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The concept of ontologies plays a pivotal role in organizing and making sense of the vast information available. In data management, ontologies are critical for enhancing data interoperability, integration, and analysis across various domains and platforms. They provide a structured framework that enables data from disparate sources to "speak" the same language, facilitating more effective and utilization. data sharing Ontologies are more than just data schemas; they are comprehensive mappings of knowledge domains that include the vocabulary associated with a domain and the relationships between those terms. This foundational aspect of data architecture not only aids in categorizing and storing data but also in its discovery, analysis, and application in solving real-world problems. Understanding Ontologies At its core, an ontology in data management is a way to represent the knowledge of a particular domain. It's a structured framework that describes the types of entities within that domain and their relationships. This allows for a shared understanding of a domain that can be communicated across people and computers. Key Components of an Ontology : These are the fundamental categories of objects or concepts within a domain. For example, in a healthcare ontology, classes might include "Patient," "Disease," and "Treatment." Classes (or Concepts) : These define how classes are related. For instance, the relationship "hasSymptom" might connect the "Patient" class to the "Disease" class. Relationships (or Properties) : These are the actual data points or objects that belong to each class. An instance of the "Patient" class might be "John Doe." Instances (or Individuals) Ontologies vs. Taxonomies vs. Schemas While these terms are sometimes used interchangeably, they have distinct meanings: provide a rich domain description, including classes, relationships, and instances. They enable reasoning about the entities within a domain. Ontologies are hierarchical classifications of entities, focusing on the subclass-superclass relationships. They are more straightforward than ontologies, not including specific properties or relationships between classes beyond the hierarchical structure. Taxonomies are structures that define data organization within a database, including tables, fields, and the relationships between tables. They are more about data structure than the representation of domain knowledge. Schemas Examples of Ontologies Ontologies are used across various domains to facilitate data sharing and integration: In healthcare, ontologies like SNOMED CT help standardize medical terminology across different systems. In e-commerce, ontologies can represent product categories and their attributes to enhance search and recommendation systems. In finance, ontologies can define the relationships between financial instruments, markets, and entities to aid data analysis and compliance reporting. The Role of Ontologies in Data Architecture Ontologies serve a crucial function by establishing a common vocabulary and set of relationships that can be used to describe and understand data from different sources. This standardization is critical to achieving semantic interoperability, allowing disparate systems to exchange data with unambiguous, shared meaning. Enhancing Data Interoperability Ontologies enable different data systems, applications, and services to communicate effectively by providing a shared understanding of the domain concepts and relationships. This is particularly important in environments where data must be combined or compared across different sources, such as in data lakes or when integrating legacy systems with new applications. Facilitating Data Integration By defining a standard set of terms and structures, ontologies help map data from diverse sources into a unified model. This simplifies the data integration process and ensures that the integrated data maintains its context and meaning, enabling more accurate and meaningful analysis. Supporting Data Discovery and Governance Ontologies play a vital role in data discovery by providing a structured way to tag and categorize data, making searching for and locating relevant information more accessible. They also contribute to effective data governance, offering a framework for ensuring data quality, consistency, and compliance across the organization. Real-World Application Consider a multinational corporation that operates in various sectors, including finance, healthcare, and retail. Each of these sectors may use different systems and data formats. Ontologies can provide a unified view of the company's data assets, enabling cross-sector analysis and strategic insights that would be difficult to achieve with isolated data sets. The Impact on Data Architecture Incorporating ontologies into data architecture necessitates a thoughtful approach to design and implementation. It requires collaboration between domain experts, data architects, and IT professionals to ensure that the ontology accurately represents the domain knowledge and can be effectively used across systems and applications. The goal is a flexible, scalable, and semantically rich data architecture that adapts to changing business needs and technological advancements. Managing Ontologies in Data Architecture The effective management of ontologies is critical to leveraging their full potential within data architecture. This involves a lifecycle approach encompassing development, integration, governance, and maintenance. Here, we outline the key phases and considerations for managing ontologies in a way that supports robust data architecture. Development and Design Developing an ontology requires a systematic approach, beginning with a clear understanding of the domain and the organization's needs. : Identify the ontology's goals, scope, and stakeholders. Determine what problems it will solve and how it will be used. Requirement Analysis : Gather and define the key concepts, relationships, and properties that make up the ontology. This often involves collaboration with domain experts. Conceptualization : Translate the conceptual model into a formal ontology language, such as OWL (Web Ontology Language). This step makes the ontology machine-readable and ready for integration. Formalization : Deploy the ontology in a suitable environment and populate it with instances (data). Implementation : Establish processes for updating and refining the ontology over time, considering new requirements or changes in the domain. Maintenance Integration and Deployment Integrating ontologies into existing data architectures poses several challenges, from technical implementation to user adoption. : Ensure the ontology is compatible with existing data systems and standards. This may involve developing custom APIs or middleware. Technical Integration : Map existing data to the ontology, a process that may require significant transformation or enrichment of the data. Data Mapping : Educate stakeholders on using the ontology effectively, including how to query and interpret the results. User Training Governance and Maintenance Ongoing governance and maintenance are essential for ensuring the ontology remains relevant, accurate, and valuable. : Use version control systems to track changes to the ontology, allowing for rollback if necessary and understanding the evolution of the ontology. Version Control : Regularly review the ontology for accuracy, consistency, and completeness. This may involve automated testing or manual review by domain experts. Quality Assurance : Establish a straightforward process for updating the ontology, including who is authorized to make changes and how changes are reviewed and approved. Update Process Managing ontologies within data architecture is a complex but rewarding endeavor. When effectively developed, integrated, governed, and maintained, ontologies can significantly enhance data interoperability, integration, and analysis capabilities. They provide a structured and semantic framework that supports the organization's data needs, facilitating better decision-making and innovation. Use Cases and Applications The practical applications of ontologies in data architecture span various industries and functions, demonstrating their versatility and value in organizing and interpreting complex data landscapes. Here, we highlight some key use cases where ontologies have been instrumental in driving data interoperability, enhancing analysis, and supporting decision-making processes. Healthcare: Enhancing Patient Care and Research In healthcare, ontologies like SNOMED CT and LOINC have been crucial in standardizing medical terminology across different systems and geographies. These ontologies enable: : Facilitating the exchange and interpretation of patient data among healthcare providers, leading to improved patient care. Interoperable Patient Records : Supporting the aggregation and analysis of clinical data from diverse sources, accelerating research efforts, and the development of new treatments. Clinical Research E-commerce: Improving Product Discovery and Recommendations E-commerce platforms leverage ontologies to categorize products and define their attributes, significantly improving the customer experience through: : Allowing users to find products more efficiently through semantic search techniques. Enhanced Search Functionality : Enabling more accurate recommendations by understanding the relationships between different products and user preferences. Personalized Recommendations Finance: Risk Management and Compliance Reporting Financial institutions use ontologies to model complex relationships between different financial instruments, markets, and regulatory requirements, aiding in: : Providing a comprehensive view of an institution's exposure to various types of financial risk. Risk Analysis : Streamlining the process of aggregating and reporting data following different regulatory frameworks. Regulatory Compliance Environmental Science: Biodiversity Conservation Ontologies are applied in environmental science to model the complex relationships between species, habitats, and environmental factors, supporting: : Facilitating the organization and analysis of data on species distribution, conservation status, and ecological roles. Biodiversity Studies : Aiding in developing effective conservation strategies by providing a holistic view of ecosystem dynamics. Conservation Planning Future Trends: Ontologies and AI Integrating ontologies with artificial intelligence (AI) and machine learning (ML) represents a promising development area. Ontologies can provide the structured, semantic frameworks that AI models need to understand and reason about the world, potentially leading to breakthroughs in natural language processing, knowledge representation, and automated decision-making. These use cases illustrate the transformative potential of ontologies in enhancing data architecture across various domains. By providing a structured way to represent and link data, ontologies enable organizations to unlock deeper insights, drive efficiencies, and innovate more effectively. As data grows in volume and complexity, the role of ontologies in managing this data landscape will only become more critical. Conclusion In closing, the journey to effective data management is ongoing, and ontologies represent a powerful tool. By fostering a deeper understanding of ontologies and their application in data architecture, we can look forward to a future where data is not just abundant but truly understandable and actionable.

The Role of Ontologies in Data Management

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SingularityNet (AGIX) Investors Hock Onto AI Giant Option2Trade's (O2T) $888K Giveaway

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2024 is bringing some of that futuristic magic, with a twist, right to our real-world doorstep! Get ready for tech trends that'll bend your brain and blast your expectations into orbit. 1. Sentient AI: Can Machines Dream? Remember HAL from 2001: A Space Odyssey? Or maybe Samantha from Her? Those sci-fi movies where robots start thinking on their own and even feeling emotions are captivating but also a bit unsettling. But what if I told you the line between fiction and reality in this regard is starting to blur in 2024? Buckle up because we're diving deep into the fascinating and slightly scary world of . sentient AI Don't hold your breath for sentient robot buddies just yet, but prepare for mind-blowing AI advancements! While sentience remains a fuzzy concept, DeepMind's co-founder, Demis Hassabis, assures us that machines are about to unlock abilities that'll leave us speechless. The future of intelligence is wilder than you think. Here's what we know (or think we know): AI is getting better at understanding language and emotions. Some AI models can generate human-quality text and even adapt their responses based on the emotional tone of a conversation. AI is learning to learn. This means they can improve their skills and abilities over time without explicit programming. Brain-computer interfaces are bridging the gap between humans and machines. These technologies could one day allow us to directly communicate with AI using our thoughts, offering deeper insights into their internal processes. But here's the catch: We still need to understand consciousness or sentience fully. These are complex concepts that, even for humans, are difficult to define, let alone measure in machines. Some argue that true sentience requires a physical body. Without experiencing the world through senses like touch and taste, can an AI truly understand it? The ethical implications of sentient AI are immense. Questions about rights, responsibilities, and even potential dangers become very real if machines become truly conscious. 2. Brain-Computer Interface: Think It, Do It! Have you watched movies where characters control things with their minds? Like moving objects with a mere thought or typing without touching a keyboard? That sci-fi stuff is becoming a reality fast! 2024 is all about , and they're about to blow your mind (literally!). Brain-Computer Interfaces (BCIs) This technology is opening doors to amazing possibilities: No more drilling holes in your skull! These BCIs use sensors on your scalp to read your brainwaves, making them accessible to everyone. Non-invasive BCIs: Advanced AI algorithms are getting better at understanding your brain signals, translating them into real-world actions with greater accuracy and speed. AI-powered decoding: BCIs are helping diagnose and treat neurological disorders like Parkinson's and epilepsy, offering hope for millions of people. Medical breakthroughs: Here's a snapshot of how BCIs are already making a difference: Application Impact Communication Helping people with speech disabilities communicate by translating their thoughts into words. Gaming Immersing users in virtual worlds like never before, controlling characters and objects with their minds. Rehabilitation Assisting people with paralysis regain control of their limbs and regain independence. But of course, there are concerns! Neuroethics expert Frederic Gilbert flags confusing changes in how AI affects our core concepts of self, like personality and agency. He calls these qualities "obscure and opaque," highlighting the complex, hidden impacts of BCI on who we are. This raises these questions in mind! Who controls your thoughts? And how do we protect them from hacking or misuse? Safety and security: Should we enhance human minds with technology? Who decides who gets access? Ethical considerations: What happens when everyone can control things with their thoughts? Will it make our lives easier or more chaotic? Unforeseen consequences: 3. Quantum Supremacy Reigns Supreme: Well, forget fiction – that mind-blowing power is on the horizon, thanks to a tech trend called quantum supremacy. What is Quantum Supremacy, Anyway? Imagine a computer that doesn't just crunch numbers but harnesses the weird and wonderful laws of quantum mechanics. That's what quantum computers are about. They use qubits, which can be both 0 and 1 at the same time (unlike our regular bits that are either 0 or 1), to achieve mind-blowing speed and tackle problems that would take traditional computers millions of years. Think breaking complex encryption codes, designing life-saving drugs, or simulating the weather with mind-boggling accuracy. Here's why it's a big deal: Quantum Supremacy will solve problems beyond our wildest dreams. Forget traffic jams – quantum computers could optimize traffic flow in real time, minimizing congestion and saving valuable time. Forget incurable diseases – quantum simulations could accelerate drug discovery, leading to breakthroughs in treating currently untreatable illnesses. The possibilities are endless! It's not just about speed; it's about a whole new way of thinking. Quantum computers approach problems differently, opening doors to entirely new solutions and innovations we have yet to consider. It's like unlocking a hidden room of possibilities in the vast library of science and technology. Alan Baratz, The CEO of D-Wave, thinks that the US is finally playing catch-up in using quantum computing to solve real-world problems. Still, countries like Australia, Japan, the UK, and the EU have already been swiftly taking steps to use quantum solutions to address public sector issues. Expect to see this trend explode in 2024. Stay curious, stay informed, and get ready to witness the dawn of a new era in computing. The future is quantum, and it's coming faster than you think! 4. Hyperloop Highways: Warp Speed for Everyone! Stuck in traffic jams? Not anymore! Picture zipping from city to city in minutes, not hours, thanks to hyperloop technology. Imagine vacuum tubes and magnetic levitation whisking you across the country at supersonic speeds. Get ready for shorter travel times, greener transportation options, and a whole new way to explore the world! Here's why Hyperloop is the next big travel trend: Forget traffic jams and delayed flights. Hyperloop can slash travel times between major cities, revolutionizing our transportation system. Speed Demons: Ditch the carbon footprint! Hyperloop uses renewable energy sources and produces much less pollution than traditional transportation. Eco-Friendly Flyer: Imagine a network of Hyperloop tubes crisscrossing continents, connecting cities like never before. It could open up new economic opportunities and boost global trade. City Connector: Buckle up for a smooth, silent, and exhilarating journey! Hyperloop promises a futuristic travel experience unlike anything we've ever seen. The Thrill of the Ride: But hold on, there are some bumps on the road: Building and maintaining Hyperloop infrastructure is expensive. Finding investors and making it affordable for everyone will be crucial for widespread adoption. Cost Concerns: New technology comes with new risks. Ensuring the safety and security of Hyperloop systems is paramount. Safety First: Governments need to develop regulations and legal frameworks for this disruptive technology before it can take off. Regulatory Hurdles: Despite the challenges, the potential of Hyperloop is undeniable. Companies like Virgin Hyperloop One and Hyperloop One are already making significant progress, with test tracks operational in several countries. Dirk Ahlborn, the CEO of Virgin Hyperloop, also hopes that the ongoing advancements and approval from regulators will make hyperloop a fundamental part of transportation systems in the coming decade. 5. Bioprinting Reboots Medicine: Bioprinting, using 3D technology with living cells, is reshaping healthcare. Prepare for potential cures for currently untreatable diseases, personalized cancer treatments, and maybe even printing replacement lungs or hearts! Here's why it's a game-changer: Imagine no more endless waits for organ transplants! Bioprinting opens the door to creating personalized tissues and organs specifically designed for each patient. This means shorter waiting times, fewer complications, and potentially even printing replacement organs like lungs or hearts! Goodbye Waiting Lists, Hello Hope: Forget guesswork! Bioprinting lets scientists build tiny replicas of human organs on a chip, allowing them to test new drugs and treatments with incredible precision. This means safer, more effective medicines and fewer failed trials with real patients. Drug Testing Revolution: No two bodies are alike, and neither should their treatments. Bioprinting allows doctors to create tissues that match your unique genetic makeup, leading to personalized therapies and better outcomes for everyone. Personalized Medicine Takes Center Stage: Imagine damaged nerves and tissues healing themselves! Bioprinting can create scaffolds for your cells to grow on, promoting natural regeneration and potentially reversing injuries that were once considered permanent. Regenerative Medicine Gets a Boost: But wait, there's more! This is just the beginning of the bioprinting story. We're also seeing exciting developments in: This could revolutionize surgery by ensuring adequate blood flow to transplanted organs. Printing blood vessels: Imagine offering rapid relief and promoting faster healing for burn victims. Bioprinting skin for burn victims: Building custom bone replacements could speed up recovery times and make surgeries less invasive. Printing bone grafts: Is it all sunshine and rainbows? Not quite. Bioprinting is still in its early stages, and there are challenges to overcome: These are the materials that "print" the cells, and we need to ensure they're safe, compatible, and support cell growth. Finding the right bioinks: Printing complex organs is still a delicate process, and making it efficient and affordable for widespread use is key. Scaling up production: Who owns the printed tissues? What are the implications for genetic engineering and human enhancement? Ethical considerations: But despite the challenges, the potential of bioprinting is so vast it's hard to ignore. It's like getting a glimpse into a future where medicine is personalized, regenerative, and potentially cures diseases we once thought untreatable. Wrapping Up! Keep an eye out for flying cars (not just for billionaires!), AI-powered homes that manage your life, and sustainable tech that cleans our planet and heals our environment.

