Policy changes at Norway’s Wilhelmsen Group have resulted in new-energy boss Jan Eyvin Wang resigning from the board of Swiss satellite company Astrocast. Astrocast said in a filing to the Oslo bourse that Wang’s position had been under discussion at board level for “some time” due to unspecified “policy changes” at his main employer.

Wilhelmsen’s new energy chief Wang leaves Astrocast board due to ‘policy changes’

Penalty Bench is a special compartment where the securities of Issuers that fail to comply with the Rules are placed. Penalty Bench is regulated in section 2.10.3 of Oslo Rule Book II (Oslo Børs and Euronext Expand) and section 3.8.3 of Euronext Growth ...

OSLO BØRS - STATUS COMPANIES PLACED IN RECOVERY BOX AND PENALTY BENCH

Telecom operators will generate $17 billion of additional revenue from 3GPP-compliant 5G satellite networks between the 2024-2030 period, according to a new research report from Juniper Research. The report urges operators to sign partnerships with ...

5G satellite to generate $17bn through 2023: Juniper Research

A threat actor and alleged ransomware operator going by the alias USDoD has leaked data on over 3,000 suppliers of aviation giant Airbus after supposedly penetrating the organisation’s systems ...

Data on over 3,000 Airbus suppliers leaked after breach

In February 2022, Astrocast unveiled the companys partnership with UnaBiz-an IoT service provider, aims on developing an end-to-end IoT solution for asset tracking and monitoring. Under this ...

IoT Professional Services Market Analysis, Key Segments, Share, Size, Growth Status, and Forecast 2031

The global satellite IoT communications market is growing at a steady pace, according to Berg Insight, with its subscriber base growing to more than 4,5-million in 2022 – and with CAGR forecast at 39,6% it will reach 23,9-million units in 2027.

