append(gpx_df)

df = pd.concat(frames)
df That leaves us with all the necessary data of an activity: a list of all the GPS coordinates and a Pandas DataFrame containing all kinds of metrics. Plotting GPX Trails on the Interactive Map With our GPS data now organized in a Pandas DataFrame, we can visualize our outdoor activities on an interactive map. Folium makes this task a breeze: ### CONFIG

# LOCATION = None
LOCATION = [48.13743, 11.57549] # latitude, longitude
ZOOM_START = 10

ACTIVITY_TYPES = {
 'Hiking': {
 'icon': 'person-hiking',
 'color': 'green'
 },
 'Running': {
 'icon': 'person-running',
 'color': 'orange'
 },
 'Biking': {
 'icon': 'person-biking',
 'color': 'red'
 },
 'Skiing': {
 'icon': 'person-skiing',
 'color': 'blue'
 }
} We’ll create a map centered at a specific location (you can choose one or let the code determine the center based on your data). We’ll assign unique colors and icons to each activity type, such as hiking, running, biking, or skiing. The ACTIVITY_TYPES dictionary will help us with this. ### READ GPX FILES

import gpxpy
import pandas as pd
import numpy as np
import haversine as hs
from pathlib import Path


def parse_gpx(file_path):
 # parse gpx file to pandas dataframe
 gpx = gpxpy.parse(open(file_path), version='1.0')

 data = []
 points = []
 for track in gpx.tracks:
 for segment in track.segments:
 for point_idx, point in enumerate(segment.points):
 points.append(tuple([point.latitude, point.longitude]))

 # calculate distances between points
 if point_idx == 0:
 distance = np.nan
 else:
 distance = hs.haversine(
 point1=points[point_idx-1],
 point2=points[point_idx],
 unit=hs.Unit.METERS
 )

 data.append([point.longitude, point.latitude,point.elevation, point.time, segment.get_speed(point_idx), distance])

 columns = ['Longitude', 'Latitude', 'Elevation', 'Time', 'Speed', 'Distance']
 gpx_df = pd.DataFrame(data, columns=columns)

 return points, gpx_df


activities = {}
frames = []
for activity_type in ACTIVITY_TYPES:
 activities[activity_type] = {}

 pathlist = Path(activity_type).glob('**/*.gpx')
 for path in pathlist:
 # exclude hidden directories and files
 # will lead to performance issues if there are lots of hidden files
 if any(part.startswith('.') for part in path.parts):
 continue

 activity_group = path.parts[1]
 activity_name = path.parts[2]

 if activity_group not in activities[activity_type]:
 activities[activity_type][activity_group] = []

 points, gpx_df = parse_gpx(path)

 gpx_df['Elevation_Diff'] = np.round(gpx_df['Elevation'].diff(), 2)
 gpx_df['Cum_Elevation'] = np.round(gpx_df['Elevation_Diff'].cumsum(), 2)
 gpx_df['Cum_Distance'] = np.round(gpx_df['Distance'].cumsum(), 2)
 gpx_df['Gradient'] = np.round(gpx_df['Elevation_Diff'] / gpx_df['Distance'] * 100, 1)

 activities[activity_type][activity_group].append({
 'name': activity_name.replace('.gpx', '').replace('_', ' '),
 'points': points,
 'gpx_df': gpx_df
 })
 frames.append(gpx_df)

df = pd.concat(frames)
df

That leaves us with all the necessary data of an activity: a list of all the GPS coordinates and a Pandas DataFrame containing all kinds of metrics.
Plotting GPX Trails on the Interactive Map

With our GPS data now organized in a Pandas DataFrame, we can visualize our outdoor activities on an interactive map. Folium makes this task a breeze:

### CONFIG

# LOCATION = None
LOCATION = [48.13743, 11.57549] # latitude, longitude
ZOOM_START = 10

ACTIVITY_TYPES = {
 'Hiking': {
 'icon': 'person-hiking',
 'color': 'green'
 },
 'Running': {
 'icon': 'person-running',
 'color': 'orange'
 },
 'Biking': {
 'icon': 'person-biking',
 'color': 'red'
 },
 'Skiing': {
 'icon': 'person-skiing',
 'color': 'blue'
 }
}

 We’ll create a map centered at a specific location (you can choose one or let the code determine the center based on your data).

 We’ll assign unique colors and icons to each activity type, such as hiking, running, biking, or skiing. The ACTIVITY_TYPES dictionary will help us with this.