Hold Onto Your Hats, Tech Voyagers: 2024's Mind-Bending Trends are Here!

Knowledge base vs LLM What if we could create a digital inventory of all human knowledge, an encyclopedia but ordered in such a way that complex queries can be answered? I can hear you shout: ‘Isn’t that a bit like a LLM trained on Wikipedia?' Kind of … Except that such would have created the inventory itself, leaving the connections between facts opaque. This is so because all text input data fed into an LLM is unstructured and treated on equal footing. It’s the internal attention mechanism that starts organising the text.  So, although we can ask complex queries in the form of natural language and retrieve an answer in the same medium, there is no knowledge inventory accessible to us. It’s as if the LLM is giving a press conference but leaves us dry with ‘no further questions’ after the first round. an LLM Is that better than having no query capability at all? Well, that’s really a mis-apperception of the playing field. The choice isn’t between LLM-like query magic and nothing at all. The choice is between LLM-like query magic and incomplete yet carefully crafted knowledge bases (search engines can answer queries but do so by pointing to documents on the web rather than providing the answer themselves). Using the earlier press conference analogy, the choice is between listening to a spokesman with a vast vocabulary and asking the actual protagonists at the risk of coming home empty-handed. Medical Ontology exposing LLM limits. The foundational model of anatomy (FMA) is such an attempt an create an inventory of human knowledge, in this particular case, all knowledge related to the structural organisation of the human body at micro- and macroscopic levels. A prima facie uncontroversial choice of fundamental relationship to be used in this knowledge base is the partonomic inclusion relationship. For example, . Little spoiler… this slicing up of the conceptual space doesn’t cut it (pun intended). I’ll explain to you why. However, let’s first focus on what this means within the here-discussed wider context. cytoplasm part_of cell We pride ourselves on having LLMs which can assist us in digging into vast amounts of knowledge, conveniently ignoring the fact that we have no real insight into how facts are connected within the model, while our own attempts to connect facts in the most rigorous fashion have turned out to be treacherous endeavours. The press conference gives us the impression of having received some reliable answers while even the protagonists themselves can’t really recall what has happened. If we’re a member of the public relationships committee, this might not worry us too much, but as a citizen with a vested interest in the upholding of liberal democracy, I might not want to rely too heavily on such reports. Partonomic inclusion has gone wrong Now, how can a relationship so seemingly innocent and straightforward as partonomic inclusion be so misleading? Here are some seemingly uncontroversial examples of such a relationship: human testis part_of human being Yes, but there are male and female human beings. So, every human testis belongs (or belonged if the human was subject to an unlikely accident) to a human, but not every human has a human testis. Solution? The part_of relationship is split into two sub-relationships: has_part and part_for. So, now we can state that the human testis part_for human being without committing to human being has_part human testis. The part_of relationship is salvaged as the conjunction of these two sub-relationships: A part_of B =def A part_for B & B has_part A. replication fork part_of nucleoplasm Yes, but only so during certain times during the cell replication cycle (i.e., the S phase). Biology read: https://www.nature.com/scitable/topicpage/replication-fork-stalling-and-the-fork-protection-14435782/ Solution? At first glance, there seem to be at least two possible ways out. Firstly, we could introduce time-dependent or phase-dependent mereological relationships. So, part_of could get a time-index to distinguish between different times at which a relationship is obtained. However, since the cell replication cycle consists of clearly defined phases, it’s logically more apt to introduce phase-dependent partonomic inclusion relationships. Secondly, instead of fiddling with the relationship itself, we can enrich the representational capacity of the relata (i.e., the things related). So, a replication fork is not just a replication fork tout court, nor is the nucleoplasm always the same entity. The partonomic inclusion of the replication fork in the nucleoplasm during the S phase can be represented as follows: replication fork part_of nucleoplasm_S. synaptonemal complex part_of chromosome Yes, but not in all cases. For example, in protozoan ciliates, the formation of a synaptonemal complex is not required for pairing homologous chromosomes. Biology read: https://pubmed.ncbi.nlm.nih.gov/24336924/ Solution? In this case, recourse is found in the ancillary axioms, which define the logical relationships between the fundamental mereological relationships at hand. If we want to represent ‘part of’ as ‘can be part of,‘ then we can introduce a less fundamental part_of_can relationship by defining it as an existential import relationship: A part_of_can B =def C(C is_a B & A part_of C). So, for this particular example, we’d have synaptonemal complex part_of_can chromosome, which is to say that there exists some chromosome for which a synaptonemal complex is part of it. The attentive reader will, at this point, see an alternative solution akin to one earlier exposed. Indeed, instead of introducing a secondary relationship, one can stick to the fundamental relationships and represent the facts by demultiplexing the relata. So, the fact ‘synaptonemal complex part_of chromosome_Animalia’ would be part of the knowledge base but not the fact ‘synaptonemal complex part_of chromosome_Ciliophora.’ ∃ Conclusion In short, prima facia well-rounded relationships need to be broken up and defined in more primitive terms or require ancillary axioms in order to capture the complexity of the reality one desires to represent. Moreover, these solutions are in tension with solutions that prefer relational and definitional parsimony at the cost of an increase in entity cardinality. Since these issues occur in medical ontologies designed by professionals, it should be clear that this task is under no circumstance an easy one. It highlights a perplexing double bind between, on the one hand, the subtlety of reality’s relational composition and, on the other hand, the flexibility of the natural language to squeeze and squash that marvelous detail into communicable trunks. Now, what are the implications of this insight on the state of AI’s contemporary playing field? If we aim for accuracy of representation, there might still be a role for classical AI or resurrections of its core ideas within deep learning paradigms. Conversely, if we aim for smoothness of communication, LLM might actually be capturing exactly what we want… were it not for the fact that we, humans ourselves, have already mastered the skill of human language so that LLM become what sex dolls are in comparison to real intimacy. Sources Paper on which this article is based: Bary Smith and Cornelius Rosse - The role of foundational relations in the alignment of biomedical ontologies: https://pubmed.ncbi.nlm.nih.gov/15360852/

Saving Medical Ontologies with Formal Logic: A Tale of Caution and Hope for Classical AI