Global satellite IoT subscriber base to reach 24m by 2027

Astrocast

astrocast

Let's learn about via these 63 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Space /Learn Repo 1. Exodus from Mars Mother Earth, the womb of the solar system. Every other place, foster homes for her unwanted children. What is next? 2. The Impact of Web3 and Blockchain on the Space Industry How can Web3 and Blockchain, two new technologies that are more associated with grifting and scams have anything to do with the space race? 3. Lets Go Beyond Nuclear Fusion and Extract Energy from Blackholes I actually have been thinking about this for many years, and up until recently it has been confirmed. This is nothing new, as cosmologist and thermonuclear physicist Yakov Zeldovich have discussed this nearly half a century ago. 4. The Westall UFO Incident Explained In Under 5 Minutes The Westall UFO [incident] occurred on April 6, 1966, in the Melbourne suburb of Westall, Victoria, Australia. The incident involved multiple reports of... 5. DIY Tutorial: Building a System to Track and Communicate with Satellites in Orbit The purpose of my project is to build a system for tracking satellites in orbit and will make it possible to communicate with the satellites 6. Why Every Company Will Eventually Become a Space Company At some point, every company will become a space company. Sounds stupid at the moment but soon all companies will want to be part of the industry. Here's how. 7. How Twilio Sent Code to Space In a first of its kind mission, Twilio invited participants to launch their code into the stratosphere, and have it run in space. 8. “A robot army isn’t an ideal opponent, but I would have a water gun” Naturally, another good portion of the population would be trying to destroy everything around them for stupidity’s sake. 9. I'll pick up the check - from space! Everyone is all like, don't come back from space Jeff, but let me you tell you something, I am single and ready to mingle. 10. Can Mankind Survive as an Interplanetary Species? Humanity is well on the way to becoming an interplanetary species, but can we overcome the challenges of survival on another planet? 11. With Close to $100 million in Total Sales in Metaverses, NFTs Are Boosting Commerce in Metaverses Space is a 3D virtual world experience which provides its citizens powerful tools to create content, build businesses, interact collaboratively, and own and mon 12. Nominee Paul Bailey is Building the Future of Automated Space Systems Paul Bailey from the United States has been nominated for a 2020 Noonie in the Software Development category. He is a System Architect at CognitiveSpace, a company performing R&D in the field of aerospace engineering. 13. How to Build SpaceX’s Starship The Starship is a reusable heavy-lift launch vehicle (rocket) designed to travel to space with cargo and humans on board. 14. What Is Elon Musk's Deal with Space Colonization and Underpopulation? Elon Musk is just too busy to explain why we should worry more about underpopulation than the overpopulation problem. 15. Videos About Space We Shouldn't Undervalue ▶️ Ok, let's explore a few cool videos that I think a lot of addicted to space people should watch. 16. This Swiss Company is Building a Constellation of Nano-Satellites Astrocast is a company working to provide 100% of global coverage for IoT with an innovative constellation of nano-satellites. 17. Student Spaceshot Rocket Clubs — Part 1 (USA) In the past few years, liquid propulsion and spaceshot rocket clubs have popped up at high schools and colleges worldwide. Let's take a look at some of them. 18. Everything You Need to Know About Nasa's Perseverance Rover NASA's latest and greatest $2.7 Billion rover, Perseverance, was launched on July 30, 2020. It was a huge deal, and you're probably confused as to why. NASA's Jet Propulsion Lab manufactured Percy, and it is expected to land on Mars on February 18, 2021. Though it looks like a tiny robot to the eye, it is the size of an SUV and weighs 1025 kg (2260 lb). Let's start with the purpose of the whole mission. Perseverance serves four purposes. 19. Starship Transformer: Launch And Land Anywhere  Even In Wind The moment SpaceX mastered rocket engine vectoring all kinds of cool new space ship design possibilities suddenly opened. 20. What is SpaceX's Starlink Project? - A Brief Summary SpaceX is preparing to deliver high-speed internet to the world by sending about 30 thousand satellites into the Earth's orbit. 21. Tesla Cybertruck: Built for Mars, Available on Earth Elon Musk is known for his incredibly ambitious, multi-year visions for the future. Tesla’s initial Master Plan, funding cheap vehicles with expensive ones, was realized with the Model 3. Now the Cybertruck is part of a whole new Master Plan that has much more to do with SpaceX than Tesla. You might love the Cybertruck, or you might hate it, but it was never designed for you. Cybertruck was designed to be extremely rugged, Mars rugged. 22. Merging The Galaxies With The Metaverse Space research meets the metaverse using advanced technologies like blockchain, AI, VR and cryptocurrency. Explore multiple use cases of metaverse in space and 23. We Need Some SPACE: What Is Missing From DORA? The SPACE framework offers a holistic approach which takes into account wellbeing, collaboration and satisfaction alongside the usual performance metrics. 24. Fermi Paradox: Is Earth on Quarantine? Someone asks me a year ago about what Fermi Paradox is. And it's not a simple question, especially when you dig deeper. So I'm sharing a list of excellent clips that you might enjoy. Especially with recent news from our science friends - I think this article should be published. 25. Sustainable Agriculture, Space Colonization, And Food Security Challenges As humans colonize space, advances in key agricultural technologies are key to achieving long term survivability and also help Earth at the same time. 26. On MoonDAO's Unending Ambition to Decentralize The Space Industry MoonDAO will stop at nothing to decentralize the space industry, or so it seems, and with a consortium of 1,000 crypto investors throwing 2,620 ETH behind it 27. A Beginner's Guide for Alien Invaders Exclusively on HackerNoon! An original never-before-published science fiction story! 28. The Minkowski's Four-Dimensional Space Relativity: The Special and General Theory by Albert Einstein is part of HackerNoon’s Book Blog Post series. 29. Always Bet Musk's Oxen! Fresh off the Virgin Martian Express, Jackson had a layover at the Blue Origin moon base. He was trying to get to the Starbase Autonomous Zone 30. Why SpaceX’s Historic Starship Flight Wasn't a “Failure” This week, the coverage of one of humanity’s most impressive milestones was reduced to a climax of fire. Starship didn’t fail. Science divulgation, however, might have. 31. The Noonification: Can Mankind Survive as an Interplanetary Species?  (10/20/2022) 10/20/2022: Top 5 stories on the Hackernoon homepage! 32. Advancements in Airplane Tech Could Lead Major Airports to Become Spaceports Hypersonic aviation is going to open up more opportunities than it realises and airports need to be on the ball to become spaceports themselves. 33. Why Netflix’s Squid Game is NOT Close to Reality I'm not thrilled, and I'm far away from being obsessed with Netflix's Squid Game. 34. The Universe is So Big, Aliens Must Exist This Slack discussion by Utsav Jaiswal, Arthur, Patrick Lee (Mentor) and Limarc occurred in hackernoon's official #slogging-beta channel. 35. Spacecraft Engineering Models: How to Migrate UML to TypeQL How to map UML to TypeQL. Read how the European Space Agency migrated their spacecraft engineering models from UML to TypeQL for use in a TypeDB database. 36. Big ‘Earth Observation’ Data: Challenges and Applications As nearly a thousand Earth observation satellites currently orbit the planet, terabytes of remote sensing data and satellite imagery of land, vegetation, water bodies, glaciers, urban landscapes, and other geographic features become available for end users across multiple industries. Modern GIS systems allow the collection of all such geospatial data in one place for a comprehensive analysis of the area under study. 