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In a podcast conversation between Kyriakos, the CEO of Terra, and former professional cyclist Lance Armstrong, they discussed various topics related to the sport of cycling and its future. Armstrong shared his thoughts on the importance of data and technology in the sport and how it has changed since he was a professional cyclist. https://youtu.be/Yi3ZJVVpPrM?embedable=true One of Armstrong's main points was the democratization of data and technology in cycling. He talked about how when he was a professional cyclist, only the top athletes had access to data and technology to track their performance. However, now with the advent of wearables and other technology, even amateur athletes can track and analyze their performance data. This has led to a democratization of the sport, making it more accessible to the everyday warrior. Armstrong also discussed data's importance in monitoring and understanding an athlete’s body. He talked about how when he was a professional cyclist, he would wear a heart rate monitor during time trials to track his heart rate. He even shared a story of a time when his heart rate never dropped below 200 beats per minute during an hour-long time trial. This level of monitoring and understanding of an athlete’s body is now possible for anyone with access to wearables and other technology. One of the key areas that Armstrong discussed was the use of data and technology to optimize performance. He talked about how data can be used to track things like heart rate variability, core temperature, and resting heart rate, which can all be used to optimize an athlete’s performance. He also discussed the potential for data to be used in other areas, such as nutrition and sleep, to help optimize an athlete’s overall performance. Another important point that Armstrong discussed was the potential for data and technology to be used for telemedicine. He talked about how data from wearables and other technology can be used to track an individual’s health in real time and over time. This can be used to provide doctors with valuable information about an individual’s health and can be used to prevent and treat illnesses. Kyriakos also asked Armstrong what he thought the future of cycling would look like in 10 years. Armstrong responded that he thinks the sport of cycling should not aspire to be an Olympic sport, but should instead aim to become a major professional sport like the NFL or Formula One. He also said that he thinks in 10 years, the Tour de France, which currently dominates the sport, will be owned by someone other than the French and will have a new set of eyes and perspectives that will help to evolve and grow the sport. Overall, the conversation highlighted the importance of data and technology in the sport of cycling, both in terms of understanding an athlete’s body and optimizing performance, as well as its potential for use in telemedicine. Armstrong also discussed his vision for the future of cycling, where it should aim to become a major professional sport rather than just an Olympic sport. Also published here.

Cycling's Tech-Driven Future: A Podcast with Lance Armstrong

Source There are various categories of numbers on the basis of some set of rules like, if a number is divisible by two then it is an even number otherwise it will be an odd number. There could be more possible categories like prime number, composite number, twin prime number, and there are many more. Among these all there are special types of numbers known as Armstrong Numbers. For a number, if the sum of all digits to their raised power equal to the length of the number is equal to the number itself then that number will be an armstrong number. Let's take an example of 407: -> Length of the 407 is 3 and if we take sum of all of its digit to raised power 3 that is . As we get the result equal to , which is our initial number that means is an armstrong number. 4*4*4 + 0*0*0 + 7*7*7 = 64 + 0 + 343 = 407 407 407 We will discuss two algorithms: To find a given number is an armstrong number or not. Find the number of armstrong numbers between a given range. A number is an Armstrong number or not Steps for the algorithm: Find the length of the given number using a while loop. Create a variable to store the number of digits in the given number. Run while loop until the current number becomes zero and keep dividing the current number by 10 and increasing the count variable to count the number of digits in the number. Now create a variable to store the sum of powers of each digit and initialize it with zero. Run a while loop until the current number becomes zero and keep dividing the current number by 10. In this process, the last digit of the current number can be obtained by using current_number % 10. Adding the last_digit raised to the power length of the number results in a variable that is created to store the total sum. At last, check if the total sum is equal to the given number then the given number is the Armstrong number otherwise not. Let’s take an example to implement the above given approach for better understanding: #include <math.h>
#include <stdio.h>
 
int main(){
  // initial number 
  int n = 407;
  
  // creating extra variable to store initial number 
  int temp_n = n;
 
  // store the number of digits of n in count
  int count = 0;

  while(temp_n)
  {
      temp_n = temp_n / 10; // keep dividing by 10
      count++;
  }

  // as temp_n becomes zero again assigning it n 
  temp_n = n;

  // finding the required sum 
  int sum = 0;
  while(temp_n)
  {
      // a contains the last digit
      int a = temp_n % 10;
     
      // store the sum of the power of individual digits in variable sum
      sum += pow(a, count);
     
      temp_n = temp_n / 10; // keep dividing 
  }
   
  // if sum is equal to n, the number is an Armstrong number
  if (sum == n)
  {
    printf("%d is an Armstrong number.", n);
  }
  else
  {
    printf("%d is not an Armstrong number.", n);
  }
  return 0;
} Output: 407 is an Armstrong number Time Complexity In the above approach, we keep dividing the current number by until it becomes zero, that is times. After that for every digit we raised their power to the number of digits, that is . 10 log10(N) log10(N) So final complexity if . O(log10(N))2 Space Complexity In the above approach, we haven’t used extra space. So space complexity is . O(1) Count of Armstrong number in given Range Steps for the algorithm: First, we will implement a for loop in between the given range. We will create a function to check whether the current number is the Armstrong number or not. In the function, we will implement the approach which we have discussed above. At each iteration in for loop, we will simply pass the current number and if it is an Armstrong number then we will print it as an Armstrong number. Also, we are going to maintain the count of the total number of armstrong numbers. Lets see an example on the above given approach for better understanding: #include <math.h>
#include <stdio.h>
 
// function to check whether number is Armstrong or not 
bool is_armstrong(int n)
{
    // creating extra variable to store initial number 
    int temp_n = n;
   
    // store the number of digits of n in count
    int count = 0;

    while(temp_n)
    {
        temp_n = temp_n / 10; // keep dividing by 10
        count++;
    }

    // as temp_n becomes zero again assigning it n 
    temp_n = n;

    // finding the required sum 
    int sum = 0;
    while(temp_n)
    {
        // a contains the last digit
        int a = temp_n % 10;
       