What is a skills-based economy, and how is LinkedIn moving from vision to implementation? As LinkedIn Director of Engineering Sofus Macskássy shares, there’s AI, taxonomy, and ontology involved in building the Skills Graph that powers the transition. Skills are the new currency. That’s a bold statement coming from LinkedIn CEO Ryan Roslansky. Roslansky makes the case for the so-called skills-first economy based on both anecdotal evidence and data. Skills-first hiring was mentioned in the 2022 State of the Union address, and a growing number of CEOs are calling on the need for companies to shift how they hire. In addition, that the skill sets for jobs have changed by around 25% since 2015. By 2027, this number is expected to double. That means jobs are changing on you even if you aren’t changing jobs, just as business demands are changing on you even if you’re not changing your business. recent LinkedIn data shows That was not the first or the last time Roslansky made that point. In 2021, to help transition the hiring market from focusing solely on titles and companies, degrees, and schools to also focusing on skills and abilities. In his 2021 post, Roslansky announced new LinkedIn features and services. He also referred to AI-driven changes, a prescient point . LinkedIn’s CEO outlined a vision brought forward again in 2023 Roslansky is not alone in identifying this shift. In 2018, we posited that . In 2019, we followed that up with more analysis on the relationship between the , , and knowledge graphs. in a rapidly shifting job market, being able to formalize skills is a requirement for job seekers and employers going forward future of work skills In 2021, Roslansky first referred to the LinkedIn Skills Graph. The Skills Graph was introduced to help create a common skills language, and it’s powering a multitude of LinkedIn features and services, as well as . In 2022, LinkedIn Director of Engineering Sofus Macskássy elaborated on how . Microsoft Viva LinkedIn’s Skills Graph is being built to power a skills-first world https://www.youtube.com/watch?v=tiE9ZMgfJKM&embedable=true Further details on were shared earlier in 2023. Today, Macskássy and his team are sharing more details on how they are . We caught up with Macskássy to discuss the journey from vision to implementation. building and maintaining the skills taxonomy that powers LinkedIn’s Skills Graph extracting skills from content to fuel the LinkedIn Skills Graph LinkedIn skills taxonomy: Helping everyone speak the same “skills language.” As Macskássy pointed out, the story of skills on LinkedIn goes way back. LinkedIn users have long been able to add skills to their profiles and endorse each other. LinkedIn understood that skills represent an important vocabulary by which people communicate and understand each other. But at some point the realization settled in that this goes beyond just finding a job or finding talent. When the CEO highlighted LinkedIn should move into a skills-first world, there was a question – what does that even mean? From a technical perspective, the team needed to understand what the vision was. As the team started digging into the various product lines as well as the news feed, advertising, recommendation, and search, they realized that skills could be leveraged as signals to aid ranking and recommendation. But going from vision to implementation has not been without obstacles. Many people didn’t have skills listed on their profiles. Even when they did, people did not necessarily use the same vocabulary. It was not easy to figure out whether the two skills were related to each other or even synonyms of each other. This is what Roslansky’s mention of helping everyone speak the same “skills language” alluded to. To resolve ambiguity, the first approach was to build out the . The skills taxonomy is where LinkedIn organizes and categorizes skills based on their hierarchical relationships with each other. LinkedIn skills taxonomy Each skill is represented by a “node” in the skills taxonomy, and nodes are linked together to form a hierarchical skill network through “edges” called knowledge lineages. Knowledge lineages reflect how two skills relate to each other. Skills may relate for various reasons, such as the skills are both part of a career specialization or one skill is for a tool that is used to apply another skill. To create a stronger network of connected skills, a framework called “Structured Skills” is utilized. This framework increases understanding of every skill by mapping the relationships it has to other skills around it. Building the Skills Graph with AI and humans in the loop The connected skills taxonomy is curated by a combination of human taxonomists and machine learning. This Human-In-The-Loop approach to building the Skills Graph helps grow the taxonomy at scale while ensuring the skills data meets the required quality and standards. “It is a feedback loop where we see new skills and new ways of mentioning skills across the board. We use this to expand the Skills Graph dynamically as we see new ways of phasing a skill or even a new skill we never had seen before. For example, prompt engineering is now a new skill that really popped up over the last couple of quarters. So we want to dynamically add it to the Skills Graph. We then use these new skills that can subsequently be tagged in the content”, Macskássy said. That is a continuous process, and the cadence by which it’s done depends on the type of content. The skill harvesting process happens on demand as content is updated. Potential additions or improvements to the Skills Graph are batched, so reviews and updates don’t have to be executed every time any single update happens. In 2022, LinkedIn’s taxonomy consists of over 39,000 skills spanning 26 languages, over 374,000 aliases (different ways to refer to the same skill – e.g., “data analysis” and “data analytics”), and more than 200,000 links between skills. In 2023, there are more than 41,000 skills. Instead of relying only on taxonomists to manually curate over 41k skills, LinkedIn applies machine learning techniques to help scale the taxonomy construction. This includes a tool LinkedIn developed, KGBert which was inspired by , a supervised model that applies a deep semantic understanding of skills to predict relationship lineages. KG-Bert From taxonomy to ontology By utilizing machine learning models, LinkedIn can extract and map skills from diverse content sources and collect feedback for continuous model improvement and member value. To do this, large pieces of text (such as job descriptions and resumes) first need to be segmented out into meaningful parts. Mentions of skills can then be removed from each piece of the text. Once extracted, they are normalized into canonical/single representations (i.e., “data analytics” and “data analysis” are the same type of skill), represented in the skills taxonomy. Attention also has to be paid to where a skill sits in a piece of content and what type of content it is. Skills are often represented differently in resumes, member profiles, or job descriptions, so models are fine-tuned to learn the specifics of those types of content. To address these nuances, LinkedIn built an architecture and platform that caters to the challenges of extracting skills and mapping them onto the Skills Graph. The details the AI model workflow that extracts and maps skills from raw text, such as job postings, as well as the architecture for serving models at scale. post that Macskássy’s team published As former AI Division Technical Lead for Taxonomies and Ontologies at LinkedIn Mike Dillinger points out, however, . They seem simple, flexible, and familiar. They are widely used. And they seem to work across many use cases and many domains. taxonomies are the duct tape of connected data But when looked at in more detail, taxonomies turn out to be crude tools for knowledge organization that are difficult to create, to scale, to adapt, to align, and to build on. A key reason is the fact that they are limited to modeling hierarchical relationships, which are only a small part of the rich relations connecting entities in the real world. Some of the technical details shared on the Skills Graph allude to harvesting and exploiting more than hierarchical relationships. Macskássy verified that LinkedIn utilizes an ontology used not only for mapping skills between branches but also for other concepts. That has provided great improvements when it comes to ranking and recommendation, with more details to be unveiled in due course. Explicit and implicit skills Applications across LinkedIn include career-relevant skills and job-important skills. The approach has resulted in performance improvements in Job Recommendation, Job Search, and Job Member Skills matching. As part of the process, recruiter skill feedback and seeker skill feedback are collected. When a recruiter manually posts a job on LinkedIn, a list of skills, pulled by LinkedIn’s AI model, is suggested after they fill in the posting content. A recruiter can edit this list depending on if they believe a skill is important. Similarly, when a job seeker opens a job posting on LinkedIn, a feature will show how many skills overlap between their profile and the job. Seekers can review the top 10 skills used for skill matching calculation, and if a certain skill is irrelevant to the job, they can provide feedback. Obviously, harvesting and utilizing those “implicit” skills, in addition to skills explicitly listed by members is something LinkedIn has put a lot of effort into. And there are anchors to surface those implicit skills and feedback from job seekers and recruiters on both ends of the recruiting process. But what about LinkedIn members who are not actively recruiting or being recruited? Wouldn’t they benefit from the opportunity to see the skills LinkedIn harvested from their content and perhaps be prompted to add them to their profiles too? Furthermore, explicit skills are part of a member’s profile and, therefore, get to be exported if members so choose. What about implicit skills? Macskássy noted that there are surfaces and flows by which members can add skills. LinkedIn continues to improve those flows where suggestions are made based on member resumes and profiles. Members are asked whether they might want to add certain skills and even associate certain skills with certain of their jobs as well. However, prompting is not done in an overly aggressive way. As for implicit skills, Macskássy said those are not necessarily surfaced because they are more fluid in nature. It makes less sense to surface all of these, particularly as LinkedIn is moving forward into how to think about skills more dynamically and more in-depth, he added. So, those skills are not considered as part of member profiles and are not exported either. Skill provenance, credibility, and depth There are more dimensions of implicit skill harvesting that are worth highlighting: provenance, credibility, depth, and interoperability. Since implicit skills are extracted from content that members themselves provide, is there a way to evaluate the weight this content carries or does it have to be taken at face value? The content that members provide is how they want to represent themselves, Macskássy said. So that has to be taken somewhat at face value, as it all comes down to trust. LinkedIn has not observed that members are stretching the truth because there are a lot of people looking at their professional profiles, and they would be called out, Macskássy added. https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/how-linkedin-is-moving-towards-a-skills-based-economy-with-the-skills-graph?embedable=true However, there is a mechanism through which the depth of member expertise can be assessed. (SAs) are adaptive assessments designed by LinkedIn Learning experts to evaluate and validate skills across a range of domains. LinkedIn Skill Assessments These short-form assessments are accessible through the profile skills section, where members can click on the “Take skill quiz” button to access a list of SA recommendations. Upon successfully passing a SA with a 70th percentile or higher score, members are awarded a “verified skill” badge that they can display on their profile page and is visible to recruiters. Skill assessments, as well as other learning material, align with skills in the skills taxonomy. It is represented internally in member data and can be part of the ranking. The amount of SAs and certificates is still very lightweight compared to other content. LinkedIn is considering how to best elicit and leverage it, for example, by evaluating member interactions. Interoperability, Open Badges, and Future Work Something that could potentially help both in terms of interoperability, as well as content enrichment, is . Open Badges is a data specification initiated by Mozilla and the MacArthur Foundation. The goal is to provide a simple but flexible format for documenting and showcasing skills. A badge corresponds to a skill a person has, as recognized by a third party. Open Badges That means that by using Open Badges, LinkedIn members would be able to both import skills documented by third parties as well as export their skills in a standardized format. Macskássy said that the team is aware of Open Badges, however, the focus is primarily on what can help with the member experience on the platform. Open Badges is on the radar but has a low priority at this point. LinkedIn is investing in continuously improving skill understanding capabilities in approaches such as leveraging Large Language Models. Examples include using LLMs to provide rich descriptions of skills, fine-tuning LLMs to improve skill extraction, or leveraging embedding for skill representation. As the team notes, the LinkedIn Skills Graph is at the center of powering the skills-first transformation. The tech stack for mapping content to the Skills Graph enables constant updates and evolution of the Skills Graph to stay up to date on the always-changing skills landscape. Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives.Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. Also published . here

LinkedIn's Skills Graph: Paving the Way for the Skills-First Economy with AI and Ontology

Ontology Network has recently announced the launch of stONT, marking a significant stride toward liquid staking on its platform. This initiative moves beyond the conventional staking barriers, creating a pathway toward a flexible and accessible means to garner rewards. Unlike traditional staking tokens, stONT appreciates in value in ONG (Ontology Gas), instead of increasing in quantity, presenting a unique proposition for those keen on venturing into new territories of the staking universe. Key Features: Early Phase Engagement: The launch phase begins with the integration of 10 nodes to facilitate the stONT service, thereby laying a solid foundation for further testing and improvements. Accessibility: stONT is made easily accessible whether on a PC via node.ont.io or on mobile through the stONT dApp on ONTO Wallet, bringing the service closer to the users. Liquid Staking Adventure: Liquid staking emerges as a significant financial flexibility beacon within the blockchain arena. Unlike traditional staking where assets are locked up to earn rewards, liquid staking via stONT allows users to retain asset liquidity while still earning. This modern methodology represents a welcoming gateway for both seasoned stakers and newcomers, with the allure of compounding earnings being a lucrative facet that's likely to attract many to this innovative staking frontier. : Future Endeavours The upcoming collaboration with iZiSwap and Wing Finance indicates that the journey of stONT in the broader staking world is just beginning. Each collaboration widens the horizon, availing Ontonauts numerous pathways to explore and gain from. Crypto wallets & DeFi projects are invited to explore integration possibilities with stONT, widening the audience and showcasing the advantages of liquid staking. Community Invitation: Ontology extends an invitation to the community and partners to dive into the stONT venture, share invaluable feedback, and engage in this journey into new staking territories. The stONT initiative is more than just a token; it’s a ticket to a new staking adventure with boundless possibilities. Elevating Financial Empowerment: Ontology’s unveiling of stONT is a clear testament to its unwavering commitment to financial empowerment. Traditional Ontology staking has been a hallmark of financial autonomy, with a simplified process of connecting a wallet on the ONTO Wallet platform, specifying the amount of ONT tokens to stake, and affirming the staking action. The introduction of stONT adds a layer of flexibility and liquidity to this robust mechanism, allowing stakers to utilize stONT in other DeFi protocols without having to unstake their ONT tokens. This not only broadens financial activities but also enriches the user interface with the upcoming integration of various tabs such as Stake, Unstake, Dashboard, and Stats, making the staking journey more intuitive and informative. Ontology and stONT together are not just ushering in an era of financial independence but significantly enhancing it, allowing users to take charge of their financial destiny in the burgeoning digital economy. Through Ontology staking and the liquidity provision of stONT, financial empowerment transitions from a mere promise to an actionable reality. Want More Ontology? Learn more about our decentralized data and identity solutions on our and official account. You can also chat with us on and keep up-to-date with the latest news via our account. website Twitter , Telegram Telegram Announcement Other Resources / / / / / LinkedIn Medium Facebook Reddit Discord YouTube