37. SpaceX Starlink Master Plan A parody, not written by Elon Musk 38. The Computational Infrastructure Behind Habidatum Location Risk Score Learn about space-time indexing at scale using quadkeys and clickhouse. 39. UFOs: Five things we can be pretty sure about technologically superior civilizations If we believe in evolution then, however, sophisticated form of life they are. They probably evolved from the simplest life forms  daily fighting for survival. 40. Main Reasons To Build Intelligent Space Stations With An Artificial Gravity It’s been the dream of many space enthusiasts from the 1960s to live in an intelligent artificial gravity space station ever since it was popularized by NASA chief scientist Von Braun and the legendary film: ‘Space Odyssey: 2001’. Unfortunately, generating artificial gravity for astronauts was not a priority for NASA as astronauts were able to reduce some of the impact of weightlessness on their bodies thanks to a ruthless exercise regimen. Further, a ship that contains a centrifuge for generating artificial gravity would be extremely expensive to launch to orbit. So, NASA ended up sticking with a compact and modular station that is more affordable with their budget and that doesn’t deal with weightlessness risk and accepted the risks it posed to astronauts in the international space station. Below is a comprehensive list of health risks astronauts face in the zero-g space station. Weightlessness risks are highlighted in the red box. 41. Quantum Origin, CosmicOS, and Lambda Calculus In regards to Quantum Origin, you may have read my post from June. This is a follow up to it. In our observable universe, it seems infinite in terms of the scale everything is in, whether going on in the micro, nano or cosmic scales and beyond. I followed up on the fact that there are certain mathematical patterns in regards to everything and I'm evidentially wanting to make a theory of everything. Proof in regards to my mathematical proposals in terms of computation would be solving the holy grail of man's biggest questions. While I'm confident in regards to the math and the way things are structured, there are things I have thought about in regards to taking things a step further. 42. About Man's Need to Go to Space Human Expansion: the space industrialization 43. Building a Decentralized and Uncensorable Internet — The Nexus Protocol Building a Decentralized and Uncensorable Internet — The Nexus Protocol 44. NASA Discovers Evidence of ‘Water Worlds’ in Deep Space Scientists have spotted two water worlds, Kepler-138 c and Kepler-138 d, that they think could be teeming with water. 45. How Starlink is Disrupting the Internet Starlink, by SpaceX, will provide internet from the constellation of low Earth orbit satellites with low latency and high speed compared to submarine cables. 46. The United States Lost the Space Race With the discovery of water ice on the moon, a self-sufficient base becomes a real possibility. We're going to the moon, but only if some corporation takes us. 47. In Memoriam: the Fallen Defenders of G'oldurree When We Are Gone, Will There Be Anyone to Mourn Our Passing? 48. First-ever Image Of A Multi-planet System Around A Sun-like Star In this thread, the HackerNoon Community discusses their thoughts on scientific evolution, its consequences, and our favorite Hubble images. 49. Spacetex: Clothes Made for Space, the Fiber Behind Gravity Spacetex textile is changing the game in fashion introducing subcategories such as Dystopian Fashion and Techwear. 50. Latin America Seeks its Riches in Space by Reaching Jupiter in 30 Days JOVE is a novel solar-powered mission that will use the solar wind's momentum, speed, and direction as the propulsive thrust to reach Jupiter in 30 days 51. The REAL Life of Pi: Ethereal, Romantic, Mysterious And Completely Memorizing “Probably no symbol in mathematics has evoked as much mystery, romanticism, misconception and human interest as the number Pi” ~William L. Schaaf, Nature and History of Pi. π 52. Space Exploration: New Frontier For Technological Innovations From the earliest known periods of human history, people have always been fascinated by the big yellow ball that provided light and heat, the tiny light dots shimmering in and regularly recurring tailed streaks dashing across the night sky. We wanted to know more about those objects, but the crippling limitations in our knowledge and technology didn’t allow that. 53. Starhopper: The Prototype Of Starship By SpaceX SpaceX is a space exploration company owned by a genius and a visionary Elon Musk who’s also the CEO of Tesla Inc. The main goal of SpaceX is to make space travel and exploration easy and efficient. Throughout the years, Elon Musk has done a lot of amazing things and has stayed completely open to the questions of the people. I think that Elon Musk’s transparency is his best quality. Anyways, let’s take a closer look at the latest advancement of SpaceX. 54. Can Satellites Help Combat Global Warming? In this slogging thread, our technology channel took the chance to discuss this news about the possibility of satellites helping fight global warming. 55. The Ongoing Mars Missions: Are We Ready to Live on the Red Planet? Back in the '60s, we saw the biggest and heated space race to the moon and now we are in the space race to Mars. The coming year is going to bring some exciting news and adventures about life on Mars. NASA is not alone this time either, there are several other space agencies sending Mars missions. But why are these space organizations spending their millions on such missions? Just to know one thing— could humans live on Mars? Does life exist on the red planet? 56. What 2021 Scientific Discovery Excited You the Most? In this slogging thread, our community shared their favorite scientific discoveries of the year. 57. The Long Game in the Space Industry: Why the Need to Transcend Requires Time and Effort Monica’s log, Stardate… I have been wanting to articulate some of my thoughts about the notion of time and effort for the space industry. Allow me to state some of my premises in advance. 58. Houston, We Have a Problem. There’s Not Enough Women on this Spaceship! Celebrate this Intentional Holiday with Girls that Rule the World! 59. The Space Tech Startup Making Our Internet A Better Place With Lasers🖖 Greetings, humans. I remote chat this afternoon with Rohit Jha, the CEO & Co-Founder of Transcelestial Technologies, a startup building light speed communications infrastructure for the next 100 years. 60. Visualizing Asteroids in Python with Bokeh and NASA APIs A GET request to the Astronomy Picture of the Day (APOD) web service on September 18, 2020, returned the above photo of the spiral galaxy Arp 78.  APOD is one of many APIs available through NASA Open APIs.  Like APOD, some include imagery, such as the Earth Polychromatic Imaging Camera (EPIC) API, which returns images like this: 61. Into Space MANY of my readers will remember the mysterious radio messages which were heard by both amateur and professional short wave operators during the night 62. NexOS, Internet of Things (IoT), And Beyond: Part I Understanding the inspiration and history behind Nexus helps convey the current vision. This is the first of three articles covering pertinent aspects of Nexus’ history in which the context is set for the burgeoning NexOS (Nexus Operating System), and its innovative security principles are introduced. 63. Argentina’s Steady And Visionary Path To Space Is An Inspiring Tale Which path is required for us to reach new worlds? Argentina’s history in the space industry shows an unwavering course of perseverance and resilience. Thank you for checking out the 63 most read stories about Space on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