        // store the sum of the power of individual digits in variable sum
        sum += pow(a, count);
       
        temp_n = temp_n / 10; // keep dividing 
    }
     
    // if sum is equal to n, return true
    if (sum == n)
    {
        return true;
    }
    else
    {
        return false;
    }
}

int main(){
  int start = 100, end = 1000;

  printf("Print all Armstrong numbers between %d and %d:\n",start,end);
 
  // for checking each number is Armstrong number or not, traversing a loop over them

  // alos, we will maintain count of number of Armstrong number 
  int count = 0;

  for(int i = start; i <= end;i++){
      // calling function to check whether current number is Armstrong or not 

      if(is_armstrong(i))
      {
          // increase the count 
          count++;
          printf("Armstrong number is:%d\n", i);
      }
  }
  printf("Total %d number is an Armstrong number between 1 to 1000.", count );
 
  return 0;
} Output: Print all Armstrong numbers between 100 and 1000:
Armstrong number is:370
Armstrong number is:371
Armstrong number is:407
Total 3 number is an Armstrong number between 1 to 1000. Time Complexity In the above approach, we iterate over all of the numbers which take time where is the number of elements in the range. Also, as we have seen in the above section, for number , time complexity to check whether it is an armstrong number or not, takes . O(N) N M O(log10(M))2 So final complexity if N*O(log10(N))2. Space Complexity In the above approach, we haven’t used extra space. So space complexity is . O(1) Conclusion For a number, if the sum of all digits to their raised power equal to the length of the number is equal to the number itself then that number will be an armstrong number. Time complexity to find a number is an armstrong number or not is . O(log10(N))2 Space complexity to find a number is armstrong or not is . O(1) Zero is also an Armstrong number. You may refer to this to cover all the concepts related to Armstrong numbers in c in detail. article by Scaler

Armstrong Number in C

Towards the Automation of Book Typesetting: Acknowledgments and References

NeRF Editing and Inpainting Techniques: Conclusion and References

The Hidden Hand: Is Paper Bitcoin Suppressing BTC Prices?

লুকানো হাত: কাগজের বিটকয়েন কি বিটিসি দামকে দমন করছে?

Die verborgene Hand: Drückt Papier-Bitcoin den BTC-Preis?

La mano oculta: ¿El Bitcoin de papel está suprimiendo los precios de BTC?

La main cachée : le papier Bitcoin supprime-t-il les prix du BTC ?

छिपा हुआ हाथ: क्या कागजी बिटकॉइन बीटीसी की कीमतों को दबा रहा है?

隠れた手：紙のビットコインは BTC の価格を抑制しているのか？

숨겨진 손: 종이 비트코인이 BTC 가격을 억제하고 있나요?

A mão oculta: o Bitcoin de papel está suprimindo os preços do BTC?

Скрытая рука: бумажный биткойн подавляет цены на BTC?

Gizli El: Kağıt Bitcoin BTC Fiyatlarını Baskılıyor mu?

Bàn tay ẩn giấu: Bitcoin giấy có đang cản trở giá BTC không?