stONT Launches on the Ontology Network: The Advancement of Liquid Staking

অন্টোলজি নেটওয়ার্কে stONT চালু করেছে: তরল স্টেকিংয়ের অগ্রগতি

stONT startet im Ontology Network: Die Weiterentwicklung des Liquid Staking

stONT se lanza en Ontology Network: el avance del Liquid Stake

stONT se lance sur le réseau d'ontologies : l'avancement du jalonnement liquide

ओन्टोलॉजी नेटवर्क पर stONT लॉन्च: लिक्विड स्टेकिंग की उन्नति

stONT がオントロジー ネットワーク上で開始: リキッド ステーキングの進歩

stONT, 온톨로지 네트워크 출시: 리퀴드 스테이킹의 발전

stONT é lançado na Ontology Network: O avanço do Liquid Staking

stONT запускается в сети Ontology: развитие ликвидного ставок

stONT, Ontoloji Ağında Başlatılıyor: Liquid Staking'in Gelişmesi

sONT ra mắt trên mạng Ontology: Sự tiến bộ của đặt cược lỏng

stONT上线本体网络：流动性质押的进步

Goal: put software on hardware It's the oldest business model in Silicon Valley: Build custom hardware. Write software to run on that hardware. Get paid to use this combo to solve people's problems. This three-step formula has generated trillions in market capitalization— , , and are all good examples of this strategy at work. Hardware builds their moat and software pads their margin. It's simple! Apple Nvidia AWS Problem: it's hard though Unfortunately, for robots and other embedded devices, no one has really perfected this strategy's technical underpinnings. While many device-specific, well-architected systems exist, the core challenge of how to install your software on your custom hardware remains canonically unsolved. Between two worlds This problem endures because these products exist at the intersection of two worlds. They're embedded devices, sure—robots usually run on batteries, move around their physical environment, and regularly experience shoddy network connectivity. But modern robots often have much more computing capacity than your average smartwatch or refrigerator. In addition to their drivetrain and sensor arrays, these devices often have CPUs, GPUs, and disk capacity to rival a top-end server rack. And of course, they have all the added management complexity that comes with these capabilities. Since all new robots are custom hardware, every new robot has slightly different hardware specifications, and thus, software deployment mechanisms. For engineers building an embedded software deployment system, there is no singular, correct, generalized system design. Instead, we're left to pick and choose from the long list of available tools from both the embedded and data center domains. And the list of potential options to choose from is quite long. A preponderance of tools Common technologies for packaging, deploying, and configuring software on hardware, in either or both the embedded and data center domains, include: Containers ( , ) Docker Podman Other packaging mechanisms (Snapcraft, Flatpak, AppImage) Container orchestrators ( , ) Kubernetes Docker Swarm Deployment agents ( , , ) Argo CD Gitlab CI/CD Mender Embedded Linux ( , , , ) Ubuntu Core OpenWrt BuildRoot Yocto Configuration management systems (Ansible, Salt, Puppet) IoT cloud frameworks ( , , ) AWS Azure GCP Virtualization mechanisms (VMWare, KVM) ... and more. This article is not a guide to which of these technologies is best—all of them are appropriate for certain applications some of the time. Instead, stay focused on a high-quality design for your particular embedded software deployment system with one simple acronym: . The five requirements are . CRAVE Comprehensive, Reversible, Atomic, Versioned, and Everywhere Regardless of product domain, hardware complexity, or company growth stage, designs that fulfill these five requirements will serve your business well. What bots CRAVE Comprehensive Comprehensive software deployment systems ensure that . Depending on what you're deploying, comprehensive could refer to a single binary, an application with all of its dependencies, or an entire root file system. the entire set of changes is deployed when a deployment completes This means that when a deployment finishes, the entire system has been updated to a comprehensively known state. Only allow incremental patching if you can reliably and deterministically sum up those patches to a single, well-understood, deployed version of your software. It's important to note that "comprehensive" does not necessarily mean that the entire deployment payload gets pushed onto your device every time. For example, many deployment systems use caching mechanisms to minimize network transfer. Caching is great—just make sure you're always cache-busting correctly when necessary. Comprehensive deployments ensure that you've deployed everything you think you've deployed. If your deployment mechanisms don't include a way for you to do this, you'll eventually lose track of what's deployed on your devices! Designing your system to perform comprehensive updates also helps deliver reproducible deployments. Without comprehensiveness, you'll have no way of knowing that you've deployed the same thing across your device fleet, or that a piece of software that works on one machine will work when deployed onto another. Reversible Reversible deployment mechanisms . Ideally, if a deployment fails for some reason, the software system will roll itself back without any human intervention. can always be rolled back to the version they were on before the update It's ok if this is actually "roll forward", or "deploy again from onboard cache". The important design goal here is not to get stuck on a botched deployment, forcing yourself to commit to emergency engineering work if a bug is found after a release. Reversibility is essential for minimizing the risk associated with software deployments. Without it, every update is a dangerous endeavor—if the update goes awry, you'll be stuck fixing forward, or worse, troubleshooting a bricked device. Without reversibility, your engineering team will underperform. Because of the risks involved, a lack of reversibility will force your team to ship changes less frequently, iterate less freely, and build the product more slowly. Atomic Atomic deployment mechanisms ensure that . Effectively, for every software deployment from version A to version B, there should be a single, well-understood moment in time before which the system was running version A, and after which it was running version B. your changes take effect in their target system in a single, well-coordinated moment Depending on the specific deployment mechanism in question, atomicity can take many forms. For base operating system deployments, this is often a system reboot. For Kubernetes-orchestrated workloads, it could be a new Pod replacing an old Pod behind a Service. Regardless, atomic deployment mechanisms ensure that your individually-versioned units of software do not update incrementally. If you have one hundred virtual machines in an autoscaling group and you want to gradually transition from A to B across the cluster, that's fine—but at any given moment, you should know for certain whether any individual machine is running version A or B. Atomicity lets you pinpoint which version of a piece of software to associate with behavioral changes. If you don't know exactly when your changes took effect, you can't reliably troubleshoot which changes in your software caused them. Atomic deployments also help you avoid disruptive incremental changes in production that might introduce compatibility issues in the middle of a software release. If your software deployment process lacks atomicity, and you allow different components of your software to float between two versions while deployments occur, then must ensure that all combinations of those two versions across those components are compatible and function properly. This can be very expensive—and it's easy to avoid that expense if you keep your deployment systems atomic. Versioned If you haven't realized it by now, your software deployments need to be versioned. This means that . all changes to the contents of your deployment payloads need to be tracked and auditable over time At first glance, versioning is simple—just check your source code into git, right? In practice, however, this design requirement often gets overlooked. Are the binaries that your compiled executables link against also versioned? Have you pinned all of your package dependencies? Do you know where those dependencies come from? Versioning is essential if you want to actually know what software you are running on your device after a deployment. It is a critical component of reproducibility, too—without versioning, you can't ever really know if a deployment on one device will work on another, because you have no reliable mechanism for telling whether the two devices are actually running the same software. If a system component's version is floating or unspecified, a change to that version might break your system's functionality in a way that would be very difficult to root cause. Incorporating versioning into your deployment system design prevents this expensive mistake. Everywhere For software deployment systems, "everywhere" means "everywhere at your company". This means that you should strive to use the same deployment mechanisms in your: Feature development processes. Quality assurance and testing procedures. Manufacturing line software installation. Production software over-the-air update systems. In other words, don't invent different deployment systems for different parts of your company or workflow. Abandoning this design requirement can be tempting, but you will regret it eventually. Over time, changes and fixes will make it into some deployment systems, but not others. And as the implementation details diverge, software that works properly in one part of your company will stop working in another. This will cause chaos and mistrust, and slow down your feature delivery. Once this deployment system divergence takes hold, it will naturally worsen over time. As separate individuals and teams run into deployment issues, they'll develop local, ad hoc solutions to solve the problems they run into. This leads to your engineering organization duplicating lots of automation work while delivering product updates more slowly and less reliably. Build what bots CRAVE The five requirements of the CRAVE framework are an ideal—it's probably impossible to build a system that fulfills them all completely. Software engineering is a game of tradeoffs—for every product, at every company, engineers should prioritize these criteria as time allows, weighing them against the needs of their employers. Even if you can't build exactly the deployment systems your robots CRAVE, it's still worth it to get as close as possible to this ideal. The five design criteria outlined in this article can help steer your company away from the classic "works on my machine" quagmire, where intermittent, environment-specific bugs are never root-caused or fixed and software quality rots away over time. Tell your friends! 👇 Also published here.

What Robots CRAVE: How to Deploy Software Onto Hardware Platforms

Load Balancing Strategies for Applications: From Infrastructure to Code

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan;
(2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan;
(3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan;
(4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;
(5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Setup Result Summary and Discussion Appendix A. Dual Energy Formalism B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime References REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. 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L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934 This paper is available on arxiv under CC 4.0 license. Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Abstract and Intro Setup Setup Result Result Summary and Discussion Summary and Discussion Appendix Appendix A. Dual Energy Formalism A. Dual Energy Formalism B. Convergence of Results B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime C. Change of the Mass Loss Rate in MHD Regime References References REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. 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L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934

Magnetic Wind From a Massive White Dwarf Merger: References

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 38 of 38. Conclusion As the NSS and NCS note, the 2020s are a decisive decade, and actions taken now will shape the contours of cyberspace, digital technologies, and the digital economy for the future. As it implements this strategy, the Department of State will work with Congress and interagency partners to evaluate current cyber authorities and to amend or create authorities as needed for the Department to keep pace with evolving cyber and digital technologies. Building innovative, secure, and rights-respecting digital ecosystems is a process that will extend beyond the timespan of this strategy, and likely to be characterized by progress, pauses, and reversals. There will be, however, some early signposts that will indicate the United States, allies, and partners are moving forward. First, the United States, allies, and partners, along with the private sector and civil society, will build on the early successes of the G7-Hiroshima Code of Conduct, the Biden-Harris Executive Order on AI, and the UK AI Safety Summit. We will reach consensus on guiding principles that foster innovation and the development of responsible AI as well as make significant investments to build the knowledge and infrastructure necessary to measure, evaluate, and verify advanced AI systems, including through the launch of the U.S. AI Safety Institute. We will advance global norms on the responsible and rights-respecting use of AI-enabled technologies. Second, the United States allies, and partners, along with the private sector, will develop common understandings and shared principles for security and trustworthiness in subsea cable, cloud services, and data centers and will increase support for extending access to cloud services to emerging economies. Third, the United States, allies, and partners will succeed in pushing forward more action-oriented discussions at the UN on international security issues in cyberspace. These discussions will focus on how member states can work together to implement critical elements of the framework for responsible state behavior and on building all states’ capacity to manage cyber-related threats. Fourth, the Department of State will draw on the Cyberspace, Digital Connectivity, and Related Technologies Fund to provide rapid incident response and cyber aid quickly and effectively, as well as longer-term capacity and resilience building. These strategic investments will not only strengthen the role of the United States as a digital partner, but also generate larger, self-sustaining investments by host countries in their own cybersecurity and digital transformation. oving forward, the United States will strive for a future in which cyberspace and digital technologies are used to advance economic prosperity and inclusion, enhance security, promote and protect human rights and democracy, and address transnational challenges. The Department of State will build and extend digital solidarity to partners across the globe. The United States recognizes the need to work together to align approaches to data and digital governance and to promote the research, development, and deployment of critical and emerging technologies. The United States seeks to be the partner of choice in improving cybersecurity, building resilience, responding to, and recovering from malicious cyber activity. Digital solidarity aims to connect people and information like never before, fostering a more inclusive, secure, prosperous, rights-respecting, safe, and equitable world. Notes [1] The idea of digital solidarity was first promoted by Pablo Chavez, “Toward Digital Solidarity,” Lawfare, June 28, 2022, https://www.lawfaremedia.org/article/toward-digital-solidarity [back to 1] [2] Fast Track Action Subcommittee on Critical and Emerging Technologies, Critical and Emerging Technologies Update, National Science and Technology Council, February 2024, https://www.whitehouse.gov/wp-content/uploads/2024/02/Critical-and-Emerging-Technologies-List-2024-Update.pdf [back to 2] [3] Anna Fleck, “Cybercrime Expected To Skyrocket in Coming Years,” Statista, February 22,2024, https://www.statista.com/chart/28878/expected-cost-of-cybercrime-until-2027/ [back to 3] [4] ITU, The Gender Digital Divide,  https://www.itu.int/itu-d/reports/statistics/2023/10/10/ff23-the-gender-digital-divide/ [back to 4] [5] UN, Secretary General, Group of Governmental Experts on Developments in the Field of Information and Telecommunications in the Context of International Security, July 2015, https://digitallibrary.un.org/record/799853?ln=en&v=pdf [back to 5] This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 38 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 38 of 38. here here Conclusion As the NSS and NCS note, the 2020s are a decisive decade, and actions taken now will shape the contours of cyberspace, digital technologies, and the digital economy for the future. As it implements this strategy, the Department of State will work with Congress and interagency partners to evaluate current cyber authorities and to amend or create authorities as needed for the Department to keep pace with evolving cyber and digital technologies. Building innovative, secure, and rights-respecting digital ecosystems is a process that will extend beyond the timespan of this strategy, and likely to be characterized by progress, pauses, and reversals. There will be, however, some early signposts that will indicate the United States, allies, and partners are moving forward. First , the United States, allies, and partners, along with the private sector and civil society, will build on the early successes of the G7-Hiroshima Code of Conduct, the Biden-Harris Executive Order on AI, and the UK AI Safety Summit. We will reach consensus on guiding principles that foster innovation and the development of responsible AI as well as make significant investments to build the knowledge and infrastructure necessary to measure, evaluate, and verify advanced AI systems, including through the launch of the U.S. AI Safety Institute. We will advance global norms on the responsible and rights-respecting use of AI-enabled technologies. First Second , the United States allies, and partners, along with the private sector, will develop common understandings and shared principles for security and trustworthiness in subsea cable, cloud services, and data centers and will increase support for extending access to cloud services to emerging economies. Second Third , the United States, allies, and partners will succeed in pushing forward more action-oriented discussions at the UN on international security issues in cyberspace. These discussions will focus on how member states can work together to implement critical elements of the framework for responsible state behavior and on building all states’ capacity to manage cyber-related threats. Third Fourth , the Department of State will draw on the Cyberspace, Digital Connectivity, and Related Technologies Fund to provide rapid incident response and cyber aid quickly and effectively, as well as longer-term capacity and resilience building. These strategic investments will not only strengthen the role of the United States as a digital partner, but also generate larger, self-sustaining investments by host countries in their own cybersecurity and digital transformation. Fourth oving forward, the United States will strive for a future in which cyberspace and digital technologies are used to advance economic prosperity and inclusion, enhance security, promote and protect human rights and democracy, and address transnational challenges. The Department of State will build and extend digital solidarity to partners across the globe. The United States recognizes the need to work together to align approaches to data and digital governance and to promote the research, development, and deployment of critical and emerging technologies. The United States seeks to be the partner of choice in improving cybersecurity, building resilience, responding to, and recovering from malicious cyber activity. Digital solidarity aims to connect people and information like never before, fostering a more inclusive, secure, prosperous, rights-respecting, safe, and equitable world. Notes [1] The idea of digital solidarity was first promoted by Pablo Chavez, “Toward Digital Solidarity,” Lawfare, June 28, 2022, https://www.lawfaremedia.org/article/toward-digital-solidarity [back to 1] [1] https://www.lawfaremedia.org/article/toward-digital-solidarity https://www.lawfaremedia.org/article/toward-digital-solidarity [back to 1] [back to 1] [2] Fast Track Action Subcommittee on Critical and Emerging Technologies, Critical and Emerging Technologies Update, National Science and Technology Council, February 2024, https://www.whitehouse.gov/wp-content/uploads/2024/02/Critical-and-Emerging-Technologies-List-2024-Update.pdf [back to 2] [2] https://www.whitehouse.gov/wp-content/uploads/2024/02/Critical-and-Emerging-Technologies-List-2024-Update.pdf https://www.whitehouse.gov/wp-content/uploads/2024/02/Critical-and-Emerging-Technologies-List-2024-Update.pdf [back to 2] [back to 2] [3] Anna Fleck, “Cybercrime Expected To Skyrocket in Coming Years,” Statista, February 22,2024, https://www.statista.com/chart/28878/expected-cost-of-cybercrime-until-2027/ [back to 3] [3] https://www.statista.com/chart/28878/expected-cost-of-cybercrime-until-2027/ https://www.statista.com/chart/28878/expected-cost-of-cybercrime-until-2027/ [back to 3] [back to 3] [4] ITU, The Gender Digital Divide, https://www.itu.int/itu-d/reports/statistics/2023/10/10/ff23-the-gender-digital-divide/ [back to 4] [4] https://www.itu.int/itu-d/reports/statistics/2023/10/10/ff23-the-gender-digital-divide/ https://www.itu.int/itu-d/reports/statistics/2023/10/10/ff23-the-gender-digital-divide/ [back to 4] [back to 4] [5] UN, Secretary General, Group of Governmental Experts on Developments in the Field of Information and Telecommunications in the Context of International Security , July 2015, https://digitallibrary.un.org/record/799853?ln=en&v=pdf [back to 5] [5] Group of Governmental Experts on Developments in the Field of Information and Telecommunications in the Context of International Security https://digitallibrary.un.org/record/799853?ln=en&v=pdf https://digitallibrary.un.org/record/799853?ln=en&v=pdf [back to 5] [back to 5] This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State by the U.S Department of State