63 Stories To Learn About Space

331 Stories To Learn About Iot

How can you track the water quality of a river that descends from a remote mountain? Or monitor illegal fishing in ocean reserves hundreds of miles off the coast? How can a company transporting vaccines across continents monitor and track its cargo? The answer to these, and other vital questions, is satellite internet of things (IoT). By 2030 there will be more intelligent devices connected than people on this planet. Experts estimate that IoT devices will the 75 billion mark. That’s more than nine devices for every person alive today. surpass While in smart homes, IoT devices show off their potential for slick consumers, in reality, the industrial sectors are the main players of the new technology. Industries operate hundreds of thousands of connected devices, from smart robotic manufacturing to international logistics. The core foundation of this entire multibillion-dollar market relies on one thing: Connectivity. But IoT devices don’t need just any kind of connectivity; they require low latency (no delay), low power (to drive autonomy and extend battery life), and high-data performance. And while more than 90% of the global population in urban environments have online services, the shocking truth is that networks only cover 15% of the surface of our planet. Connectivity for the remaining 85% Some of the most critical operations for society, life, and business on Earth—including global supply chains, transportation, oceanography, agriculture, climate management, and disaster prevention—are operating in remote areas outside network coverage. How can these industries and sectors transform? To find out, we spoke to , CEO of , a company working to provide 100% of global coverage for IoT with an innovative constellation of nano-satellites. Astrocast is a Swiss company backed by venture capital firm Adit Ventures, Airbus SE’s venture arm, and the European Space Agency (ESA). They have been providing cost-efficient, low-energy, and bidirectional satellite IoT services since early 2022. Fabien Jordan Astrocast On November 26, 2022, the PSLV-C54 Polar Satellite Launch Vehicle rocketed into space from India’s Satish Dhawan Space Center. The PSLV-C54 rocket carried four Astrocast nano-satellite spacecraft among its payload. Once the satellites joined the other 10 in operation in low-earth orbit, the company’s constellation expanded to a total of 14 satellites. “Our next launch is scheduled for early January on a Falcon9 from SpaceX,” Jordan told 150Sec. “We are launching a batch of four additional satellites. At this point, our 18-satellite constellation distributed on four different polar orbits will already have the capacity and capabilities to efficiently serve the needs of our key markets such as shipping container tracking, agritech, and utilities.” Astrocast is progressively expanding to serve new industries and meet the demands for connectivity that can close the gaps of the digital divide. Shipping, agritech and water access: Leverage satellite IoT Astrocast believes that satellite technologies can drive progress and digitally transform industries that are roadblocked by a lack of connectivity. The company focuses on a cost-effective, economically viable business model. This approach has attracted the attention of several global companies. ArrowSpot— a leading system integrator in the container tracking industry, recently signed a multi-million dollar deal with Astrocast. “They realized the importance of having a hybrid, multi-connectivity tracker that uses the most appropriate technology depending on the location of the shipping container,” Jordan said. Embedding a cost-effective satellite IoT link, ArrowSpot greatly improved the visibility of the asset along its journey. It can now offer its customers full visibility at an affordable price. “Oceans are the least connected places, and maritime assets constantly travel in and out of terrestrial networks,” Jordan said. “Very similar constraints also apply to the land transport and rail industries, crossing vast and unconnected remote areas.” Another company that found value in satellite connectivity is —a pioneer in agritech driving agriculture 4.0 technology. Avirtech, with important operations in Southeast Asia, especially in areas where terrestrial networks are unavailable, is helping transform the global agriculture sensor market while impacting local populations by providing them access to cutting-edge farming technology. Avirtech Using smart IoT devices connected via satellite through Astrocast, the new Avirtech system can provide crop intelligence. This includes plantation control systems that monitor site and conditions to reduce operational costs and increase yields. “We believe this partnership puts Astrocast in a very good position to support the acceleration of smart farming deployment in these regions,” Jordan added. “The most affected sectors by lack of connectivity are agriculture and livestock, maritime, oil and gas, and environmental and utilities,” Jordan explained. “These are usually industries that have assets in rural areas, but the use case does not necessarily justify the enormous investment to legacy satellite connectivity.” On December 19, Swiss integrator Oratek working with , a Swiss foundation dedicated to improving access to drinking water, health, education, and jobs, partnered up with Astrocast to add satellite connectivity to water systems in Senegal. Access to Water Astrocast explained that the classifies Senegal as a water-poor country. Approximately 20% of the country’s population does not have access to clean water. The latest generation of Access to Water’s purification filters can treat dirty, polluted, or brackish raw water. Now, thanks to new satellite IoT technology—in addition to producing clean water—these filters are collecting important information such as water quality, usage, and equipment status. The information benefits rural populations and the foundation, helping them better manage resources, maintain the systems and filters, and make key decisions. United Nations “Because these locations are extremely remote, they are not accessible by any standard communication network. The only way to collect and share this information is through a satellite connection, an approach that until recently was deemed too expensive for this type of use case,” Aurelien Essig, Founder of Oratek explained. Oratek opted to implement Astrocast’s nanosatellite solution, which provides a highly cost-effective approach to achieving satellite connectivity. But how does the technology work? The Earth-Space network technology approach To establish communication via the Astrocast Network, the system integrator or device manufacturer must integrate the Astronode S modem together with a small L-band antenna into the device and configure the application. The device will then be able to send its IoT data to Astrocast’s satellites using a store and forward approach. “Data is then downloaded from the satellites to a network of ground stations and then to our servers, available through an online portal or our APIs,” Jordan explained. Low-power consumption is absolutely critical for direct-to-satellite IoT. “We are often talking about battery-powered devices to track moving assets, such as containers, animals, and machinery,” Jordan added. “You need your assets to be as autonomous as possible, running for several years without human intervention.” Thanks to the introduction of new technologies that allow the miniaturization of satellites, and thanks to easier commercial access to space, a constellation like Astrocast allows services to be very cost-effective, more than three times less expensive than legacy operators. The fourth industrial revolution, automation, AI, and the Internet of Things, is inevitably changing the way the world works every day. As this new movement evolves and smart devices proliferate, global connectivity becomes essential and a top priority. Satellites and new space technology are poised to contribute greatly to the development of the IoT industry. Furthermore, use cases are already proving that the tech can serve societies, humanity, and our environment, addressing the most pressing challenges our planet faces. Ray Fernandez, Contributor, 150Sec. This story was originally published by Ray Fernandez on . 150Sec

This Swiss Company is Building a Constellation of Nano-Satellites

এই সুইস কোম্পানি ন্যানো-স্যাটেলাইটের একটি নক্ষত্রমণ্ডল তৈরি করছে

Dieses Schweizer Unternehmen baut eine Konstellation von Nanosatelliten auf

Esta empresa suiza está construyendo una constelación de nanosatélites

Cette entreprise suisse construit une constellation de nano-satellites

यह स्विस कंपनी बना रही है नैनो-उपग्रहों का समूह

このスイスの会社は、ナノ衛星のコンステレーションを構築しています

이 스위스 회사는 나노 위성의 별자리를 구축하고 있습니다.