### CREATE MAP

import folium
from folium import plugins as folium_plugins

if LOCATION:
 location = LOCATION
else:
 location=[df.Latitude.mean(), df.Longitude.mean()]

map = folium.Map(location=location, zoom_start=ZOOM_START, tiles=None)
folium.TileLayer('OpenStreetMap', name='OpenStreet Map').add_to(map)
folium.TileLayer('Stamen Terrain', name='Stamen Terrain').add_to(map)


### MAP TRAILS

def timedelta_formatter(td):
 td_sec = td.seconds
 hour_count, rem = divmod(td_sec, 3600)
 hour_count += td.days * 24
 minute_count, second_count = divmod(rem, 60)
 return f'{hour_count}h, {minute_count}min, {second_count}s'

def create_activity_popup(activity):
 df = activity['gpx_df']
 attributes = {
 'Date': {
 'value': df['Time'][df.index[0]].strftime("%m/%d/%Y"),
 'icon': 'calendar'
 },
 'Start': {
 'value': df['Time'][df.index[0]].strftime("%H:%M:%S"),
 'icon': 'clock'
 },
 'End': {
 'value': df['Time'][df.index[-1]].strftime("%H:%M:%S"),
 'icon': 'flag-checkered'
 },
 'Duration': {
 'value': timedelta_formatter(df['Time'][df.index[-1]]-df['Time'][df.index[0]]),
 'icon': 'stopwatch'
 },
 'Distance': {
 'value': f"{np.round(df['Cum_Distance'][df.index[-1]] / 1000, 2)} km",
 'icon': 'arrows-left-right'
 },
 'Average Speed': {
 'value': f'{np.round(df.Speed.mean() * 3.6, 2)} km/h',
 'icon': 'gauge-high'
 },
 'Max. Elevation': {
 'value': f'{np.round(df.Elevation.max(), 2)} m',
 'icon': 'mountain'
 },
 'Uphill': {
 'value': f"{np.round(df[df['Elevation_Diff']>0]['Elevation_Diff'].sum(), 2)} m",
 'icon': 'arrow-trend-up'
 },
 'Downhill': {
 'value': f"{np.round(abs(df[df['Elevation_Diff']<0]['Elevation_Diff'].sum()), 2)} m",
 'icon': 'arrow-trend-down'
 },
 }
 html = f"<h4>{activity['name'].upper()}</h4>"
 for attribute in attributes:
 html += f' {attributes[attribute]["value"]}'
 return folium.Popup(html, max_width=300)

feature_groups = {}
for activity_type in activities:
 color = ACTIVITY_TYPES[activity_type]['color']
 icon = ACTIVITY_TYPES[activity_type]['icon']

 for activity_group in activities[activity_type]: 
 # create and store feature groups
 # this allows different activity types in the same feature group
 if activity_group not in feature_groups:
 # create new feature group
 fg = folium.FeatureGroup(name=activity_group, show=True)
 feature_groups[activity_group] = fg
 map.add_child(fg)
 else:
 # use existing
 fg = feature_groups[activity_group]

 for activity in activities[activity_type][activity_group]:
 # create line on map
 points = activity['points']
 line = folium.PolyLine(points, color=color, weight=4.5, opacity=.5)
 fg.add_child(line)

 # create marker
 marker = folium.Marker(points[0], popup=create_activity_popup(activity),
 icon=folium.Icon(color=color, icon_color='white', icon=icon, prefix='fa'))
 fg.add_child(marker)

map.add_child(folium.LayerControl(position='bottomright'))
folium_plugins.Fullscreen(position='topright').add_to(map)
map We’ll use Foliums FeatureGroup concepts to group the trails based on their group name. That will allow us to show and hide certain activity groups later. Now, we’ll iterate through our parsed data and plot each trail on the map using Folium’s PolyLine and Marker objects. The PolyLine will represent the actual trail, while the Marker will act as a starting point for each activity. When you click on a marker, a popup will display relevant information about the corresponding trail. Conclusion In this journey of exploration and mapping, we’ve learned how to use to transform mundane GPX files into a dynamic and interactive map. Now, you can relive your outdoor adventures, celebrate your progress, and stay motivated for the next stage of your journey. Python and Folium However, there are many ways to customize, extend, and improve your map. So grab your GPX files, fire up Jupyter Notebook, and let your past outdoor activities come to life on the map! Happy mapping! What’s next? Stay tuned because there is much more to come: deploying a website with your map using AWS plotting elevation and speed profiles using Python and Plotly enhancing trails with pictures taken one the way and much more References All the code, including the Jupyter Notebook, is on . my GitHub While doing my research on Folium, I found by Patrick, who did the same thing I planned to do. His work was a great base to build upon my solution, so please check it out. a great article Jupyter Notebook Folium Pandas Also published . here