Towards Responsible Innovation: US Initiatives in AI Safety and Cybersecurity

When I first heard about Nervos Network's dedication to building a BTC Layer 2, I wasn't surprised. That's because the CKB public blockchain technology not only matches but also goes beyond BTC. It not only maintains BTC's native UTXO features but also allows for more complex programmable extensions. While surpassing BTC may seem challenging for CKB public chain, aiming to be a top-notch BTC Layer 2 seems like a smart choice. Why? Let's delve into my take on CKB. The current BTC Layer 2 market is constrained by the mainnet’s limited validation capabilities, resulting in diverse alternative solutions. The simplicity of BTC's scripting language, combined with its near-zero computation and validation power, has created significant opportunities for market innovation. As of now, aside from limited transaction validation and multisig functionalities within UTXO unlocking conditions, the BTC mainnet is incapable of directly handling more complex transaction logic involving data validation, state changes, etc. It relegates BTC to an asset settlement layer, relying on extending through a powerful public chain for local consensus and computational validation capabilities. This leads to a lack of uniform standards or 'orthodoxy' for BTC Layer 2 solutions, making it challenging to rank them. However, distinctions can be made based on the community's perception between narrow and broad interpretations: Narrowly defined, only solutions like the Lightning Network state channels and the one-time seal approach of RGB qualify as truly 'orthodox' BTC Layer 2s. They effectively utilize BTC's limited script validation capacity without relying on or minimally depending on external local consensus. In a more general sense, any extension chain that gains acceptance for its local consensus, along with a cross-chain bridge solution that ensures the safe migration of assets, can theoretically serve as a BTC Layer 2. This includes contemporary Ethereum EVM chains, Solana with its high-concurrency capabilities, and others. Clearly, the BTC Layer 2 market is divided: on one end are highly-specific solutions like the slowly evolving Lightning Network and RGB, facing substantial challenges; on the other end are broadly defined solutions, where any performance chain capable of securely interacting with the BTC mainnet qualifies as a BTC Layer 2. But is there a 'middle-of-the-road' option? Yes, and it lies in the Nervos Network, which adheres to the UTXO model at its core, enhancing its performance. Notable features include: The CKB Network aligns closely with BTC, sharing the same 'UTXO model and mining consensus mechanism,' unlike the account balance model of mainstream public blockchains like Ethereum. The UTXO model has distinct advantages in transaction privacy, flexible transaction structuring, and parallel processing capabilities to prevent double-spending, which may be one of Satoshi Nakamoto’s most brilliant inventions. This is why post-Ethereum projects like Sui and Aptos have also adopted similar UTXO models. Bitcoin's capacity and block speed may be constrained by its era, but the UTXO model is remarkably forward-thinking. CKB adopts this UTXO model, evolving it into the 'Cell model', which preserves the pure transactional nature of Bitcoin’s UTXO model while also supporting the data states found in account models like Ethereum’s. To simplify: In the Bitcoin UTXO model, the creation and destruction of coins resemble a continuous minting and melting process. The Cell model, however, omits the destruction aspect, focusing on verifying and persistently storing states. Each Cell includes Capacity and Data: Capacity measures the balance in bytes, similar to UTXO, while Data holds any kind of information, including the history of transaction states. Thus, the Cell model can accurately represent balances and manage asset transfers, and also handle a variety of complex smart contract states. In summary, the Cell model is a more persistent and flexible transaction model that greatly broadens the scope of the UTXO model. It is crucial for CKB’s ability to maintain the security of the BTC mainnet while offering a 'speed boost' to slower Bitcoin expansion initiatives like the Lightning Network and RGB. For instance, the recent rollout of RGB++ by CKB illustrates this. In the BTC ecosystem, developing a mature RGB solution involves challenges that are not so much about the one-time sealing process of the BTC mainnet, but rather the communication, coordination, and mutual state maintenance among off-chain client validation nodes, particularly in a decentralized setting. In simpler terms, RGB's theory might seem straightforward, but its practical implementation is hindered by foundational infrastructural limitations and various barriers.
Recognizing this, CKB integrates these off-chain client validation nodes into its on-chain public validation process. This approach significantly accelerates the intended UTXO client extension pathway that RGB aims to achieve. Complex P2P consensus among off-chain nodes is notoriously challenging, fraught with complexity and obstacles such as potential data synchronization delays or inconsistencies, and susceptibility to fraud and attacks. Transposing this process onto the blockchain can simplify these issues. With the increasing discussions around RGB++, let’s also look at CKB's Open Transaction data format, showcasing the chain's forward-thinking features. Open Transaction allows multiple participants to collaboratively build and aggregate different transactions over time. It supports partial construction, amendability, and incremental build-up and aggregation. For example, Alice initiates an Open Transaction to exchange a certain amount of Token A for Token B with Bob. The transaction, once started, remains in an editable state. Bob, upon agreeing to the transaction terms, can then add his Token B and finalize the conditions. It might initially seem abstract. Take cross-chain scenarios as an example: Alice and Bob could independently execute asset trades on various distinct chains, significantly boosting the CKB chain's cross-chain interaction capabilities. In the realm of complex DeFi transactions, where market-driven dynamic adjustments are often necessary, Open Transaction allows contract participants to adapt trading conditions fluidly during the contract's execution. This undoubtedly enhances the ability to manage transactional complexities. From my perspective, Open Transaction mirrors the UTXO transaction unlocking conditions, capable of amalgamating intricate unlocking conditions, multi-party signatures, and complex transactional environments. This represents an evolutionary and valuable innovation that builds on the foundational principles of the BTC main chain. Interestingly, Jan Xie, a core developer from the Ethereum team, chose to embrace the BTC UTXO model for his inaugural major project. Despite the broader application of Ethereum's smart contract model, Jan and his Nervos team decisively chose to expand and refine the BTC UTXO model. This choice reflects a deep respect for Satoshi Nakamoto’s simplistic UTXO transaction model and also subtly lays the groundwork for its transformation into a native BTC Layer 2. In conclusion, I am quite bullish about CKB’s potential as a BTC Layer 2. In the short term, it certainly has the potential to expedite the implementation of projects like the Lightning Network and RGB within UTXO-based chains, offering valuable insights for these 'orthodox' expansion solutions on the BTC mainnet. Looking long-term, the inherent features of CKB's chain and its innovative architectural compatibility may enable it to excel in the complex and standardless arena of BTC Layer 2s. Note: There’s much more to explore regarding the technical nuances and standout features of CKB, which I plan to analyze in greater depth later. It's fascinating to see how BTC Layer 2 not only provides a platform for new chains to rise but also opens up endless possibilities for rejuvenation within established chains. Author: Haotian, Independent Researcher This article is the translation of a Haotian’s tweet. When I first heard about Nervos Network's dedication to building a BTC Layer 2, I wasn't surprised. That's because the CKB public blockchain technology not only matches but also goes beyond BTC . It not only maintains BTC's native UTXO features but also allows for more complex programmable extensions. While surpassing BTC may seem challenging for CKB public chain, aiming to be a top-notch BTC Layer 2 seems like a smart choice. Why? Let's delve into my take on CKB. BTC The current BTC Layer 2 market is constrained by the mainnet’s limited validation capabilities, resulting in diverse alternative solutions. The simplicity of BTC's scripting language, combined with its near-zero computation and validation power, has created significant opportunities for market innovation. As of now, aside from limited transaction validation and multisig functionalities within UTXO unlocking conditions, the BTC mainnet is incapable of directly handling more complex transaction logic involving data validation, state changes, etc. It relegates BTC to an asset settlement layer, relying on extending through a powerful public chain for local consensus and computational validation capabilities. This leads to a lack of uniform standards or 'orthodoxy' for BTC Layer 2 solutions, making it challenging to rank them. However, distinctions can be made based on the community's perception between narrow and broad interpretations: However, distinctions can be made based on the community's perception between narrow and broad interpretations: Narrowly defined, only solutions like the Lightning Network state channels and the one-time seal approach of RGB qualify as truly 'orthodox' BTC Layer 2s. They effectively utilize BTC's limited script validation capacity without relying on or minimally depending on external local consensus. In a more general sense, any extension chain that gains acceptance for its local consensus, along with a cross-chain bridge solution that ensures the safe migration of assets, can theoretically serve as a BTC Layer 2. This includes contemporary Ethereum EVM chains, Solana with its high-concurrency capabilities, and others. Clearly, the BTC Layer 2 market is divided: on one end are highly-specific solutions like the slowly evolving Lightning Network and RGB, facing substantial challenges; on the other end are broadly defined solutions, where any performance chain capable of securely interacting with the BTC mainnet qualifies as a BTC Layer 2. Clearly, the BTC Layer 2 market is divided: But is there a 'middle-of-the-road' option? Yes, and it lies in the Nervos Network, which adheres to the UTXO model at its core, enhancing its performance. Notable features include: The CKB Network aligns closely with BTC, sharing the same 'UTXO model and mining consensus mechanism,' unlike the account balance model of mainstream public blockchains like Ethereum. The UTXO model has distinct advantages in transaction privacy, flexible transaction structuring, and parallel processing capabilities to prevent double-spending, which may be one of Satoshi Nakamoto’s most brilliant inventions. This is why post-Ethereum projects like Sui and Aptos have also adopted similar UTXO models. Bitcoin's capacity and block speed may be constrained by its era, but the UTXO model is remarkably forward-thinking. CKB adopts this UTXO model, evolving it into the 'Cell model', which preserves the pure transactional nature of Bitcoin’s UTXO model while also supporting the data states found in account models like Ethereum’s. To simplify : In the Bitcoin UTXO model, the creation and destruction of coins resemble a continuous minting and melting process. The Cell model, however, omits the destruction aspect, focusing on verifying and persistently storing states. Each Cell includes Capacity and Data: Capacity measures the balance in bytes, similar to UTXO, while Data holds any kind of information, including the history of transaction states. Thus, the Cell model can accurately represent balances and manage asset transfers, and also handle a variety of complex smart contract states. To simplify In summary, the Cell model is a more persistent and flexible transaction model that greatly broadens the scope of the UTXO model. It is crucial for CKB’s ability to maintain the security of the BTC mainnet while offering a 'speed boost' to slower Bitcoin expansion initiatives like the Lightning Network and RGB. For instance, the recent rollout of RGB++ by CKB illustrates this. In the BTC ecosystem, developing a mature RGB solution involves challenges that are not so much about the one-time sealing process of the BTC mainnet, but rather the communication, coordination, and mutual state maintenance among off-chain client validation nodes, particularly in a decentralized setting. In simpler terms, RGB's theory might seem straightforward, but its practical implementation is hindered by foundational infrastructural limitations and various barriers.
Recognizing this, CKB integrates these off-chain client validation nodes into its on-chain public validation process. This approach significantly accelerates the intended UTXO client extension pathway that RGB aims to achieve. Complex P2P consensus among off-chain nodes is notoriously challenging, fraught with complexity and obstacles such as potential data synchronization delays or inconsistencies, and susceptibility to fraud and attacks. Transposing this process onto the blockchain can simplify these issues. With the increasing discussions around RGB++, let’s also look at CKB's Open Transaction data format, showcasing the chain's forward-thinking features. Open Transaction allows multiple participants to collaboratively build and aggregate different transactions over time. It supports partial construction, amendability, and incremental build-up and aggregation. For example, Alice initiates an Open Transaction to exchange a certain amount of Token A for Token B with Bob. The transaction, once started, remains in an editable state. Bob, upon agreeing to the transaction terms, can then add his Token B and finalize the conditions. It might initially seem abstract. Take cross-chain scenarios as an example: Alice and Bob could independently execute asset trades on various distinct chains, significantly boosting the CKB chain's cross-chain interaction capabilities. In the realm of complex DeFi transactions, where market-driven dynamic adjustments are often necessary, Open Transaction allows contract participants to adapt trading conditions fluidly during the contract's execution. This undoubtedly enhances the ability to manage transactional complexities. From my perspective, Open Transaction mirrors the UTXO transaction unlocking conditions, capable of amalgamating intricate unlocking conditions, multi-party signatures, and complex transactional environments. This represents an evolutionary and valuable innovation that builds on the foundational principles of the BTC main chain. Interestingly, Jan Xie , a core developer from the Ethereum team, chose to embrace the BTC UTXO model for his inaugural major project. Despite the broader application of Ethereum's smart contract model, Jan and his Nervos team decisively chose to expand and refine the BTC UTXO model. This choice reflects a deep respect for Satoshi Nakamoto’s simplistic UTXO transaction model and also subtly lays the groundwork for its transformation into a native BTC Layer 2. Jan Xie Jan Xie In conclusion , I am quite bullish about CKB’s potential as a BTC Layer 2. In the short term, it certainly has the potential to expedite the implementation of projects like the Lightning Network and RGB within UTXO-based chains, offering valuable insights for these 'orthodox' expansion solutions on the BTC mainnet. Looking long-term, the inherent features of CKB's chain and its innovative architectural compatibility may enable it to excel in the complex and standardless arena of BTC Layer 2s. In conclusion Note: There’s much more to explore regarding the technical nuances and standout features of CKB, which I plan to analyze in greater depth later. It's fascinating to see how BTC Layer 2 not only provides a platform for new chains to rise but also opens up endless possibilities for rejuvenation within established chains. Author: Haotian, Independent Researcher Author: Haotian , Independent Researcher Author: Haotian Haotian This article is the translation of a Haotian’s tweet. This article is the translation of a Haotian’s tweet. This article is the translation of a Haotian’s tweet . tweet tweet