Esta empresa suíça está construindo uma constelação de nano-satélites

Bu İsviçre Şirketi Nano Uydulardan Oluşan Bir Takımyıldız İnşa Ediyor

Công ty Thụy Sĩ này đang xây dựng một chòm sao vệ tinh nano

这家瑞士公司正在建造一个纳米卫星星座

Authors:
(1) Toshiki Kawai, Department of Physics, Hokkaido University, Sapporo 060-0810, Japan (E-mail: t-kawai@higgs3.sci.hokudai.ac.jp);
(2) Yoshiharu Kawamura, Department of Physics, Shinshu University, Matsumoto 390-8621, Japan (E-mail: haru@azusa.shinshu-u.ac.jp). Table of Links Abstract and 1 Introduction 2 U(1) gauge theory on a warped background 3 Gauge-Higgs inflation on a warped background 4 Conclusions and discussions, Acknowledgements, and References 4 Conclusions and discussions We have investigated whether inflation in the early universe can be induced by an extra component of a 5d U(1) gauge field coupled to fermions in the RS warped spacetime or not. Imposing CBCs on fields involved in inflation, we have shown that the one-loop effective potential for a Wilson line phase obtained by quantum corrections can behave as an inflaton potential by finding parameter regions consistent with Planck 2018 results. Our model shares basic properties with higher-dimensional models on a flat spacetime, e.g., a potential is generated radiatively, and is robust owing to the gauge symmetry, and then a model has a predictability for physics on inflation. The remaining issues and future prospects are as follows. There is a fine-tuning problem that the (initial) vaules of φ and other parameters should be chosen in order to make φ as an inflaton work well. It would be better if they are determined from a fundamental theory. Or we would have to rely on the anthropic principle in the framework of multiverse. It is necessary to consider contributions of a radion and/or modulus in order to identify an inflaton. In concrete, it is essential to study their stabilization and how inflation can be realized in a system with such several scalar fields including a Wilson line phase on the RS warped background. It would be wonderful to construct a more realistic higher-dimensional gauge theory which solves riddles of particle physics and cosmology on a similar footing. More specifically, it would be fantastic that, in a system governed by the gauge principle, there are several Wilson line phases originated from extra components of gauge bosons, which behave as Higgs boson, an inflaton, a dark matter and so forth. A top-down approach would be useful to construct such a model in a more unified form. Superstring theory, for instance, provides many scalar and vector fields, and possesses many candidates of Higgs boson, an inflaton, a dark matter and so on. Hence, it is intriguing to investigate how a 5d effective theory on the warped geometry can be realized in such a top-down framework. Acknowledgements T.K. would like to thank Dr. O. Seto for the helpful discussions and advice. This work was supported in part by scientific grants from the Ministry of Education, Culture, Sports, Science and Technology under Grant No. 22K03632 (YK). References [1] D. H. Lyth and A. Riotto, “Particle physics models of inflation and the cosmological density perturbation”, Phys. Rept. 314 , 1 (1999), arXiv:hep-ph/9807278, and references therein. [2] Y. Hosotani, “Dynamical Mass Generation by Compact Extra Dimensions”, Phys. Lett. B126, 309 (1983). [3] Y. Hosotani, “Dynamics of Nonintegrable Phases and Gauge Symmetry Breaking”, Annals. Phys. 190, 233 (1989). [4] N. Arkani-Hamed, H.-C. Cheng, P. Creminelli, and L. Randall, “Extranatural Inflation”, Phys. Rev. Lett. 90, 221302 (2003), arXiv:hep-th/0301218. [5] Y. Abe, T. Inami, Y. Kawamura, and Y. Koyama, “Radion stabilization in the presence of a Wilson line phase”, Prog. Theor. Exp. Phys. 2014, 073B04 (2014), arXiv:1404.5125 [hep-th]. [6] Y. Abe, T. Inami, Y. Kawamura, and Y. Koyama, “Inflation from radion gauge-Higgs potential at Planck scale”, Prog. Theor. Exp. Phys. 2015, 093B03 (2015), arXiv:1504.06905 [hep-th]. [7] N. Aghanim et al. (Planck), “Planck 2018 results. VI. Cosmological parameters”, Astron. Astrophys. 641, [Erratum: Astron.Astrophys. 652, C4 (2021)], A6 (2020), arXiv:1807.06209 [astro-ph.CO]. [8] L. Randall and R. Sundrum, “A Large mass hierarchy from a small extra dimension”, Phys. Rev. Lett. 83, 3370-3373 (1999), arXiv:hep-ph/9905221. [9] L. Randall and R. Sundrum, “An Alternative to compactification”, Phys. Rev. Lett. 83, 4690-4693 (1999), arXiv:hep-ph/9906064. [10] A. D. Medina, N. R. Shah, and C. E. M. Wagner, “Gauge-Higgs Unification and Radiative Electroweak Symmetry Breaking in Warped Extra Dimensions”, Phys. Rev. D 76, 095010 (2007), arXiv:0706.1281 [hep-ph]. [11] Y. Hosotani, K. Oda, T. Ohnuma, and Y. Sakamura, “Dynamical Electroweak Symmetry Breaking in SO(5) × U(1) Gauge-Higgs Unification with Top and Bottom Quarks”, Phys. Rev. D 78, [Erratum: Phys. Rev.D 79, 079902 (2009)], 096002 (2008), arXiv:0806.0480[hep-ph]. [12] Y. Hosotani, “New dimensions from gauge-Higgs unification”, PoS CORFU2016, 026 (2017), arXiv:1702.08161 [hep-ph]. [13] S. Funatsu, H. Hatanaka, Y. Hosotani, Y. Orikasa, and N. Yamatsu, “CKM matrix and FCNC suppression in SO(5)×U(1)×SU(3) gauge-Higgs unification”, Phys. Rev. D 101, 055016 (2020), arXiv:1909.00190 [hep-ph]. [14] A. Hebecker and J. March-Russell, “The structure of GUT breaking by orbifolding”, Nucl. Phys. B 625, 128-150 (2002), arXiv:hep-ph/0107039. [15] N. Haba, Y. Kawamura, and K.-y. Oda, “Dynamical Rearrangement of Theta Parameter in Presence of Mixed Chern-Simons Term”, Phys. Rev. D 78, 085021 (2008), arXiv:0803.4380 [hep-ph]. [16] Y. Abe, Y. Goto, Y. Kawamura, and Y. Nishikawa, “Conjugate boundary condition, hidden particles, and gauge-Higgs inflation”, Mod. Phys. Lett. A 31, 1650208 (2016), arXiv:1608.06393 [hep-ph]. [17] K. Kojima and Y. Okubo, “Early dark energy from a higher-dimensional gauge theory”, Phys. Rev. D 106, 063540 (2022), arXiv:2205.13777 [astro-ph.CO]. [18] C. C´saki, S. Lombardo, and O. Telem, “TASI Lectures on Non-supersymmetric BSM Models”, Proceedings, Theoretical Advanced Study Institute in Elementary Particle Physics : Anticipating the Next Discoveries in Particle Physics (TASI 2016): Boulder, CO, USA, June 6-July 1, 2016 , edited by R. Essig and I. Low (WSP, 2018), 501-570, arXiv:1811.04279 [hep-ph]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Toshiki Kawai, Department of Physics, Hokkaido University, Sapporo 060-0810, Japan (E-mail: t-kawai@higgs3.sci.hokudai.ac.jp); (2) Yoshiharu Kawamura, Department of Physics, Shinshu University, Matsumoto 390-8621, Japan (E-mail: haru@azusa.shinshu-u.ac.jp). Authors: Authors: (1) Toshiki Kawai, Department of Physics, Hokkaido University, Sapporo 060-0810, Japan (E-mail: t-kawai@higgs3.sci.hokudai.ac.jp); (2) Yoshiharu Kawamura, Department of Physics, Shinshu University, Matsumoto 390-8621, Japan (E-mail: haru@azusa.shinshu-u.ac.jp). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 U(1) gauge theory on a warped background 2 U(1) gauge theory on a warped background 3 Gauge-Higgs inflation on a warped background 3 Gauge-Higgs inflation on a warped background 4 Conclusions and discussions, Acknowledgements, and References 4 Conclusions and discussions, Acknowledgements, and References 4 Conclusions and discussions We have investigated whether inflation in the early universe can be induced by an extra component of a 5d U(1) gauge field coupled to fermions in the RS warped spacetime or not. Imposing CBCs on fields involved in inflation, we have shown that the one-loop effective potential for a Wilson line phase obtained by quantum corrections can behave as an inflaton potential by finding parameter regions consistent with Planck 2018 results. Our model shares basic properties with higher-dimensional models on a flat spacetime, e.g., a potential is generated radiatively, and is robust owing to the gauge symmetry, and then a model has a predictability for physics on inflation. The remaining issues and future prospects are as follows. There is a fine-tuning problem that the (initial) vaules of φ and other parameters should be chosen in order to make φ as an inflaton work well. It would be better if they are determined from a fundamental theory. Or we would have to rely on the anthropic principle in the framework of multiverse. It is necessary to consider contributions of a radion and/or modulus in order to identify an inflaton. In concrete, it is essential to study their stabilization and how inflation can be realized in a system with such several scalar fields including a Wilson line phase on the RS warped background. It would be wonderful to construct a more realistic higher-dimensional gauge theory which solves riddles of particle physics and cosmology on a similar footing. More specifically, it would be fantastic that, in a system governed by the gauge principle, there are several Wilson line phases originated from extra components of gauge bosons, which behave as Higgs boson, an inflaton, a dark matter and so forth. A top-down approach would be useful to construct such a model in a more unified form. Superstring theory, for instance, provides many scalar and vector fields, and possesses many candidates of Higgs boson, an inflaton, a dark matter and so on. Hence, it is intriguing to investigate how a 5d effective theory on the warped geometry can be realized in such a top-down framework. Acknowledgements T.K. would like to thank Dr. O. Seto for the helpful discussions and advice. This work was supported in part by scientific grants from the Ministry of Education, Culture, Sports, Science and Technology under Grant No. 22K03632 (YK). References [1] D. H. Lyth and A. Riotto, “Particle physics models of inflation and the cosmological density perturbation”, Phys. Rept. 314 , 1 (1999), arXiv:hep-ph/9807278, and references therein. [2] Y. Hosotani, “Dynamical Mass Generation by Compact Extra Dimensions”, Phys. Lett. B126, 309 (1983). [3] Y. Hosotani, “Dynamics of Nonintegrable Phases and Gauge Symmetry Breaking”, Annals. Phys. 190, 233 (1989). [4] N. Arkani-Hamed, H.-C. Cheng, P. Creminelli, and L. Randall, “Extranatural Inflation”, Phys. Rev. Lett. 90, 221302 (2003), arXiv:hep-th/0301218. [5] Y. Abe, T. Inami, Y. Kawamura, and Y. Koyama, “Radion stabilization in the presence of a Wilson line phase”, Prog. Theor. Exp. Phys. 2014, 073B04 (2014), arXiv:1404.5125 [hep-th]. 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Exploring Gauge-Higgs Inflation with Extra Dimensions: Conclusions, Acknowledgements, and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Vitor da Fonseca, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa;
(2) Tiago Barreiro, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa and 2ECEO, Universidade Lus´ofona;
(3) Nelson J. Nunes, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa. Table of Links Abstract and Intro
Coupling Dark Energy to Neutrinos
Impact of the Coupling on Perturbations and Observables
Parameter Estimation
Discussion
Acknowledgment, Appendix, and References ACKNOWLEDGMENTS The authors would like to thank C. van de Bruck and D. F. Mota for the fruitful discussions. This work is supported by the Funda¸c˜ao para a Ciˆencia e a Tecnologia (FCT) through the research grants UIDB/04434/2020 and UIDP/04434/2020, and the BEYLA project PTDC/FIS-AST/0054/2021. V.d.F. acknowledges FCT support through fellowship 2022.14431.BD. Appendix A: Parameter constraints for the MaVaN and the uncoupled scalar field models In the appendix we show the triangular plots of the analysis made with the Plk18+BAO data for the MaVaN model (β free) and the uncoupled case (β = 0), i.e. no interaction between the quintessence component and the neutrino fluid. References [1] J. Lesgourgues and S. Pastor, Phys. Rept. 429, 307 (2006), arXiv:astro-ph/0603494. [2] M. Gerbino and M. Lattanzi, Frontiers in Physics 5 (2018), 10.3389/fphy.2017.00070. [3] Y. Fukuda et al. (Super-Kamiokande), Phys. Rev. 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Authors: (1) Vitor da Fonseca, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa; (2) Tiago Barreiro, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa and 2ECEO, Universidade Lus´ofona; (3) Nelson J. Nunes, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Vitor da Fonseca, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa; (2) Tiago Barreiro, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa and 2ECEO, Universidade Lus´ofona; (3) Nelson J. Nunes, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa. Table of Links Abstract and Intro Coupling Dark Energy to Neutrinos Impact of the Coupling on Perturbations and Observables Parameter Estimation Discussion Acknowledgment, Appendix, and References Abstract and Intro Abstract and Intro Coupling Dark Energy to Neutrinos Coupling Dark Energy to Neutrinos Impact of the Coupling on Perturbations and Observables Impact of the Coupling on Perturbations and Observables Parameter Estimation Parameter Estimation Discussion Discussion Acknowledgment, Appendix, and References Acknowledgment, Appendix, and References ACKNOWLEDGMENTS The authors would like to thank C. van de Bruck and D. F. Mota for the fruitful discussions. This work is supported by the Funda¸c˜ao para a Ciˆencia e a Tecnologia (FCT) through the research grants UIDB/04434/2020 and UIDP/04434/2020, and the BEYLA project PTDC/FIS-AST/0054/2021. V.d.F. acknowledges FCT support through fellowship 2022.14431.BD. Appendix A: Parameter constraints for the MaVaN and the uncoupled scalar field models In the appendix we show the triangular plots of the analysis made with the Plk18+BAO data for the MaVaN model (β free) and the uncoupled case (β = 0), i.e. no interaction between the quintessence component and the neutrino fluid. References [1] J. Lesgourgues and S. Pastor, Phys. Rept. 429, 307 (2006), arXiv:astro-ph/0603494. [2] M. Gerbino and M. Lattanzi, Frontiers in Physics 5 (2018), 10.3389/fphy.2017.00070. [3] Y. Fukuda et al. (Super-Kamiokande), Phys. Rev. Lett. 81, 1562 (1998), arXiv:hep-ex/9807003. [4] K. Abe et al. (T2K), Phys. Rev. Lett. 112, 061802 (2014), arXiv:1311.4750 [hep-ex]. [5] I. Esteban, M. C. Gonzalez-Garcia, A. HernandezCabezudo, M. Maltoni, and T. Schwetz, JHEP 01, 106 (2019), arXiv:1811.05487 [hep-ph]. [6] S. Roy Choudhury and S. Hannestad, JCAP 07, 037 (2020), arXiv:1907.12598 [astro-ph.CO]. [7] M. Aker et al. (KATRIN), Phys. Rev. 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Relaxing cosmological constraints on current neutrino masses: Acknowledgment, Appendix & References