隐藏的手：纸质比特币正在抑制 BTC 价格吗？

There’s actually a lot of audiovisual media about cryptocurrencies and related technology that you can enjoy from your couch. Through analyses and unique tales, these films and documentaries address topics such as the history of Bitcoin, the evolution of the crypto market, its economic and social implications, and the legal and regulatory challenges. Besides all the learning stuff, they also give us unique perspectives about the crypto world, and amazing fictional stories full of action, mystery, drama, and more. Well, who doesn’t want to see a good story about a big heist or a death mystery? Let’s check ahead some crypto stories now available on several streaming platforms. The Rise and Rise of Bitcoin (2014) This documentary first premiered at the Tribeca Film Festival in New York in 2014. It was directed by the American Nicholas Mross, and it follows the life of Daniel Mross, a 35-year-old programmer who, in addition to his work and family life, has a great passion for Bitcoin. Since discovering this cryptocurrency in 2011, he’s been fascinated by everything surrounding it. Along with his brother Nicholas, who’s also the director, they decided to start filming this documentary due to their strong belief that Bitcoin could be the future of the economy. On his journey, Daniel meets and interviews several pioneers to explore this decentralized technology. Over time, it becomes evident that not everyone involved in Bitcoin managed to overcome the initial challenges. The piece received a 67% score on Rotten Tomatoes, along with 82% of the audience score. Besides the Mross brothers, the cast includes Daniel’s wife, Eliza Mross; some Bitcoin entrepreneurs such as Gabriel Sukenik and Erik Voorhees, as well as crypto-personalities and consultants like Gavin Andresen, Brian Armstrong, Charlie Shrem, Alex Waters, and Jered Kenna. Currently, the documentary is available on Prime Video, Fandango at Home, and YouTube. https://www.youtube.com/watch?v=gcwnpvODd-8&embedable=true Bitcoin Heist (2016) Directed by the Vietnamese-American filmmaker Ham Tran, this action movie was released in Vietnam in 2016. The film's main cast includes Kate Nhung, Thanh Pham, Petey Majik Nguyen, Suboi, Veronica Ngo, and Nhung Kate. It immerses us in a plot where a group of undercover agents team up to capture the enigmatic criminal known as "The Ghost." Combining their unique skills, they embark on a mission filled with intrigue and technology to bring this cryptocurrency heist expert to justice. The movie offers a mix of suspense, action, and touches of comedy, providing an exciting look at the world of digital crimes. It received a 93% audience score on Rotten Tomatoes and grossed a total of $562,919 in its country. Currently, this film is available on Prime Video and Fandango at Home. https://www.youtube.com/watch?v=LCqUB58LnhE&embedable=true StartUp (2016-2018) This one is an American crime drama series created by Ben Ketai and premiered in 2016 on the video streaming platform Crackle. The main cast, led by Adam Brody, Martin Freeman, Edi Gathegi, Otmara Marrero, Ron Perlman, Mira Sorvino, and Addison Timlin, captivated the audience. After its second season on November 15, 2017, the series was greenlit for a third installment that premiered on November 1, 2018. However, it wasn’t renewed for a fourth season. The story narrates the rise of GenCoin, a new digital coin created with dubious purposes. The plot revolves around three unconventional individuals who don’t necessarily fit the stereotype of tech entrepreneurs, and a corrupt FBI agent determined to sabotage their efforts at all costs. With a backdrop of organized crime, corruption, and ethical challenges, the series explores the complexities of starting a business in the digital world, where the lines between good and evil blur, and moral decisions are constantly tested. StartUp received an 89% audience score on Rotten Tomatoes. Currently, it can be found on Prime Video and Fandango at Home. https://www.youtube.com/watch?v=yCf_MpXAXK8&embedable=true Trust Machine: The Story of Blockchain (2018) This documentary, written and directed by British-American actor Alex Winter, was released in 2018 by SingularDTV. The film recounts the evolution of Bitcoin from its beginnings to its first encounter with fame through the infamous Silk Road market on the Darknet. It also examines the government's resistance to the idea of a decentralized currency, exemplified in the cases of hacktivists Lauri Love and Aaron Swartz. Trust Machine explores the fascinating world of Distributed Ledger Technology (DLT) and its potential to transform various aspects of our society. That includes its potential applications in industries such as banking, healthcare, and logistics, where it offers a decentralized and secure system for data exchange and transaction recording. Additionally, it also addresses the public perception of cryptocurrencies as tools for illicit activities. It received a 90% score on Rotten Tomatoes and a 79% audience score. Currently, it can be found on Fandango at Home and on YouTube. https://www.youtube.com/watch?v=hvohNgVWKvw&embedable=true Crypto (2019) Maybe not the most popular option, but likely the first crypto movie backed by a heavyweight production company, such as Lionsgate. Released in 2019 and directed by John Stalberg Jr., it also features a notable cast including Beau Knapp, Alexis Bledel, Luke Hemsworth, and Kurt Russell. The film combines elements of suspense, crime drama, and money laundering through the world of cryptocurrencies. The plot follows a young Wall Street anti-money laundering agent who is demoted by his company to a small branch in his hometown and ends up investigating a series of suspicious financial transactions. As he delves deeper into the case, he uncovers a giant network of corruption and organized crime using cryptocurrencies to cover their illicit activities. Crypto offers an intriguing and suspenseful look at the darker side of the modern financial world. However, its release was limited to Turkiye, and it received a 17% score on Rotten Tomatoes, plus a 55% audience score. You can be the judge there. Currently, it’s available on Prime Video. https://www.youtube.com/watch?v=TxJPaeX2wp0&embedable=true Cryptopia: Bitcoin, Blockchains and the Future of the Internet (2020) This award-winning documentary, released in 2020, explores the potential impact of cryptocurrencies and DLT. Directed by Torsten Hoffmann and produced by Bitfilm Production, the film examines how these innovations are shaping the future of the Internet and redefining concepts such as money, privacy, and online security. Cryptopia offers an insightful and enlightening view of the ongoing digital revolution, highlighting the opportunities and challenges these new technologies pose for society as a whole. The cast includes prominent figures in crypto, such as Andreas Antonopoulos, Vitalik Buterin, Charlie Lee, and Roger Ver. The film premiered at various film festivals worldwide before becoming available on streaming platforms. It received an 82% audience score on Rotten Tomatoes, and it’s currently available on Prime Video and YouTube. https://www.youtube.com/watch?v=aMLSXH8TAdw&embedable=true Trust No One: The Hunt for the Crypto King (2022) This title is an exclusive Netflix documentary directed by Luke Sewell and released in March 2022. The plot follows a group of cryptocurrency investors who were affected by the collapse of the Canadian crypto exchange QuadrigaCX and the supposed death of his founder, Gerald Cotten, which caused millions in losses. About this, the documentary examines Cotten's strange and untimely death in India and the subsequent financial crisis that left thousands of investors without access to their funds. The cast includes testimonies from cryptocurrency experts, lawyers, and victims affected by the scandal. It received a 34% audience score on Rotten Tomatoes, but it could be a thought-provoking starting point to understand the Gerald Cotten and QuadrigaCX case. Maybe fraud? https://www.youtube.com/watch?v=vW2BPQ15OSw&embedable=true Now, time to relax and pick your favorites! If you want to make your own story in the crypto world, don’t forget to visit Obyte. From human-readable smart contracts to customized coins, we have everything there. Featured Vector Image by gstudioimagen / Freepik There’s actually a lot of audiovisual media about cryptocurrencies and related technology that you can enjoy from your couch. Through analyses and unique tales, these films and documentaries address topics such as the history of Bitcoin, the evolution of the crypto market, its economic and social implications, and the legal and regulatory challenges. Besides all the learning stuff, they also give us unique perspectives about the crypto world, and amazing fictional stories full of action, mystery, drama, and more. Well, who doesn’t want to see a good story about a big heist or a death mystery? Let’s check ahead some crypto stories now available on several streaming platforms. The Rise and Rise of Bitcoin (2014) The Rise and Rise of Bitcoin (2014) This documentary first premiered at the Tribeca Film Festival in New York in 2014. It was directed by the American Nicholas Mross, and it follows the life of Daniel Mross, a 35-year-old programmer who, in