How CKB's Public Blockchain Tech Accelerates Bitcoin's Orthodox Layer 2  

কিভাবে CKB এর পাবলিক ব্লকচেইন টেক বিটকয়েনের অর্থোডক্স লেয়ার 2কে ত্বরান্বিত করে

Wie die öffentliche Blockchain-Technologie von CKB die orthodoxe Schicht 2 von Bitcoin beschleunigt

Cómo la tecnología blockchain pública de CKB acelera la capa 2 ortodoxa de Bitcoin

Comment la technologie de blockchain publique de CKB accélère la couche orthodoxe 2 de Bitcoin

सीकेबी की सार्वजनिक ब्लॉकचेन तकनीक बिटकॉइन की रूढ़िवादी परत 2 को कैसे गति देती है

CKBのパブリックブロックチェーン技術がビットコインのオーソドックスなレイヤー2を加速させる方法

CKB의 퍼블릭 블록체인 기술이 비트코인의 정통 레이어 2를 가속화하는 방법

Como a tecnologia Blockchain pública da CKB acelera a camada ortodoxa 2 do Bitcoin

Как публичная технология блокчейна CKB ускоряет ортодоксальный уровень Биткойна 2

CKB'nin Halka Açık Blockchain Teknolojisi Bitcoin'in Ortodoks Katman 2'sini Nasıl Hızlandırıyor?

Công nghệ Blockchain công cộng của CKB tăng tốc lớp 2 chính thống của Bitcoin như thế nào

CKB 的公共区块链技术如何加速比特币正统的第 2 层

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name Table of Links Abstract and Introduction
Formal Model - Rollups with Decentralized Common Pool (DCP)
Atomic composability & ZK-proofs
Incorporating Zk-proofs
Application of the Formal Model
Conclusion and References 6 Conclusion The formal model presented serves as a superset for all possible atomic composability across rollups because of its comprehensive nature. This model is a superset because it is designed to encompass every step, every entity, and every possible scenario in the lifecycle of a transaction across rollups. By being exhaustive in its approach, any atomic composability solution across rollups that exists or might be conceived in the future can be mapped onto some subset of this model. It acts as a general framework or blueprint from which specific implementations can be derived, tailored to particular requirements or constraints. Comprehensiveness of Definitions It identifies every rollup, transaction, timestamp, cryptographic key, buffer mechanism, and even the decentralized pool where transactions are posted. This covers all essential tools and entities required for atomic transactions between rollups. Detailed Operations The detailed operation steps ensure that every conceivable action associated with transactions – from their creation, publication, verification, buffering, to dependency resolution – is incorporated: • Publish: Every transaction is committed to a decentralized common pool, capturing the universal broadcast mechanism. • Buffer: Handles the uncertainties and delays. • Resolve: Manages interdependencies, making sure that if one transaction in a set can’t be executed, none in that set are. • Verify: Ensures that only legitimate and valid transactions are processed. Robust Dependency Handling This is perhaps the crux of atomic composability. The model provides: • A method to check the timing (timestamps) of transactions, which is crucial for ensuring order and dependencies. • Buffering mechanisms to account for delays in dependency resolution. • Defined limits for buffering to prevent infinite waits and to give an outcome (acceptance or rejection) within a finite time. Flexibility & Scalability The model doesn’t restrict the number or type of dependencies. It simply provides mechanisms to handle them. This ensures that as blockchain technology evolves and new dependency types emerge or transactions become more intricate, the model remains applicable. Incorporation of Limits By integrating system limits like τmax, Bmax, and Dmax, the model not only accounts for ideal scenarios where all dependencies are quickly resolved but also for edge cases where system constraints come into play. This adds to its universality. Security through Verification The model’s inclusion of cryptographic verification ensures that security concerns are front and center. It recognizes that composability isn’t just about making sure transactions work together, but that they’re also genuine and untampered. References [1] E. Ben-Sasson, A. Chiesa, D. Genkin, E. Tromer, and M. Virza. Snarks for c: Verifying program executions succinctly and in zero knowledge. In Proceedings of the 33rd Annual Cryptology Conference, pages 90–108. Springer, 2013. [2] E. Ben-Sasson, I. Bentov, Y. Horesh, and M. Riabzev. Scalable, transparent, and post-quantum secure computational integrity. IACR Cryptol. ePrint Arch., 2018:46, 2018. [3] N. Bitansky, R. Canetti, A. Chiesa, and E. Tromer. Extractable collision resistance: A new complexity assumption for zero-knowledge. Advances in Cryptology–CRYPTO 2012, pages 416–434, 2012. [4] V. Buterin. A next-generation smart contract and decentralized application platform. White paper, 2014. Available at https://ethereum.org/en/whitepaper/. [5] V. Buterin. Ethereum’s scalability and decentralization challenge, 2020. [6] R. Gennaro, C. Gentry, B. Parno, and M. Raykova. Quadratic span programs and succinct nizks without pcps. In Proceedings of the 32nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, pages 626–645. Springer, 2013. [7] S. Micali. Cs proofs. Journal of Cryptology, 4(3):175–194, 1994. [8] S. Micali. Atomic transactions in blockchain, 2016. [9] A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder. Bitcoin and Cryptocurrency Technologies. Princeton University Press, 2016. [10] C. Reitwiessner. zk-snarks in a nutshell. Blog post, 2016. Available at https://blog.ethereum.org/2016/12/05/zksnarks-in-a-nutshell/. [11] F. Schär. Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 2020. [12] R. S. Wahby, I. Tzialla, A. Shelat, J. Thaler, and M. Walfish. Fast and simple constant-size zk-snarks without trusted setup. IACR Cryptol. ePrint Arch., 2019:748, 2019. [13] H. Williamson. Optimistic rollups: Layer 2 scaling. Blog post, 2020. Available at https://optimism.io/. This paper is available on arxiv under CC 4.0 license. Authors: (1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name Table of Links Abstract and Introduction Formal Model - Rollups with Decentralized Common Pool (DCP) Atomic composability & ZK-proofs Incorporating Zk-proofs Application of the Formal Model Conclusion and References Abstract and Introduction Abstract and Introduction Formal Model - Rollups with Decentralized Common Pool (DCP) Formal Model - Rollups with Decentralized Common Pool (DCP) Atomic composability & ZK-proofs Atomic composability & ZK-proofs Incorporating Zk-proofs Incorporating Zk-proofs Application of the Formal Model Application of the Formal Model Conclusion and References Conclusion and References 6 Conclusion The formal model presented serves as a superset for all possible atomic composability across rollups because of its comprehensive nature. This model is a superset because it is designed to encompass every step, every entity, and every possible scenario in the lifecycle of a transaction across rollups. By being exhaustive in its approach, any atomic composability solution across rollups that exists or might be conceived in the future can be mapped onto some subset of this model. It acts as a general framework or blueprint from which specific implementations can be derived, tailored to particular requirements or constraints. Comprehensiveness of Definitions It identifies every rollup, transaction, timestamp, cryptographic key, buffer mechanism, and even the decentralized pool where transactions are posted. This covers all essential tools and entities required for atomic transactions between rollups. Comprehensiveness of Definitions Detailed Operations The detailed operation steps ensure that every conceivable action associated with transactions – from their creation, publication, verification, buffering, to dependency resolution – is incorporated: Detailed Operations • Publish: Every transaction is committed to a decentralized common pool, capturing the universal broadcast mechanism. • Buffer: Handles the uncertainties and delays. • Resolve: Manages interdependencies, making sure that if one transaction in a set can’t be executed, none in that set are. • Verify: Ensures that only legitimate and valid transactions are processed. Robust Dependency Handling This is perhaps the crux of atomic composability. The model provides: • A method to check the timing (timestamps) of transactions, which is crucial for ensuring order and dependencies. • Buffering mechanisms to account for delays in dependency resolution. • Defined limits for buffering to prevent infinite waits and to give an outcome (acceptance or rejection) within a finite time. Flexibility & Scalability The model doesn’t restrict the number or type of dependencies. It simply provides mechanisms to handle them. This ensures that as blockchain technology evolves and new dependency types emerge or transactions become more intricate, the model remains applicable. Incorporation of Limits By integrating system limits like τmax, Bmax, and Dmax, the model not only accounts for ideal scenarios where all dependencies are quickly resolved but also for edge cases where system constraints come into play. This adds to its universality. Security through Verification The model’s inclusion of cryptographic verification ensures that security concerns are front and center. It recognizes that composability isn’t just about making sure transactions work together, but that they’re also genuine and untampered. References [1] E. Ben-Sasson, A. Chiesa, D. Genkin, E. Tromer, and M. Virza. Snarks for c: Verifying program executions succinctly and in zero knowledge. In Proceedings of the 33rd Annual Cryptology Conference, pages 90–108. Springer, 2013. [2] E. Ben-Sasson, I. Bentov, Y. Horesh, and M. Riabzev. Scalable, transparent, and post-quantum secure computational integrity. IACR Cryptol. ePrint Arch., 2018:46, 2018. [3] N. Bitansky, R. Canetti, A. Chiesa, and E. Tromer. Extractable collision resistance: A new complexity assumption for zero-knowledge. Advances in Cryptology–CRYPTO 2012, pages 416–434, 2012. [4] V. Buterin. A next-generation smart contract and decentralized application platform. White paper, 2014. Available at https://ethereum.org/en/whitepaper/. [5] V. Buterin. Ethereum’s scalability and decentralization challenge, 2020. [6] R. Gennaro, C. Gentry, B. Parno, and M. Raykova. Quadratic span programs and succinct nizks without pcps. In Proceedings of the 32nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, pages 626–645. Springer, 2013. [7] S. Micali. Cs proofs. Journal of Cryptology, 4(3):175–194, 1994. [8] S. Micali. Atomic transactions in blockchain, 2016. [9] A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder. Bitcoin and Cryptocurrency Technologies. Princeton University Press, 2016. [10] C. Reitwiessner. zk-snarks in a nutshell. Blog post, 2016. Available at https://blog.ethereum.org/2016/12/05/zksnarks-in-a-nutshell/. [11] F. Schär. Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 2020. [12] R. S. Wahby, I. Tzialla, A. Shelat, J. Thaler, and M. Walfish. Fast and simple constant-size zk-snarks without trusted setup. IACR Cryptol. ePrint Arch., 2019:748, 2019. [13] H. Williamson. Optimistic rollups: Layer 2 scaling. Blog post, 2020. Available at https://optimism.io/.

Towards Universal Atomic Composability: Conclusion and References

Happiness Is Based on a Flawed Equation — Here's How to Fix It

সুখ একটি ত্রুটিপূর্ণ সমীকরণের উপর ভিত্তি করে - এটি কীভাবে ঠিক করা যায় তা এখানে

Glück basiert auf einer fehlerhaften Gleichung – so lässt sie sich beheben

La felicidad se basa en una ecuación defectuosa: aquí se explica cómo solucionarla

Le bonheur est basé sur une équation erronée : voici comment y remédier

खुशी एक दोषपूर्ण समीकरण पर आधारित है - इसे ठीक करने का तरीका यहां बताया गया है

幸福は間違った方程式に基づいている — これを修正する方法はこれだ

행복은 잘못된 방정식에 기초합니다. 문제를 해결하는 방법은 다음과 같습니다.