বর্তমান নিউট্রিনো ভরের উপর বিশ্বতাত্ত্বিক সীমাবদ্ধতা শিথিল করা: স্বীকৃতি, পরিশিষ্ট এবং উল্লেখ

Lockerung der kosmologischen Beschränkungen für aktuelle Neutrinomassen: Danksagung, Anhang und Referenzen

Relajación de las restricciones cosmológicas sobre las masas actuales de neutrinos: reconocimiento, apéndice y referencias

Assouplir les contraintes cosmologiques sur les masses actuelles des neutrinos : remerciements, annexes et références

वर्तमान न्यूट्रिनो द्रव्यमान पर ब्रह्माण्ड संबंधी बाधाओं को शिथिल करना: आभार, परिशिष्ट और संदर्भ

現在のニュートリノ質量に対する宇宙論的制約の緩和: 謝辞、付録、参考文献

현재 중성미자 질량에 대한 우주론적 제약 완화: 감사의 글, 부록 및 참고 자료

Relaxando as restrições cosmológicas nas massas atuais de neutrinos: reconhecimento, apêndice e referências

Ослабление космологических ограничений на текущие массы нейтрино: Благодарности, Приложение и Ссылки

Mevcut nötrino kütleleri üzerindeki kozmolojik kısıtlamaların gevşetilmesi: Teşekkür, Ekler ve Referanslar

Nới lỏng các ràng buộc vũ trụ học đối với khối lượng neutrino hiện tại: Lời cảm ơn, Phụ lục & Tài liệu tham khảo

放宽对当前中微子质量的宇宙学限制：致谢、附录和参考文献

Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources G Methods for Style Mimicry This section summarizes the existing methods that a style forger can use to perform style mimicry. Our work only considers finetuning since it is reported to be the most effective (Shan et al., 2023a). G.1 Prompting Well-known artistic styles contained in the training data (e.g. Van Gogh) can be mimicked by prompting a text-to-image model with a description of the style or the name of the artist. For example, a prompt can be augmented with “ painted in a cubistic style” “ painted by van Gogh” to mimic those styles, respectively. Prompting is easy to apply and does not require changes to the model. However, it fails to mimic styles that are not sufficiently represented in the training data of model—often from the most vulnerable artists. G.2 Img2Img Img2Img creates an updated version of an image with guidance from a prompt. For this, Img2Img processes image x with t timesteps of a diffusion process to obtain the diffused image xt. Then, Img2Img uses the model with guidance from prompt P to reverse the diffusion process into the output image variation xP . Analogous to prompting, a prompt suffices to transfer a well-known style, but Img2Img also fails for unknown styles. G.3 Textual Inversion Textual inversion (Gal et al., 2022) optimizes the embedding of some n new tokens t = [t1, . . . , tn] that are appended to image prompts P so that generations closely mimic the style of a given set of images. The tokens are optimized via gradient descent on the model training loss so that P + t generates images that mimic the target style. Textual inversion requires white-box access to the target model, but enables the mimicry of unknown styles. G.4 Finetuning Finetuning updates the weights of a pretrained text-to-image model to introduce a new functionality. In this case, finetuning allows a forger to “teach” the generative model an unknown style using a set of images in the target style and their captions (e.g. an astronaut riding a horse). First, all captions are augmented with some special word, like the name of the artist, to create prompts Px = Cx + “by w∗”. Then, the model weights are updated to minimize the reconstruction loss of the given images following the augmented prompts. At inference time, the forger can append “by w∗” to any prompt to obtain art in the target style The authors of Glaze identify this finetuning setup as the strongest style mimicry method (Shan et al., 2023a). We validate the success of our style mimicry with a user study detailed in Appendix K.1 Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources G Methods for Style Mimicry This section summarizes the existing methods that a style forger can use to perform style mimicry. Our work only considers finetuning since it is reported to be the most effective (Shan et al., 2023a). G.1 Prompting Well-known artistic styles contained in the training data (e.g. Van Gogh) can be mimicked by prompting a text-to-image model with a description of the style or the name of the artist. For example, a prompt can be augmented with “ painted in a cubistic style” “ painted by van Gogh” to mimic those styles, respectively. Prompting is easy to apply and does not require changes to the model. However, it fails to mimic styles that are not sufficiently represented in the training data of model—often from the most vulnerable artists. G.2 Img2Img Img2Img creates an updated version of an image with guidance from a prompt. For this, Img2Img processes image x with t timesteps of a diffusion process to obtain the diffused image xt. Then, Img2Img uses the model with guidance from prompt P to reverse the diffusion process into the output image variation xP . Analogous to prompting, a prompt suffices to transfer a well-known style, but Img2Img also fails for unknown styles. G.3 Textual Inversion Textual inversion (Gal et al., 2022) optimizes the embedding of some n new tokens t = [t1, . . . , tn] that are appended to image prompts P so that generations closely mimic the style of a given set of images. The tokens are optimized via gradient descent on the model training loss so that P + t generates images that mimic the target style. Textual inversion requires white-box access to the target model, but enables the mimicry of unknown styles. G.4 Finetuning Finetuning updates the weights of a pretrained text-to-image model to introduce a new functionality. In this case, finetuning allows a forger to “teach” the generative model an unknown style using a set of images in the target style and their captions (e.g. an astronaut riding a horse). First, all captions are augmented with some special word, like the name of the artist, to create prompts Px = Cx + “by w∗”. Then, the model weights are updated to minimize the reconstruction loss of the given images following the augmented prompts. At inference time, the forger can append “by w∗” to any prompt to obtain art in the target style The authors of Glaze identify this finetuning setup as the strongest style mimicry method (Shan et al., 2023a). We validate the success of our style mimicry with a user study detailed in Appendix K.1 Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

The Science Behind AI’s Ability to Recreate Art Styles

Here's Why We Need To Build A Lunar Economy - Now!