addition to his work and family life, has a great passion for Bitcoin. Since discovering this cryptocurrency in 2011, he’s been fascinated by everything surrounding it. Along with his brother Nicholas, who’s also the director, they decided to start filming this documentary due to their strong belief that Bitcoin could be the future of the economy . On his journey, Daniel meets and interviews several pioneers to explore this decentralized technology. Over time, it becomes evident that not everyone involved in Bitcoin managed to overcome the initial challenges. Along with his brother Nicholas, who’s also the director, they decided to start filming this documentary due to their strong belief that Bitcoin could be the future of the economy The piece received a 67% score on Rotten Tomatoes , along with 82% of the audience score. Besides the Mross brothers, the cast includes Daniel’s wife, Eliza Mross; some Bitcoin entrepreneurs such as Gabriel Sukenik and Erik Voorhees, as well as crypto-personalities and consultants like Gavin Andresen, Brian Armstrong, Charlie Shrem, Alex Waters, and Jered Kenna. Currently, the documentary is available on Prime Video, Fandango at Home, and YouTube . Rotten Tomatoes Rotten Tomatoes YouTube YouTube https://www.youtube.com/watch?v=gcwnpvODd-8&embedable=true https://www.youtube.com/watch?v=gcwnpvODd-8&embedable=true Bitcoin Heist (2016) Directed by the Vietnamese-American filmmaker Ham Tran, this action movie was released in Vietnam in 2016. The film's main cast includes Kate Nhung, Thanh Pham, Petey Majik Nguyen, Suboi, Veronica Ngo, and Nhung Kate. It immerses us in a plot where a group of undercover agents team up to capture the enigmatic criminal known as "The Ghost." It immerses us in a plot where a group of undercover agents team up to capture the enigmatic criminal known as "The Ghost." Combining their unique skills, they embark on a mission filled with intrigue and technology to bring this cryptocurrency heist expert to justice. The movie offers a mix of suspense, action, and touches of comedy, providing an exciting look at the world of digital crimes. It received a 93% audience score on Rotten Tomatoes and grossed a total of $562,919 in its country. Currently, this film is available on Prime Video and Fandango at Home. Rotten Tomatoes Rotten Tomatoes grossed grossed https://www.youtube.com/watch?v=LCqUB58LnhE&embedable=true https://www.youtube.com/watch?v=LCqUB58LnhE&embedable=true StartUp (2016-2018) This one is an American crime drama series created by Ben Ketai and premiered in 2016 on the video streaming platform Crackle. The main cast, led by Adam Brody, Martin Freeman, Edi Gathegi, Otmara Marrero, Ron Perlman, Mira Sorvino, and Addison Timlin, captivated the audience. After its second season on November 15, 2017, the series was greenlit for a third installment that premiered on November 1, 2018. However, it wasn’t renewed for a fourth season. The story narrates the rise of GenCoin, a new digital coin created with dubious purposes. The plot revolves around three unconventional individuals who don’t necessarily fit the stereotype of tech entrepreneurs, and a corrupt FBI agent determined to sabotage their efforts at all costs. With a backdrop of organized crime, corruption, and ethical challenges, the series explores the complexities of starting a business in the digital world, where the lines between good and evil blur, and moral decisions are constantly tested. The story narrates the rise of GenCoin, a new digital coin created with dubious purposes. StartUp received an 89% audience score on Rotten Tomatoes . Currently, it can be found on Prime Video and Fandango at Home. Rotten Tomatoes Rotten Tomatoes https://www.youtube.com/watch?v=yCf_MpXAXK8&embedable=true https://www.youtube.com/watch?v=yCf_MpXAXK8&embedable=true Trust Machine: The Story of Blockchain (2018) This documentary, written and directed by British-American actor Alex Winter, was released in 2018 by SingularDTV. The film recounts the evolution of Bitcoin from its beginnings to its first encounter with fame through the infamous Silk Road market on the Darknet. It also examines the government's resistance to the idea of a decentralized currency, exemplified in the cases of hacktivists Lauri Love and Aaron Swartz. Trust Machine explores the fascinating world of Distributed Ledger Technology (DLT) and its potential to transform various aspects of our society. That includes its potential applications in industries such as banking, healthcare, and logistics, where it offers a decentralized and secure system for data exchange and transaction recording. Trust Machine explores the fascinating world of Distributed Ledger Technology (DLT) and its potential to transform various aspects of our society. Additionally, it also addresses the public perception of cryptocurrencies as tools for illicit activities. It received a 90% score on Rotten Tomatoes and a 79% audience score. Currently, it can be found on Fandango at Home and on YouTube . Rotten Tomatoes Rotten Tomatoes YouTube YouTube https://www.youtube.com/watch?v=hvohNgVWKvw&embedable=true https://www.youtube.com/watch?v=hvohNgVWKvw&embedable=true Crypto (2019) Maybe not the most popular option, but likely the first crypto movie backed by a heavyweight production company, such as Lionsgate. Released in 2019 and directed by John Stalberg Jr., it also features a notable cast including Beau Knapp, Alexis Bledel, Luke Hemsworth, and Kurt Russell. The film combines elements of suspense, crime drama, and money laundering through the world of cryptocurrencies. The plot follows a young Wall Street anti-money laundering agent who is demoted by his company to a small branch in his hometown and ends up investigating a series of suspicious financial transactions. As he delves deeper into the case, he uncovers a giant network of corruption and organized crime using cryptocurrencies to cover their illicit activities. The film combines elements of suspense, crime drama, and money laundering through the world of cryptocurrencies. Crypto offers an intriguing and suspenseful look at the darker side of the modern financial world. However, its release was limited to Turkiye, and it received a 17% score on Rotten Tomatoes , plus a 55% audience score. You can be the judge there. Currently, it’s available on Prime Video. was limited was limited on Rotten Tomatoes on Rotten Tomatoes https://www.youtube.com/watch?v=TxJPaeX2wp0&embedable=true https://www.youtube.com/watch?v=TxJPaeX2wp0&embedable=true Cryptopia: Bitcoin, Blockchains and the Future of the Internet (2020) Cryptopia: Bitcoin, Blockchains and the Future of the Internet (2020) This award-winning documentary, released in 2020, explores the potential impact of cryptocurrencies and DLT. Directed by Torsten Hoffmann and produced by Bitfilm Production, the film examines how these innovations are shaping the future of the Internet and redefining concepts such as money, privacy, and online security. Cryptopia offers an insightful and enlightening view of the ongoing digital revolution, highlighting the opportunities and challenges these new technologies pose for society as a whole. The cast includes prominent figures in crypto, such as Andreas Antonopoulos, Vitalik Buterin, Charlie Lee, and Roger Ver. The film premiered at various film festivals worldwide before becoming available on streaming platforms. It received an 82% audience score on Rotten Tomatoes , and it’s currently available on Prime Video and YouTube . Cryptopia offers an insightful and enlightening view of the ongoing digital revolution, highlighting the opportunities and challenges these new technologies pose for society as a whole. Rotten Tomatoes Rotten Tomatoes YouTube YouTube https://www.youtube.com/watch?v=aMLSXH8TAdw&embedable=true https://www.youtube.com/watch?v=aMLSXH8TAdw&embedable=true Trust No One: The Hunt for the Crypto King (2022) This title is an exclusive Netflix documentary directed by Luke Sewell and released in March 2022. The plot follows a group of cryptocurrency investors who were affected by the collapse of the Canadian crypto exchange QuadrigaCX and the supposed death of his founder, Gerald Cotten, which caused millions in losses. About this, the documentary examines Cotten's strange and untimely death in India and the subsequent financial crisis that left thousands of investors without access to their funds. The cast includes testimonies from cryptocurrency experts, lawyers, and victims affected by the scandal. It received a 34% audience score on Rotten Tomatoes , but it could be a thought-provoking starting point to understand the Gerald Cotten and QuadrigaCX case. Maybe fraud? examines Cotten's strange and untimely death in India and the subsequent financial crisis that left thousands of investors without access to their funds. Rotten Tomatoes Rotten Tomatoes https://www.youtube.com/watch?v=vW2BPQ15OSw&embedable=true https://www.youtube.com/watch?v=vW2BPQ15OSw&embedable=true Now, time to relax and pick your favorites! If you want to make your own story in the crypto world, don’t forget to visit Obyte . From human-readable smart contracts to customized coins , we have everything there. Obyte Obyte customized coins customized coins Featured Vector Image by gstudioimagen / Freepik Featured Vector Image by gstudioimagen / Freepik Freepik Freepik