How I Used Python and Folium to Visualize My Outdoor Activities

কিভাবে আমি আমার বহিরঙ্গন ক্রিয়াকলাপ কল্পনা করতে পাইথন এবং ফোলিয়াম ব্যবহার করেছি

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मैंने अपनी बाहरी गतिविधियों को देखने के लिए पायथन और फोलियम का उपयोग कैसे किया

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我如何使用 Python 和 Folium 可视化我的户外活动

The digital world is growing, and surveillance is growing with it. Governments, companies, and even other individuals are now pretty capable of following our virtual steps —including the financial ones. That’s why a group of activists emerged in the late 20th century with a mission to safeguard individual liberties through the development of decentralized money. The term "cypherpunk" is a blend of "cypher," referring to cryptography, and "punk," which reflects their rebellious and nonconformist nature. So, they’re mostly computer science and cryptography experts aiming to create new digital tools to foster privacy and social change. These activists, known as cypherpunks, laid the groundwork for the creation and popularization of cryptocurrencies like Bitcoin. The movement gained momentum in the 1980s and 1990s as individuals concerned about the increasing surveillance and control of digital communications and transactions sought ways to counteract these trends. We can say it started with , an American cryptographer who is widely recognized for inventing the first forms of digital cash —no, it wasn’t Satoshi Nakamoto alone. David Chaum A prolific mailing list The first started in 1992 as an initiative by Eric Hughes, Timothy C. May, John Gilmore, and Judith Milhon. For more references, Hughes invented the first anonymous remailer (a server to increase privacy in emails), and May discovered the issue in computer chips. Gilmore is one of the founders of the Electronic Frontier Foundation (EFF) to defend digital rights. For her part, Milhon helped to create the first public computerized bulletin board system, besides being a writer and editor (she coined the “Cypherpunks” name). Cypherpunk mailing list Alpha Strike which describes the main purpose of the group and this kind of activism: In 1993, Hughes wrote and shared the , Cypherpunk Manifesto “Privacy is necessary for an open society in the electronic age (…) We cannot expect governments, corporations, or other large, faceless organizations to grant us privacy (…) We must defend our own privacy if we expect to have any (…) Cypherpunks write code. We know that someone has to write software to defend privacy, and (…) we're going to write it.” The number of subscribers to the list (and, likely, to the movement) reached over 2,000 individuals by 1997. But this isn’t the reason why we’re stating that the mailing list was prolific. From this mailing list and this ideal came up numerous talented people that developed a diverse set of digital tools to fight for privacy. To name a few of them: Julian Assange (WikiLeaks), Adam Back (Hashcash & Blockstream), Eric Blossom (GNU Radio Project), Phil Zimmerman (PGP Protocol), Bram Cohen (BitTorrent & Chia), Hal Finney (First Proof-of-Work), Nick Szabo (First Smart Contracts), Wei Dai (B-Money), Zooko Wilcox (Zcash), and, of course, Satoshi Nakamoto (Bitcoin). Most of them are still alive and active in 2023. Before and after Bitcoin One of the most significant contributions of cypherpunks was their role in conceptualizing and promoting the idea of decentralized digital currencies. Influenced by the works of cryptographic pioneers like David Chaum, who introduced the concept of "e-cash," and Wei Dai, who proposed the idea of "b-money," cypherpunks envisioned a system where money could be transferred electronically without the need for intermediaries. This vision laid the foundation for the creation of Bitcoin, the first and most well-known cryptocurrency. As we mentioned above, Nakamoto didn’t do it all by themselves. The process was more like putting together puzzle pieces: Hal Finney’s PoW, some features from e-cash, Hashcash, and b-money, public-key cryptography by Ralph Merkle, and time-stamping by W.S. Stornetta and Stuart Haber. Finally, in 2008, Nakamoto published the . It describes a peer-to-peer electronic cash system that utilized cryptographic techniques to secure transactions and maintain a public ledger. The principles of decentralization, pseudonymity, and cryptographic security closely aligned with the cypherpunk ideals, making Bitcoin the first realization of their vision. Bitcoin whitepaper Decentralization didn’t stop there though. A DAG-based cryptosystem like doesn’t have miners or intermediaries at all. It doesn’t have blocks, either. Only Order Providers (OPs) whose transactions serve as waypoints for ordering everything else —but they don’t have other powers and aren’t needed to “accept” transactions, like Bitcoin miners. By eliminating such big power centers as miners, . Directed Acyclic Graph (DAG) systems are the . next step of decentralization Obyte DAG achieves more even distribution of power than blockchains Cypherpunks' visionary contributions formed the puzzle of cryptocurrency evolution. Bitcoin's whitepaper, embracing decentralization and cryptographic security, materialized cypherpunk ideals. As technology advances, like Obyte emerge, furthering decentralization without middlemen. DAG systems Featured Vector Image by jcomp / Freepik