NASA’s Post-ISS Plans May Not Add Up: Here's Why

The field of artificial intelligence is booming; image generation is one of the most prominent domains. There are a lot of competitors on the market, such as Stable Diffusion, Midjourney, and DALL-E. All these products are based on the diffusion networks approach. Unlike GPT diffusion models often seen as abstruse, the goal of this story is to demystify the complexity of diffusion models and explain how they work in an easy but comprehensive way. Let’s go! The Basics The Diffusion networks are artificial neural networks that allow you to solve various generation tasks. The most well-known application is image generation. In this story, let’s focus just on this type of generation. There are countless flavors of generation tasks: image2image, image2video, text2vide, video+image2video, etc. But all of them are based on a similar structure. The diffusion network is composed of 3 building blocks (neural networks): Diffusion process: a process of image generation;
Conditioning: a way to restrict generation to a given subject;
Latent space: a trick that makes the model feasible to run in given hardware constraints. Diffusion Process The diffusion processes concept comes from physics, which describes the way particles spread in a medium over time. In the context of machine learning, a diffusion model learns to progressively "refine" a noise vector into meaningful data by reversing a diffusion-like process training set created from images by gradually adding more and more noise to each image. To sample/generate from this model, we apply the network multiple times to minimize the noise and recover the image (denoising). Different diffusion models use different numbers of steps and different approaches for generating noise. Conditioning Conditioning introduces a dependency on additional information (text prompts in our case). This will allow us to generate samples that meet specific constraints. We have to convert the text prompt to embedding to capture the semantics of our prompt (“e.g.: astronaut riding bicycle”).


We train denoising to adhere more closely to the textual semantics of our semantics. Latent Space The last building block is latent space. Typically, generative models operate in pixel space. However, inference in pixel space is expensive, and pixel space is sparse. The solution is to compress our initial image into a more compact representation, which exhibits better scaling properties with respect to the spatial dimensionality. This compression is achieved using VAE (variational autoencoder models). This type of encoding differs from “vanilla“ autoencoders; it encodes images to a distribution with permitters μ and σ. This approach allows the introduction of generative properties for latent space. I will not explain the details here. Check out this post for more details. The diffusion process is conducted in latent space. Putting Everything Together Now, we studied all key components. It’s time to put everything together. The whole process step-by-step: We get our prompt “astronaut riding bicycle.“
The prompt is compressed to embedding representation; this representation will condition generation.
The denoising process is performed in latent space with respect to the condition.
The last step state in latent space is decoded to pixel space.
We finally got our picture! Links/Papers to Read High-Resolution Image Synthesis with Latent Diffusion Models: https://arxiv.org/abs/2112.10752
Auto-Encoding Variational Bayes: https://arxiv.org/abs/1312.6114
Reproducing Stable Diffusion from scratch, easy to read GitHub repo https://github.com/juraam/stable-diffusion-from-scratch The field of artificial intelligence is booming; image generation is one of the most prominent domains. There are a lot of competitors on the market, such as Stable Diffusion, Midjourney, and DALL-E. All these products are based on the diffusion networks approach. Unlike GPT diffusion models often seen as abstruse, the goal of this story is to demystify the complexity of diffusion models and explain how they work in an easy but comprehensive way. Let’s go! The Basics The Diffusion networks are artificial neural networks that allow you to solve various generation tasks. The most well-known application is image generation. In this story, let’s focus just on this type of generation. There are countless flavors of generation tasks: image2image, image2video, text2vide, video+image2video, etc. But all of them are based on a similar structure. The diffusion network is composed of 3 building blocks (neural networks): Diffusion process: a process of image generation; Conditioning: a way to restrict generation to a given subject; Latent space: a trick that makes the model feasible to run in given hardware constraints. Diffusion process: a process of image generation; Conditioning: a way to restrict generation to a given subject; Latent space: a trick that makes the model feasible to run in given hardware constraints. Diffusion Process The diffusion processes concept comes from physics, which describes the way particles spread in a medium over time. In the context of machine learning, a diffusion model learns to progressively "refine" a noise vector into meaningful data by reversing a diffusion-like process training set created from images by gradually adding more and more noise to each image. To sample/generate from this model, we apply the network multiple times to minimize the noise and recover the image (denoising). Different diffusion models use different numbers of steps and different approaches for generating noise. Conditioning Conditioning introduces a dependency on additional information (text prompts in our case). This will allow us to generate samples that meet specific constraints. We have to convert the text prompt to embedding to capture the semantics of our prompt (“e.g.: astronaut riding bicycle”). We train denoising to adhere more closely to the textual semantics of our semantics. We have to convert the text prompt to embedding to capture the semantics of our prompt (“e.g.: astronaut riding bicycle”). We have to convert the text prompt to embedding to capture the semantics of our prompt (“e.g.: astronaut riding bicycle”). We train denoising to adhere more closely to the textual semantics of our semantics. We train denoising to adhere more closely to the textual semantics of our semantics. Latent Space The last building block is latent space. Typically, generative models operate in pixel space. However, inference in pixel space is expensive, and pixel space is sparse. The solution is to compress our initial image into a more compact representation, which exhibits better scaling properties with respect to the spatial dimensionality. This compression is achieved using VAE (variational autoencoder models). This type of encoding differs from “vanilla“ autoencoders; it encodes images to a distribution with permitters μ and σ. This approach allows the introduction of generative properties for latent space. I will not explain the details here. Check out this post for more details. VAE this post The diffusion process is conducted in latent space. Putting Everything Together Now, we studied all key components. It’s time to put everything together. The whole process step-by-step: We get our prompt “astronaut riding bicycle.“ The prompt is compressed to embedding representation; this representation will condition generation. The denoising process is performed in latent space with respect to the condition. The last step state in latent space is decoded to pixel space. We finally got our picture! We get our prompt “astronaut riding bicycle.“ The prompt is compressed to embedding representation; this representation will condition generation. The denoising process is performed in latent space with respect to the condition. The last step state in latent space is decoded to pixel space. We finally got our picture! Links/Papers to Read High-Resolution Image Synthesis with Latent Diffusion Models: https://arxiv.org/abs/2112.10752 Auto-Encoding Variational Bayes: https://arxiv.org/abs/1312.6114 Reproducing Stable Diffusion from scratch, easy to read GitHub repo https://github.com/juraam/stable-diffusion-from-scratch High-Resolution Image Synthesis with Latent Diffusion Models: https://arxiv.org/abs/2112.10752 https://arxiv.org/abs/2112.10752 Auto-Encoding Variational Bayes: https://arxiv.org/abs/1312.6114 https://arxiv.org/abs/1312.6114 Reproducing Stable Diffusion from scratch, easy to read GitHub repo https://github.com/juraam/stable-diffusion-from-scratch https://github.com/juraam/stable-diffusion-from-scratch

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