7 Must-Watch Crypto Films and Documentaries

7 অবশ্যই ক্রিপ্টো ফিল্ম এবং ডকুমেন্টারি দেখতে হবে

7 Krypto-Filme und -Dokumentationen, die man gesehen haben muss

7 películas y documentales sobre criptomonedas que debes ver

7 films et documentaires cryptographiques à ne pas manquer

7 अवश्य देखें क्रिप्टो फ़िल्में और वृत्तचित्र

必見の暗号映画とドキュメンタリー 7 選

꼭 봐야 할 암호화폐 영화 및 다큐멘터리 7선

7 filmes e documentários criptográficos imperdíveis

7 крипто-фильмов и документальных фильмов, которые обязательно нужно посмотреть

Mutlaka İzlenmesi Gereken 7 Kripto Filmi ve Belgeseli

7 bộ phim và phim tài liệu về tiền điện tử phải xem

7 部必看的加密电影和纪录片

Authors:
(1) Kun Lan, University of Science and Technology of China;
(2) Haoran Li, University of Science and Technology of China;
(3) Haolin Shi, University of Science and Technology of China;
(4) Wenjun Wu, University of Science and Technology of China;
(5) Yong Liao, University of Science and Technology of China;
(6) Lin Wang, AI Thrust, HKUST(GZ);
(7) Pengyuan Zhou, University of Science and Technology of China. Table of Links Abstract and 1. Introduction 2. Related Work 3. Method and 3.1. Point-Based rendering and Semantic Information Learning 3.2. Gaussian Clustering and 3.3. Gaussian Filtering 4. Experiment 4.1. Setups, 4.2. Result and 4.3. Ablations 5. Conclusion and References 5. CONCLUSION We propose a 3D Gaussian segmentation method guided by 2D segmentation maps, attaching a probability distribution vector for each 3D Gaussian on various categories to enable the segmentation of the majority of 3D Gaussians in the scene. Meanwhile, we employ KNN clustering to utilize the spatial continuity of objects, ensuring that nearby 3D Gaussians belong to the same category. Additionally, optional statistical filtering is used to help remove those 3D Gaussians that are incorrectly segmented. As an initial step in 3D understanding and editing, this method has a wide range of potential applications in downstream tasks. We demonstrate the effectiveness of our method on common NeRF datasets. References [1] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis, “3D Gaussian ¨ Splatting for Real-Time Radiance Field Rendering,” ACM Transactions on Graphics, vol. 42, no. 4, pp. 1–14, July 2023. [2] Weiping Liu, Jia Sun, Wanyi Li, Ting Hu, and Peng Wang, “Deep learning on point clouds and its application: A survey,” Sensors, vol. 19, no. 19, 2019. [3] Dawar Khan, Alexander Plopski, Yuichiro Fujimoto, Masayuki Kanbara, Gul Jabeen, Yongjie Jessica Zhang, Xiaopeng Zhang, and Hirokazu Kato, “Surface remeshing: A systematic literature review of methods and research directions,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 3, pp. 1680–1713, 2022. [4] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. [5] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Commun. ACM, vol. 65, no. 1, pp. 99–106, dec 2021. [6] Johannes L. Schonberger and Jan-Michael Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. [7] Mingqiao Ye, Martin Danelljan, Fisher Yu, and Lei Ke, “Gaussian Grouping: Segment and Edit Anything in 3D Scenes,” arXiv e-prints, p. arXiv:2312.00732, Dec. 2023. [8] Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, and Qi Tian, “Segment Any 3D Gaussians,” arXiv e-prints, p. arXiv:2312.00860, Dec. 2023. [9] Sosuke Kobayashi, Eiichi Matsumoto, and Vincent Sitzmann, “Decomposing nerf for editing via feature field distillation,” in Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. 2022, vol. 35, pp. 23311–23330, Curran Associates, Inc. [10] Rahul Goel, Dhawal Sirikonda, Saurabh Saini, and P. J. Narayanan, “Interactive segmentation of radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 4201–4211. [11] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng, “DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation,” arXiv eprints, p. arXiv:2309.16653, Sept. 2023. [12] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang, “GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models,” arXiv e-prints, p. arXiv:2310.08529, Oct. 2023. [13] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer, “High-resolution im- ¨ age synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 10684–10695. [14] Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang, “4D Gaussian Splatting for RealTime Dynamic Scene Rendering,” arXiv e-prints, p. arXiv:2310.08528, Oct. 2023. [15] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick, “Segment Anything,” ´ arXiv e-prints, p. arXiv:2304.02643, Apr. 2023. [16] Bing Wang, Lu Chen, and Bo Yang, “DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images,” arXiv e-prints, p. arXiv:2208.07227, Aug. 2022. [17] Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui, “Learning object-compositional neural radiance field for editable scene rendering,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021, pp. 13779–13788. [18] Youtan Yin, Zhoujie Fu, Fan Yang, and Guosheng Lin, “OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation with Neural Radiance Fields,” arXiv e-prints, p. arXiv:2305.10503, May 2023. [19] Jesus Zarzar, Sara Rojas, Silvio Giancola, and Bernard Ghanem, “SegNeRF: 3D Part Segmentation with Neural Radiance Fields,” arXiv e-prints, p. arXiv:2211.11215, Nov. 2022. [20] Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis, Jonathan Kelly, Marcus A. Brubaker, Igor Gilitschenski, and Alex Levinshtein, “Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 20669–20679. [21] Zhenxing MI and Dan Xu, “Switch-neRF: Learning scene decomposition with mixture of experts for largescale neural radiance fields,” in The Eleventh International Conference on Learning Representations, 2023. [22] Mathilde Caron, Hugo Touvron, Ishan Misra, Herve´ Jegou, Julien Mairal, Piotr Bojanowski, and Armand ´ Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021, pp. 9650–9660. [23] Yuying Hao, Yi Liu, Yizhou Chen, Lin Han, Juncai Peng, Shiyu Tang, Guowei Chen, Zewu Wu, Zeyu Chen, and Baohua Lai, “Eiseg: An efficient interactive segmentation tool based on paddlepaddle,” arXiv e-prints, pp. arXiv–2210, 2022. [24] Ben Mildenhall, Pratul P. Srinivasan, Rodrigo OrtizCayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,” ACM Trans. Graph., vol. 38, no. 4, jul 2019. [25] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman, “Mip-nerf 360: Unbounded anti-aliased neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 5470–5479. This paper is available on arxiv under CC 4.0 license. Authors: (1) Kun Lan, University of Science and Technology of China; (2) Haoran Li, University of Science and Technology of China; (3) Haolin Shi, University of Science and Technology of China; (4) Wenjun Wu, University of Science and Technology of China; (5) Yong Liao, University of Science and Technology of China; (6) Lin Wang, AI Thrust, HKUST(GZ); (7) Pengyuan Zhou, University of Science and Technology of China. Authors: Authors: (1) Kun Lan, University of Science and Technology of China; (2) Haoran Li, University of Science and Technology of China; (3) Haolin Shi, University of Science and Technology of China; (4) Wenjun Wu, University of Science and Technology of China; (5) Yong Liao, University of Science and Technology of China; (6) Lin Wang, AI Thrust, HKUST(GZ); (7) Pengyuan Zhou, University of Science and Technology of China. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. Method and 3.1. Point-Based rendering and Semantic Information Learning 3. Method and 3.1. Point-Based rendering and Semantic Information Learning 3.2. Gaussian Clustering and 3.3. Gaussian Filtering 3.2. Gaussian Clustering and 3.3. Gaussian Filtering 4. Experiment 4.1. Setups, 4.2. Result and 4.3. Ablations 4.1. Setups, 4.2. Result and 4.3. Ablations 5. Conclusion and References 5. Conclusion and References 5. CONCLUSION We propose a 3D Gaussian segmentation method guided by 2D segmentation maps, attaching a probability distribution vector for each 3D Gaussian on various categories to enable the segmentation of the majority of 3D Gaussians in the scene. Meanwhile, we employ KNN clustering to utilize the spatial continuity of objects, ensuring that nearby 3D Gaussians belong to the same category. Additionally, optional statistical filtering is used to help remove those 3D Gaussians that are incorrectly segmented. As an initial step in 3D understanding and editing, this method has a wide range of potential applications in downstream tasks. We demonstrate the effectiveness of our method on common NeRF datasets. References [1] Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuhler, and George Drettakis, “3D Gaussian ¨ Splatting for Real-Time Radiance Field Rendering,” ACM Transactions on Graphics, vol. 42, no. 4, pp. 1–14, July 2023. [2] Weiping Liu, Jia Sun, Wanyi Li, Ting Hu, and Peng Wang, “Deep learning on point clouds and its application: A survey,” Sensors, vol. 19, no. 19, 2019. [3] Dawar Khan, Alexander Plopski, Yuichiro Fujimoto, Masayuki Kanbara, Gul Jabeen, Yongjie Jessica Zhang, Xiaopeng Zhang, and Hirokazu Kato, “Surface remeshing: A systematic literature review of methods and research directions,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 3, pp. 1680–1713, 2022. [4] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. [5] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Commun. ACM, vol. 65, no. 1, pp. 99–106, dec 2021. [6] Johannes L. Schonberger and Jan-Michael Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. [7] Mingqiao Ye, Martin Danelljan, Fisher Yu, and Lei Ke, “Gaussian Grouping: Segment and Edit Anything in 3D Scenes,” arXiv e-prints, p. arXiv:2312.00732, Dec. 2023. [8] Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, and Qi Tian, “Segment Any 3D Gaussians,” arXiv e-prints, p. arXiv:2312.00860, Dec. 2023. [9] Sosuke Kobayashi, Eiichi Matsumoto, and Vincent Sitzmann, “Decomposing nerf for editing via feature field distillation,” in Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. 2022, vol. 35, pp. 23311–23330, Curran Associates, Inc. [10] Rahul Goel, Dhawal Sirikonda, Saurabh Saini, and P. J. Narayanan, “Interactive segmentation of radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 4201–4211. [11] Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng, “DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation,” arXiv eprints, p. arXiv:2309.16653, Sept. 2023. [12] Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang, “GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models,” arXiv e-prints, p. arXiv:2310.08529, Oct. 2023. [13] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer, “High-resolution im- ¨ age synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 10684–10695. [14] Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang, “4D Gaussian Splatting for RealTime Dynamic Scene Rendering,” arXiv e-prints, p. arXiv:2310.08528, Oct. 2023. [15] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick, “Segment Anything,” ´ arXiv e-prints, p. arXiv:2304.02643, Apr. 2023. [16] Bing Wang, Lu Chen, and Bo Yang, “DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images,” arXiv e-prints, p. arXiv:2208.07227, Aug. 2022. [17] Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui, “Learning object-compositional neural radiance field for editable scene rendering,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021, pp. 13779–13788. [18] Youtan Yin, Zhoujie Fu, Fan Yang, and Guosheng Lin, “OR-NeRF: Object Removing from 3D Scenes Guided by Multiview Segmentation with Neural Radiance Fields,” arXiv e-prints, p. arXiv:2305.10503, May 2023. [19] Jesus Zarzar, Sara Rojas, Silvio Giancola, and Bernard Ghanem, “SegNeRF: 3D Part Segmentation with Neural Radiance Fields,” arXiv e-prints, p. arXiv:2211.11215, Nov. 2022. [20] Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis, Jonathan Kelly, Marcus A. Brubaker, Igor Gilitschenski, and Alex Levinshtein, “Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 20669–20679. [21] Zhenxing MI and Dan Xu, “Switch-neRF: Learning scene decomposition with mixture of experts for largescale neural radiance fields,” in The Eleventh International Conference on Learning Representations, 2023. [22] Mathilde Caron, Hugo Touvron, Ishan Misra, Herve´ Jegou, Julien Mairal, Piotr Bojanowski, and Armand ´ Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021, pp. 9650–9660. [23] Yuying Hao, Yi Liu, Yizhou Chen, Lin Han, Juncai Peng, Shiyu Tang, Guowei Chen, Zewu Wu, Zeyu Chen, and Baohua Lai, “Eiseg: An efficient interactive segmentation tool based on paddlepaddle,” arXiv e-prints, pp. arXiv–2210, 2022. [24] Ben Mildenhall, Pratul P. Srinivasan, Rodrigo OrtizCayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,” ACM Trans. Graph., vol. 38, no. 4, jul 2019. [25] Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman, “Mip-nerf 360: Unbounded anti-aliased neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 5470–5479. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Effortless 2D-Guided, 3D Gaussian Segmentation: Conclusion and References