A felicidade é baseada em uma equação falha – veja como consertar

Счастье основано на ошибочном уравнении — вот как это исправить

Mutluluk Kusurlu Bir Denkleme Dayalı - İşte Nasıl Düzeltileceği

Hạnh phúc dựa trên một phương trình sai lầm - Đây là cách khắc phục

幸福建立在一个有缺陷的等式上——下面是如何修复它

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If you’re struggling to create an account at HackerNoon, read this blog post or visit our help page. Click on Start Writing to the left of your thumbnail (or go directly there). Write or upload a great tech story - detailed instructions for editor 3.0 here. Click Story Settings in the upper right-hand corner. Enter 8 tags (guideline). Upload a feature image (guideline). Enter your meta tag so search engines can easily find you (guideline). Confirm that you have the rights to submit the content and that all vested interests of companies mentioned in the story have been disclosed. Select Submit Story for Review. You will then receive an email to confirm that we have received your submission. A Staff Editor should review within 3 business days..ish. See full guidelines here. If accepted, we’ll distribute, distribute, and distribute your tech story. See how we distribute things here. Log in to Hacker Noon via Hackernoon.com/login . If you’re struggling to create an account at HackerNoon, read this blog post or visit our help page . Hackernoon.com/login read this blog post help page Click on Start Writing to the left of your thumbnail (or go directly there ). there Write or upload a great tech story - detailed instructions for editor 3.0 here . upload a great tech story here Click Story Settings in the upper right-hand corner. Enter 8 tags ( guideline ). guideline Upload a feature image ( guideline ). guideline Enter your meta tag so search engines can easily find you ( guideline ). guideline Confirm that you have the rights to submit the content and that all vested interests of companies mentioned in the story have been disclosed. Select Submit Story for Review. You will then receive an email to confirm that we have received your submission. A Staff Editor should review within 3 business days..ish. See full guidelines here . here If accepted, we’ll distribute, distribute, and distribute your tech story. See how we distribute things here . here https://www.youtube.com/watch?v=O5p0nUvIZ6A&embedable=true https://www.youtube.com/watch?v=O5p0nUvIZ6A&embedable=true Writers meet the draft page! Writers meet the draft page! Let's set the scene: you've been an avid reader of HackerNoon for some time now and finally decided it's time to share your own content here. So you hit the "Start Writing" button sitting at the top right corner of your screen. Boom! It's writing time - and here's what you'll find on our editor 3.0 : editor 3.0 If you’re looking to import a story, learn more here, and consult our republishing guidelines here. If you’re looking to import a story, learn more here , and consult our republishing guidelines here . learn more here here 1. Titles Titles When crafting your new story in our editor, you've got two choices for titles: you can either come up with your own or let our system generate suggestions for you. To enter your own title, simply type it into the designated space and use our auto-capitalization feature to ensure proper formatting—click the robot icon to toggle this feature on or off. If you're feeling stuck, click the lightbulb icon for a suggestion. Just make sure your text body contains enough content for our system to detect the story's theme, and ensure the title space is empty before clicking " generate headline ". generate headline Once you've added your title, your draft will be automatically saved, so remember to include some text. Remember, your title should accurately reflect the essence of your story and include relevant keywords to help readers discover it. Learn more here. Remember, your title should accurately reflect the essence of your story and include relevant keywords to help readers discover it. Learn more here . here 2. Formatting Features In our editor, you've got all the tools at your fingertips to format your text just how you want it: bold, overline, underline, lists, links, headlines , quotes , code , and more. To do so select your text and choose your desired options. format your text headlines quotes code Alternatively, you can click the + button on the left of your text line and pick an option from the list, which contains some options seen above and others like tables, images, links, dividers, info notices, and more. Here’s how it looks: 3. Embeds There are three main things you can embed in your story: images, videos , and links. videos Starting with images, you've got a range of choices: import from your device, import an image from a URL, grab one from an image bank, embed a GIF , or generate an AI image . Simply choose your preferred option, and voila! Your image will appear in your draft. embed a GIF generate an AI image When it comes to videos, you've can import them as you would images or embed YouTube links . Click the + button , navigate to the link option, paste your URL, and hit enter. Then, you'll encounter a prompt asking if you want to embed the link. For YouTube videos, you'll have the choice to add just the video, the transcription, or both - it will look something like this: embed YouTube links + button Lastly, links: it’s very similar to embedding YouTube videos, except you can link stories, code, and other external sources . Here’s how: link stories, code, and other external sources Embedding also extends to Twitter/X threads. Embedding also extends to Twitter/X threads . Twitter/X threads Onto The Story Settings Now that you have the gist of what you can do with your copy on our editor, here’s the rundown on the story settings fields you’ll need to fill out before submitting the story for editorial review: Add all of your story’s 8 tags: Tags help Google, Bing, DuckDuckGo, and friends find your content, to serve it to users searching for those terms. Plus, tags help HackerNoon distribute your content to top trafficked pages, as well as the most subscribed tags via our customizable newsletter, The Tech Brief. Add all of your story’s 8 tags: Tags help Google, Bing, DuckDuckGo, and friends find your content, to serve it to users searching for those terms. Plus, tags help HackerNoon distribute your content to top trafficked pages, as well as the most subscribed tags via our customizable newsletter, The Tech Brief. Add all of your story’s 8 tags: Tags help Google , Bing , DuckDuckGo , and friends find your content, to serve it to users searching for those terms. Plus, tags help HackerNoon distribute your content to top trafficked pages, as well as the most subscribed tags via our customizable newsletter, The Tech Brief. Add all of your story’s 8 tags : Add all of your story’s 8 tags Google Bing DuckDuckGo 2. Open Story Settings : At the top of your story settings you’ll see the number of words written - check this before submitting. One of our editorial guidelines stipulates a minimum word count for publication eligibility. Open Story Settings editorial guidelines Upload a featured image: Choose one from your device, explore image banks, and GIFs, or ask AI to create a new one. Upload a featured image: Choose one from your device, explore image banks, and GIFs, or ask AI to create a new one. Upload a featured image: Customize how you want comments managed for your story: Decide whether you want them to undergo editorial review or be approved instantly. Add your metadescription: Help web crawlers to index and humans to read Craft a TL:DR: type between 300 and 500 characters or have our AI generate a TL:DR for you Customize how you want comments managed for your story: Decide whether you want them to undergo editorial review or be approved instantly. Customize how you want comments managed for your story: Customize how you want comments managed for your story: Add your metadescription: Help web crawlers to index and humans to read Add your metadescription : Add your metadescription Craft a TL:DR: type between 300 and 500 characters or have our AI generate a TL:DR for you Craft a TL:DR: Pick Emoji Credibility Indicators: These indicators help you provide additional information relating to your story, giving readers more context. Is your story original? If it's a republication, hit "No" and drop the URL where it first went live; if it's fresh content, just tap "Yes". When in doubt, consult our republishing guidelines here. Pick Emoji Credibility Indicators: These indicators help you provide additional information relating to your story, giving readers more context. Pick Emoji Credibility Indicators : Pick Emoji Credibility Indicators Is your story original? If it's a republication, hit "No" and drop the URL where it first went live; if it's fresh content, just tap "Yes". When in doubt, consult our republishing guidelines here . Is your story original? here Once you've wrapped up all these steps, keep an eye out for a button that pops up at the bottom prompting you to " Submit Your Story for Review! " If you're feeling good about your story, just give it a click. Soon after, you'll get an email confirming that we've received your submission. A Staff Editor should review it within 3 business days..ish, and if accepted, we’ll distribute, distribute, and distribute your tech story. Submit Your Story for Review! NEW! On your draft, you’ll see the Submit Story button left in three different places: At the bottom of your story settings
At the top corner of your screen
At the bottom right corner of your screen Once you select the checkbox that matches your story's originality, these buttons will become active. NEW! On your draft, you’ll see the Submit Story button left in three different places: Submit Story At the bottom of your story settings At the top corner of your screen At the bottom right corner of your screen At the bottom of your story settings At the top corner of your screen At the bottom right corner of your screen Once you select the checkbox that matches your story's originality, these buttons will become active. Additional tools In the story settings, you'll find some extra goodies! You can whip up sticky notes and shoot a message to an editor for some writing support. Check out how it looks below: And there you have it! You're now equipped with the tools to transition from draft to publication - no reason not to hit that submit button and share your work with the world!

From Desk to HackerNoon: Your Ultimate Guide on How to Publish a Story

ডেস্ক থেকে হ্যাকারনুন পর্যন্ত: কীভাবে একটি গল্প প্রকাশ করবেন তার চূড়ান্ত গাইড

Vom Schreibtisch zum HackerNoon: Ihr ultimativer Leitfaden zum Veröffentlichen einer Story

Del escritorio a HackerNoon: su guía definitiva sobre cómo publicar una historia

Du bureau à HackerNoon : votre guide ultime sur la façon de publier une histoire

डेस्क से हैकरनून तक: कहानी प्रकाशित करने के बारे में आपकी अंतिम मार्गदर्शिका

デスクから HackerNoon へ: ストーリーを公開するための究極のガイド

데스크에서 HackerNoon까지: 스토리 게시 방법에 대한 최고의 가이드

From Desk to HackerNoon: seu guia definitivo sobre como publicar uma história

От стола до HackerNoon: ваше полное руководство о том, как опубликовать историю

Masadan HackerNoon'a: Bir Hikayenin Nasıl Yayınlanacağına İlişkin Temel Kılavuzunuz

Từ bàn làm việc đến HackerNoon: Hướng dẫn cơ bản về cách xuất bản một câu chuyện

从办公桌到 HackerNoon：如何发布故事的终极指南

**LONDON, United Kingdom, May 13th, 2024/Chainwire/--**Robert Kiyosaki, the renowned businessman and best-selling author of the educational series, ‘Rich Dad Poor Dad’, joins Andrew Maguire, on this week’s episode of Live from the Vault. This week, a precious metals commentary show hosted by the renowned London whistleblower takes viewers through the current economic landscape and explains how everyone can take steps to preserve their wealth. https://www.youtube.com/embed/DnS5hZGqQjA “I save gold and silver. I don’t save dollars.” Kiyosaki speaks on how everyday people are accustomed to spending and saving in centralised, bank-issued fiat currency, commonly referred to as ‘paper money’. “My mom and dad had no idea,” says Kiyosaki. The everyday person can’t necessarily see the difference between “real money and fake money”. He continues, “... they hung on to paper again, their college degrees... People have been conditioned and programmed to take paper instead of the real stuff, which violates Gresham's law.” Gresham's law states that when ‘bad money’ enters a system, people hold onto ‘good money’, which sees valuable assets like gold and silver, exit the system. Kiyosaki explains how paper assets, such as Dollars, Pounds or Yen are subject to large-scale currency devaluation due to central bank money printing. He contrasts saving in fiat and paper assets with the ownership of hard assets, such as real estate and precious metals. Kiyosaki comments, “I save gold and silver. I don’t save dollars”, an approach he has taken since his time as an army pilot in Vietnam, which coincided with Nixon’s closing of the gold window in 1971. BRICS Central Bank Gold Demand Discussing the BRICS nations, Kiyosaki highlights a growing division between the East and the West, with Eastern nations like Russia and China accumulating gold to create a currency backed by the precious metal. The Evolution of Money The conversation concludes with an evaluation of the Kinesis ecosystem; a sustainable and fair monetary alternative, that has already proven its potential in helping users globally counter the impact of a fiat-based economy. Kinesis’ gold and silver-based currencies, KAU and KAG are backed by fully allocated, fully audited gold and silver, which is made universally spendable by card, through leveraging blockchain technology. Kiyosaki voices his enthusiasm about Kinesis’ capacity to easily allocate a portfolio between gold and silver, spend on a debit card - and get paid every month through Kinesis’ innovative yields system. He calls Kinesis a game-changer, deeming it “the evolution of money”. This publication is for informational purposes only and is not intended to be a solicitation, offering or recommendation of any security, commodity, derivative, investment management service or advisory service and is not commodity trading advice. This publication does not intend to provide investment, tax or legal advice on either a general or specific basis. About Kinesis Money Kinesis is an end-to-end monetary system based 1:1 on physical gold and silver. Founded in 2017, Kinesis formed a strategic partnership with Allocated Bullion Exchange with institutional metals exchange trading globally for over a decade. With $10B traded since 2021, Kinesis has driven rapid expansion on a global scale, amassing a client base across 150+ countries. Through robust vaulting infrastructure and innovative financial technology, Kinesis reintroduces gold and silver as money. The platform enables citizens worldwide to protect their wealth outside of the volatility of the traditional banking system. Contact Zubair Bukhari Kinesis Money zubair.bukhari@kinesis.money This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **LONDON, United Kingdom, May 13th, 2024/Chainwire/--**Robert Kiyosaki, the renowned businessman and best-selling author of the educational series, ‘Rich Dad Poor Dad’, joins Andrew Maguire, on this week’s episode of Live from the Vault. This week, a precious metals commentary show hosted by the renowned London whistleblower takes viewers through the current economic landscape and explains how everyone can take steps to preserve their wealth. https://www.youtube.com/embed/DnS5hZGqQjA https://www.youtube.com/embed/DnS5hZGqQjA https://www.youtube.com/embed/DnS5hZGqQjA “I save gold and silver. I don’t save dollars.” Kiyosaki speaks on how everyday people are accustomed to spending and saving in centralised, bank-issued fiat currency, commonly referred to as ‘paper money’. “My mom and dad had no idea,” says Kiyosaki. The everyday person can’t necessarily see the difference between “real money and fake money”. He continues, “... they hung on to paper again, their college degrees... People have been conditioned and programmed to take paper instead of the real stuff, which violates Gresham's law.” Gresham's law states that when ‘bad money’ enters a system, people hold onto ‘good money’, which sees valuable assets like gold and silver, exit the system. Kiyosaki explains how paper assets, such as Dollars, Pounds or Yen are subject to large-scale currency devaluation due to central bank money printing. He contrasts saving in fiat and paper assets with the ownership of hard assets, such as real estate and precious metals. Kiyosaki comments, “I save gold and silver. I don’t save dollars”, an approach he has taken since his time as an army pilot in Vietnam, which coincided with Nixon’s closing of the gold window in 1971. BRICS Central Bank Gold Demand Discussing the BRICS nations, Kiyosaki highlights a growing division between the East and the West, with Eastern nations like Russia and China accumulating gold to create a currency backed by the precious metal. The Evolution of Money The conversation concludes with an evaluation of the Kinesis ecosystem; a sustainable and fair monetary alternative, that has already proven its potential in helping users globally counter the impact of a fiat-based economy. Kinesis’ gold and silver-based currencies, KAU and KAG are backed by fully allocated, fully audited gold and silver, which is made universally spendable by card, through leveraging blockchain technology. Kiyosaki voices his enthusiasm about Kinesis’ capacity to easily allocate a portfolio between gold and silver, spend on a debit card - and get paid every month through Kinesis’ innovative yields system. He calls Kinesis a game-changer, deeming it “the evolution of money”. This publication is for informational purposes only and is not intended to be a solicitation, offering or recommendation of any security, commodity, derivative, investment management service or advisory service and is not commodity trading advice. This publication does not intend to provide investment, tax or legal advice on either a general or specific basis. About Kinesis Money Kinesis Money Kinesis Money Kinesis is an end-to-end monetary system based 1:1 on physical gold and silver. Founded in 2017, Kinesis formed a strategic partnership with Allocated Bullion Exchange with institutional metals exchange trading globally for over a decade. With $10B traded since 2021, Kinesis has driven rapid expansion on a global scale, amassing a client base across 150+ countries. Through robust vaulting infrastructure and innovative financial technology, Kinesis reintroduces gold and silver as money. The platform enables citizens worldwide to protect their wealth outside of the volatility of the traditional banking system. Contact Zubair Bukhari Kinesis Money zubair.bukhari@kinesis.money This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Kiyosaki Sits Down With Kinesis To Discuss ‘The Evolution of Money’