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It has been over a year since announced a some-what unexpected decision to buy Activision Blizzard Inc. — the maker of , , and — for a whopping . This would be one of the biggest, if not biggest, acquisitions in the video gaming industry. And yet, the Windows maker is to actually buying the company because of regulatory hurdles and resistance from gamers and video game makers, specifically Sony. Microsoft World of Warcraft Call of Duty Diablo $68.7 billion in cash the no closer other Until this past week. Microsoft finally got a break on two fronts in its quest to purchase Activision Blizzard Inc., one in the and another in the UK, which may give it some hope in being able to complete the acquisition. U.S. The company was facing a consumer antitrust lawsuit in the U.S. by gamers concerned that the acquisition would lead to "higher prices, less innovation, less creativity, less consumer choice, decreased output, and other potential anticompetitive effects." Unfortunately for them, the judge overseeing the case ruled that said gamers did not provide enough evidence of how that might happen, the lawsuit out. Ouch! throwing However, the biggest win may have come from the UK, where the country's competition authority took a U-turn on whether the purchase would hurt competition. Having that the transaction could result in higher prices, fewer choices and less innovation — particularly in the console market — the U.K.'s Competition and Markets Authority (CMA) now believes none of that is possible since doing so would only hurt Microsoft. initially suggested "We have now provisionally concluded that the merger will not result in a substantial lessening of competition in console gaming services because the cost to Microsoft of withholding Call of Duty from PlayStation would outweigh any gains from taking such action," Martin Coleman, who is reviewing the acquisition on behalf of the U.K.'s competition authority, said in a . He was referring to concerns that Microsoft would make the games exclusive to its console (the Xbox) following the acquisition and bar Sony's PlayStation from having access to them in the future. statement Call of Duty Coleman's comment makes sense! is the best-selling first-person shooter video game in the world, and limiting past, current, and future titles from appearing on Sony's PlayStation — which, to be clear, 70% of the global console market —  would only hurt Microsoft. Call of Duty captures For now, investors are with what they have, with Activision Blizzard Inc.'s stock price shooting up on Friday: happy Microsoft is still not out of the weeds, though, since the CMA may change its position again ahead of a final decision expected on April 26. Of concern is also Microsoft's dominance in the "cloud gaming market," i.e. the Xbox Game Pass, which gives gamers to high-quality games in exchange for a nominal fee every month. "Our provisional view that this deal raises concerns in the cloud gaming market is not affected by today’s announcement. Our investigation remains on course for completion by the end of April," Coleman said. access The CMA still believes the deal could harm competition there, perhaps because Microsoft could refuse to allow Call of Duty to appear on Sony's Game Pass competitor PS Plus. Microsoft has, of course, denied such claims and instead suggested entering a deal with Sony that would guarantee that games would appear on its competitor's platforms for a 10-year period — sufficient time for Sony to develop its own shooter, . Call of Duty should they deem it necessary to do so Elsewhere, the U.S. Federal Trade Commission is still seeking to the merger over concerns that the Xbox maker would "would gain control of top video game franchises, enabling it to harm competition in high-performance gaming consoles and subscription services by denying or degrading rivals’ access to its popular content." block Microsoft continues steaming ahead. The company ranked #71 on HackerNoon's this week. Tech Company Rankings 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe to receive the newsletter in your inbox every Tuesday! here In Other News.. 📰 Elon Musk thinks , which ranked #17 this week, is less than half of what he paid for the social media company. Twitter worth Microsoft does not want its competitors to its internet-search data to train their own AI products. use CEO Tim Cook is China, and can't stop saying nice things about the country 🥲. Apple ranked #7 this week. Apple visiting Intel co-founder Gordon Moore has . 😞 passed away And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon Featured image created using Stable Diffusion 2.1 using the following prompt: “Microsoft's XBox playing on a large screen TV”

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