**DUBAI, UAE, May 22nd, 2024/Chainwire/--**Kelp DAO, a liquid restaking platform, today announced the closure of a $9 million private sale round, a milestone in its mission to enhance decentralized finance (DeFi) and the restaking landscape. SCB Limited (a proprietary trading firm based in the Bahamas) and Laser Digital (Digital asset arm of Nomura Global) led the round with a combined $3.5 million investment. Other investors in the round were Bankless Ventures, Hypersphere, Draper Dragon, DACM, Cypher Capital, ArkStream, Cluster Capital, GSR, Longhash, Side Door Ventures, NOIA Capital, HTX Ventures, Avid3, ViaBTC Capital, DWF Ventures, Coinseeker and many others. These funds build on more than $850 million in Total Value Locked (TVL) and more than 40,000 active restakers on the platform. The funds will be utilized to enhance the platform's capabilities, expand to other ecosystems like Solana and Bitcoin, and further innovate restaking solutions. The funding round showcases confidence from global investors, with support from several notable angel investors in the crypto community, including Scott, Cofounder of Gitcoin; Alex, CEO Nansen; Sam K, Cofounder Frax; Marc Zeller, Aave Chan Initiative; Saurabh Sharma, Jump Crypto; Amrit, COO Altlayer; Anthony, Cofounder of Swissborg; Winslow Strong, Mr. Block. Dheeraj Borra, Co-founder of Kelp DAO, said, “This fundraise propels our expansion in the market and sharpens our focus on building customer centric solutions. It's truly thrilling to have our investors share that vision.” Both Kelp DAO founders, Amitej Gajjala and Dheeraj Borra, expressed gratitude for the support of investors to drive Kelp DAO towards scaling new heights in restaking solutions.Jez Mohideen, CEO of Laser Digital, added their support for the project, stating “We are very excited to support Kelp DAO and the passionate team in its journey towards building innovative restaking infrastructure solutions.” This enthusiasm was reiterated by Jack Platts, Co-founder of Hypersphere Ventures, saying, “We’re excited by the prospect of restaking enabling more developer experimentation and use cases. Kelp’s proven team and focus on user experience is playing a key role in this restaking’s early success. We look forward to the team executing on its vision of enhancing yields and optionality for ETH stakers.” About Kelp DAO: Kelp DAO is one of the leading liquid restaking protocols with $850M+ in assets under management across 45,000+ restakers. Their liquid restaked token (LRT) was the first to hit the Ethereum mainnet and pioneered liquid restaking with liquid staking tokens. Currently, Kelp DAO facilitates liquid restaking for native ETH and LSTs alike across Ethereum mainnet and 8 L2 networks. About Laser Digital: Backed by Nomura, Laser Digital delivers scalable, robust opportunities across trading, asset management, and ventures. The team works at higher risk management standards, compliance, and commercial viability, all driven by a belief in more responsible crypto innovation. For more information,  please visit: https://www.laserdigital.com/ About SCB Limited: SCB Limited is a Bahamian-based proprietary trading firm and an active participant in the digital asset ecosystem. Contact Head of Communications Archana B archana@kelpdao.xyz This story was distributed as a release by Chainrwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **DUBAI, UAE, May 22nd, 2024/Chainwire/--**Kelp DAO, a liquid restaking platform, today announced the closure of a $9 million private sale round, a milestone in its mission to enhance decentralized finance (DeFi) and the restaking landscape. SCB Limited (a proprietary trading firm based in the Bahamas) and Laser Digital (Digital asset arm of Nomura Global) led the round with a combined $3.5 million investment. Other investors in the round were Bankless Ventures, Hypersphere, Draper Dragon, DACM, Cypher Capital, ArkStream, Cluster Capital, GSR, Longhash, Side Door Ventures, NOIA Capital, HTX Ventures, Avid3, ViaBTC Capital, DWF Ventures, Coinseeker and many others. SCB Limited SCB Limited Laser Digital Laser Digital These funds build on more than $850 million in Total Value Locked (TVL) and more than 40,000 active restakers on the platform. The funds will be utilized to enhance the platform's capabilities, expand to other ecosystems like Solana and Bitcoin, and further innovate restaking solutions. The funding round showcases confidence from global investors, with support from several notable angel investors in the crypto community, including Scott, Cofounder of Gitcoin; Alex, CEO Nansen; Sam K, Cofounder Frax; Marc Zeller, Aave Chan Initiative; Saurabh Sharma, Jump Crypto; Amrit, COO Altlayer; Anthony, Cofounder of Swissborg; Winslow Strong, Mr. Block. Dheeraj Borra, Co-founder of Kelp DAO, said, “This fundraise propels our expansion in the market and sharpens our focus on building customer centric solutions. It's truly thrilling to have our investors share that vision.” Dheeraj Borra, Co-founder of Kelp DAO, said, “This fundraise propels our expansion in the market and sharpens our focus on building customer centric solutions. It's truly thrilling to have our investors share that vision.” Both Kelp DAO founders, Amitej Gajjala and Dheeraj Borra, expressed gratitude for the support of investors to drive Kelp DAO towards scaling new heights in restaking solutions.Jez Mohideen, CEO of Laser Digital, added their support for the project, stating “We are very excited to support Kelp DAO and the passionate team in its journey towards building innovative restaking infrastructure solutions.” CEO of Laser Digital, added their support for the project, stating “We are very excited to support Kelp DAO and the passionate team in its journey towards building innovative restaking infrastructure solutions.” This enthusiasm was reiterated by Jack Platts, Co-founder of Hypersphere Ventures, saying, “We’re excited by the prospect of restaking enabling more developer experimentation and use cases. Kelp’s proven team and focus on user experience is playing a key role in this restaking’s early success. We look forward to the team executing on its vision of enhancing yields and optionality for ETH stakers.” About Kelp DAO: Kelp DAO is one of the leading liquid restaking protocols with $850M+ in assets under management across 45,000+ restakers. Their liquid restaked token (LRT) was the first to hit the Ethereum mainnet and pioneered liquid restaking with liquid staking tokens. Currently, Kelp DAO facilitates liquid restaking for native ETH and LSTs alike across Ethereum mainnet and 8 L2 networks. Kelp DAO Kelp DAO About Laser Digital: Backed by Nomura, Laser Digital delivers scalable, robust opportunities across trading, asset management, and ventures. The team works at higher risk management standards, compliance, and commercial viability, all driven by a belief in more responsible crypto innovation. For more information,  please visit: https://www.laserdigital.com/ https://www.laserdigital.com/ https://www.laserdigital.com/ About SCB Limited: SCB Limited is a Bahamian-based proprietary trading firm and an active participant in the digital asset ecosystem. Contact Head of Communications Archana B archana@kelpdao.xyz This story was distributed as a release by Chainrwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainrwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Kelp DAO Secures $9 Million In Private Sale For Restaking Innovations