Autofac ComponentRegistryBuilder in ASP.NET Core

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 4 of 38. Cyber Attacks and National Security Threats Adversarial cyber campaigns can cumulatively produce strategic loss for the United States and its allies, and they increasingly put the development goals of emerging economies at risk. Cyber threats continue to intensify in both frequency and severity, with increased risks of escalatory or uncontrolled cyber activity. State actor and non-state actors, including criminals, terrorists, and violent extremists, have tremendous incentives to invest in and exploit digital technologies to threaten our and other’s national interests. The People’s Republic of China (PRC) presents the broadest, most active, and most persistent cyber threat to government and private sector networks in the United States. Beijing has mounted cyber espionage operations against government, commercial, and civil society actors and has increased its ability to carry out destructive and disruptive cyberattacks. The PRC is capable of launching cyberattacks that could disrupt oil and gas pipelines, rail systems, and other critical infrastructure services within the United States or its allies and partners. Attempts to compromise critical infrastructure by PRC actors are designed in part to pre-position themselves to be able to disrupt or destroy critical infrastructure in the event of a conflict—either to either prevent the United States from being able to project power into Asia, or to affect our decision-making during a crisis by instigating societal chaos inside the United States. Both state-sponsored activity and that of PRC-linked actors are part of the PRC cyber approach. A persistent cyber threat, the Russian government is refining its cyber espionage, cyberattack, influence, and information manipulation capabilities to threaten other states and to weaken U.S. alliances and partnerships. Russia continues to provide safe haven to transnational cybercriminal actors, such as disruptive ransomware gangs. Russia’s cyberattacks in support of its 2022 unprovoked invasion of Ukraine were intended to destabilize the Ukrainian state and military and have resulted in spillover effects onto civilian critical infrastructure in other European countries. As the war continues, Russian government and Russian government-aligned cyber actors have targeted Ukraine with cyber operations against the public and private sectors, information manipulation and online influence operations, and attempts to divert and censor Ukrainians’ access to the Internet. Russia appears particularly focused on improving its ability to target critical infrastructure in the United States to demonstrate its ability to damage infrastructure during a crisis. The governments of the Democratic People’s Republic of North Korea (DPRK) and Iran have both increased the scale of their malicious cyber activities. Facing multiple rounds of international sanctions, the DPRK evades controls through cybercrime and the theft of cryptocurrencies. DPRK hackers continue to gather intelligence on military technology targets as well as academia and think tanks. In addition, the DPRK dispatches thousands of skilled IT workers around the world to generate fraudulent revenue that ultimately contributes to its weapons of mass destruction and ballistic missile programs despite U.S. and UN sanctions. Iran’s growing expertise and willingness to conduct cyber operations threaten the security of networks and data globally. Iran’s opportunistic approach to cyberattacks makes critical infrastructure owners in the United States susceptible to being targeted by Iranian actors, particularly when Tehran believes that it must demonstrate it can push back against the United States in other domains. Iranian actors have engaged in a wide range of intelligence-gathering operations around the world, and—in the wake of Hamas’ atrocities on October 7, 2023, and Israel’s military operations in Gaza—have conducted wiper, website takedown, hack and leak operations, espionage, and online information manipulations campaigns. Iranian actors have also conducted malicious activity against operational technology devices used in the water sector and other industries. Cyber criminals and criminal syndicates operating in cyberspace now represent a specific threat to the economic and national security of countries around the world. Cybercrime and online fraud cause significant harm to economic development, with small- to medium-sized enterprises and financial service providers especially at risk. According to one estimate, the global cost of cybercrime is estimated to top $23 trillion in 2027. [3] Ransomware incidents have disrupted critical functions, services, and businesses, from energy pipelines and food companies to schools and hospitals. Ransomware attacks against the healthcare industry can undercut the level of care provided to patients and others under care. Total economic losses from ransomware attacks worldwide continue to climb, reaching into the billions of U.S. dollars annually. Ransomware groups often operate out of safe haven jurisdictions whose governments, often adversaries like Russia, do not cooperate with law enforcement and sometimes encourage, direct, sanction, or tolerate their activities. Terrorists’ and violent extremists’ use of digital technologies also represents a threat to the national security of the United States and its allies and partners. Malign activities include the use of information and communications technologies (ICT) to spread violent propaganda; encourage radicalization and mobilization to commit violent acts; recruit individuals to terrorist organizations; to train, plan, and coordinate attacks; and finance terrorist acts. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 4 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 4 of 38. here Cyber Attacks and National Security Threats Adversarial cyber campaigns can cumulatively produce strategic loss for the United States and its allies, and they increasingly put the development goals of emerging economies at risk. Cyber threats continue to intensify in both frequency and severity, with increased risks of escalatory or uncontrolled cyber activity. State actor and non-state actors, including criminals, terrorists, and violent extremists, have tremendous incentives to invest in and exploit digital technologies to threaten our and other’s national interests. The People’s Republic of China (PRC) presents the broadest, most active, and most persistent cyber threat to government and private sector networks in the United States. Beijing has mounted cyber espionage operations against government, commercial, and civil society actors and has increased its ability to carry out destructive and disruptive cyberattacks. The PRC is capable of launching cyberattacks that could disrupt oil and gas pipelines, rail systems, and other critical infrastructure services within the United States or its allies and partners. Attempts to compromise critical infrastructure by PRC actors are designed in part to pre-position themselves to be able to disrupt or destroy critical infrastructure in the event of a conflict—either to either prevent the United States from being able to project power into Asia, or to affect our decision-making during a crisis by instigating societal chaos inside the United States. Both state-sponsored activity and that of PRC-linked actors are part of the PRC cyber approach. A persistent cyber threat, the Russian government is refining its cyber espionage, cyberattack, influence, and information manipulation capabilities to threaten other states and to weaken U.S. alliances and partnerships. Russia continues to provide safe haven to transnational cybercriminal actors, such as disruptive ransomware gangs. Russia’s cyberattacks in support of its 2022 unprovoked invasion of Ukraine were intended to destabilize the Ukrainian state and military and have resulted in spillover effects onto civilian critical infrastructure in other European countries. As the war continues, Russian government and Russian government-aligned cyber actors have targeted Ukraine with cyber operations against the public and private sectors, information manipulation and online influence operations, and attempts to divert and censor Ukrainians’ access to the Internet. Russia appears particularly focused on improving its ability to target critical infrastructure in the United States to demonstrate its ability to damage infrastructure during a crisis. The governments of the Democratic People’s Republic of North Korea (DPRK) and Iran have both increased the scale of their malicious cyber activities. Facing multiple rounds of international sanctions, the DPRK evades controls through cybercrime and the theft of cryptocurrencies. DPRK hackers continue to gather intelligence on military technology targets as well as academia and think tanks. In addition, the DPRK dispatches thousands of skilled IT workers around the world to generate fraudulent revenue that ultimately contributes to its weapons of mass destruction and ballistic missile programs despite U.S. and UN sanctions. Iran’s growing expertise and willingness to conduct cyber operations threaten the security of networks and data globally. Iran’s opportunistic approach to cyberattacks makes critical infrastructure owners in the United States susceptible to being targeted by Iranian actors, particularly when Tehran believes that it must demonstrate it can push back against the United States in other domains. Iranian actors have engaged in a wide range of intelligence-gathering operations around the world, and—in the wake of Hamas’ atrocities on October 7, 2023, and Israel’s military operations in Gaza—have conducted wiper, website takedown, hack and leak operations, espionage, and online information manipulations campaigns. Iranian actors have also conducted malicious activity against operational technology devices used in the water sector and other industries. Cyber criminals and criminal syndicates operating in cyberspace now represent a specific threat to the economic and national security of countries around the world. Cybercrime and online fraud cause significant harm to economic development, with small- to medium-sized enterprises and financial service providers especially at risk. According to one estimate, the global cost of cybercrime is estimated to top $23 trillion in 2027. [3] [3] [3] Ransomware incidents have disrupted critical functions, services, and businesses, from energy pipelines and food companies to schools and hospitals. Ransomware attacks against the healthcare industry can undercut the level of care provided to patients and others under care. Total economic losses from ransomware attacks worldwide continue to climb, reaching into the billions of U.S. dollars annually. Ransomware groups often operate out of safe haven jurisdictions whose governments, often adversaries like Russia, do not cooperate with law enforcement and sometimes encourage, direct, sanction, or tolerate their activities. Terrorists’ and violent extremists’ use of digital technologies also represents a threat to the national security of the United States and its allies and partners. Malign activities include the use of information and communications technologies (ICT) to spread violent propaganda; encourage radicalization and mobilization to commit violent acts; recruit individuals to terrorist organizations; to train, plan, and coordinate attacks; and finance terrorist acts. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

The Growing Cyber Threat Landscape: Insights into State-Sponsored and Criminal Cyber Activities

ক্রমবর্ধমান সাইবার হুমকি ল্যান্ডস্কেপ: রাষ্ট্র-স্পন্সর এবং অপরাধমূলক সাইবার কার্যকলাপের অন্তর্দৃষ্টি

Die wachsende Cyber-Bedrohungslandschaft: Einblicke in staatlich geförderte und kriminelle Cyber-Aktivitäten

El creciente panorama de amenazas cibernéticas: información sobre las actividades cibernéticas criminales y patrocinadas por el Estado

Le paysage croissant des cybermenaces : aperçu des cyberactivités criminelles et parrainées par l'État

बढ़ता साइबर खतरा परिदृश्य: राज्य प्रायोजित और आपराधिक साइबर गतिविधियों की जानकारी

拡大するサイバー脅威の状況: 国家主導および犯罪的なサイバー活動に関する洞察

증가하는 사이버 위협 환경: 국가 후원 및 범죄적 사이버 활동에 대한 통찰력

O cenário crescente de ameaças cibernéticas: insights sobre atividades cibernéticas criminosas e patrocinadas pelo Estado

Растущий ландшафт киберугроз: понимание спонсируемой государством и преступной кибердеятельности

Büyüyen Siber Tehdit Ortamı: Devlet Destekli ve Kriminal Siber Faaliyetlere İlişkin Bilgiler

Bối cảnh mối đe dọa mạng ngày càng tăng: Thông tin chuyên sâu về các hoạt động tội phạm mạng và do nhà nước tài trợ

日益严重的网络威胁形势：深入了解国家支持的网络犯罪活动

U.S. Digital Policy and Cyberspace Blueprint for Global Progress

Stories, News & Mentions about QONTO

Qonto’s Stories on HackerNoon