Entrepreneurs, Journalists, Researchers: The Ethereum Influencers to Follow to Stay Informed!

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps 6 Conclusion Understanding how recommender systems shape our behaviour is essential to avoid manipulation. In this paper, we investigated the Amazon recommender system concerning whether it has made it harder to change preferences. By analysing the Barrier-to-Exit (Rakova & Chowdhury, 2019) of more than 50,000 users, we found a highly significant growth in Barrier-to-Exit over time, which indicates that it has indeed become harder to change preferences for the analysed users. However, sampling bias induced by the calculation of Barrier-to-Exit makes it difficult to draw conclusions about the general population of Amazon customers. This highlights the dilemma of portability in measuring socio-technical systems (Selbst et al., 2019): accurately evaluating a concept like ”changing preferences” requires adapting to the context of the system, which makes it more difficult to generalise (and compare) to other systems. Comparing recommender systems is necessary for ensuring that these respect human autonomy (Varshney, 2020) and live up to new regulations such as the EU AI Act (Kop, 2021). Further work, should aim to create auditing procedures and metrics that allow third parties to measure potential preference manipulation in a way that fits within the context of the industry and allows for comparisons between different systems. This will help assess the pressures of Surveillance Capitalism (Zuboff, 2019) on human autonomy. References Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59 (4), 390–412. (Publisher: Elsevier) Barto´n, K. (2022, September). MuMIn: Multi-Model Inference. Retrieved 2023-01-08, from https://CRAN.R-project .org/package=MuMIn Bates, D., M¨achler, M., Bolker, B., & Walker, S. (2014, June). Fitting Linear Mixed-Effects Models using lme4. arXiv. Retrieved 2023-01-12, from http://arxiv.org/abs/1406.5823 (arXiv:1406.5823 [stat]) Bennett, J., & Lanning, S. (2007). The netflix prize. In Proceedings of KDD cup and workshop (Vol. 2007, p. 35). New York. urrell, J. (2016, June). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3 (1), 2053951715622512. Retrieved 2022-10-11, from https://ezproxy-prd.bodleian.ox.ac.uk:2246/doi/full/10.1177/2053951715622512 (Publisher: SAGE Publications Ltd) doi: 10.1177/ 2053951715622512 Calvo, R. A., Peters, D., Vold, K., & Ryan, R. M. (2020). Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry. In C. Burr & L. Floridi (Eds.), Ethics of Digital Well-Being: A Multidisciplinary Approach (pp. 31–54). Cham: Springer International Publishing. Retrieved 2022-08-16, from https://doi.org/10.1007/ 978-3-030-50585-1 2 doi: 10.1007/978-3-030-50585-1 2 Carroll, M. D., Dragan, A., Russell, S., & Hadfield-Menell, D. (2022). Estimating and penalizing induced preference shifts in recommender systems. In International Conference on Machine Learning (pp. 2686–2708). PMLR. Feng, C., Wang, H., Lu, N., Chen, T., He, H., Lu, Y., & Tu, X. M. (2014, April). Log-transformation and its implications for data analysis. Shanghai Archives of Psychiatry, 26 (2), 105–109. Retrieved 2023-01-10, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120293/ doi: 10.3969/j.issn.1002-0829.2014.02.009 Floridi, L., Holweg, M., Taddeo, M., Amaya Silva, J., M¨okander, J., & Wen, Y. 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