Humanity” will be on show until 21 January 2024. Produced by Palazzo Reale and Skira Editore, it is curated by Nicolas Ballario and Federica Crivellaro. It showcases 65 large-format photographs, a visual testimony of the evolution of Nelson as a ...

PhotoVogue Festival 2023 Side Events: “JIMMY NELSON. Humanity” at Palazzo Reale

Jean-François Manzoni’s tenure as IMD President started in January 2017. In the summer of 2020, his mandate was renewed for five years (2022-2026) with an understanding that if circumstances enabled it, an early transition might be considered. “In the ...

IMD initiates an early leadership transition

India’s digital evolution has been characterized by staggering numbers. With over 1.3 billion people, it boasts one of the world’s largest digital populations. This demographic diversity is both a challenge and an opportunity, and India has harnessed ...

Driving India's digital transformation

Paris modevecka är slut för den här gången. Nu vet vi hur vi ska klä oss nästa vår och sommar. I förra veckan avslutades modeveckan i Paris och höstens

Fem stora trender på Paris modevecka

Ozzy Osbourne lär ha sagt att The Cardigans version av ”Sabbath bloody sabbath” från 1994 är ”det läskigaste han någonsin hört”. Tio år senare bildades Hellsongs, loungemetal blev ett begrepp och bandet big in Tyskland innan allt rann ut likt en utspilld frozen margarita i sanden. På Skeppet är de tillbaka, argare och gladare än någonsin.

Recension: Hellsongs, Skeppet, lördag

En annan spännande variant är att blanda in gräddvita toner i din manikyr. Vinrött och gräddvitt blir en oslagbart fräsch kombo som välkomnar hösten på bästa sätt. Vill du ha en något coolare look kan du satsa på genomskinliga jelly nails, perfekta för övergången mellan sommar och höst.

Vinrött är höstens trendigaste manikyr

På scen står inte bara Madonna, utan också de utmålade rivalerna Britney Spears och Christina Aguilera. Popprinsessorna är klädda i vita spetskorsetter, korta shorts och knähöga boots. De har nyss slängt av sig sina skira brudslöjor och Christinas svarta hår står i stark kontrast till Britneys blonda.

Christina Aguilera - kampen för att ta sig ur Britneys skugga

På sverigesradio.se använder vi nödvändiga kakor och liknande tekniker för att webbplatsen ska fungera på ett  bra sätt. Vi använder också kakor och liknande tekniker för webbanalys så att vi kan förb

Att upprätthålla en fasad: Samuel Larsson, Köpenhamn

Tassels, glitter och glamour syntes överallt i Maria Montazamis lyxiga villa. I dag har hemmet i Kalifornien fått en mer avskalad och modern känsla.

Jan Emanuel: ”Man vill inte behöva öppna luckorna”

Skira

skira

We saw the emergence of a new Generative AI behemoth, OpenAI, with the introduction of ChatGPT on November 2022. ChatGPT serves as a chat-oriented interface for the underlying model it employs. The first version of the chatbot relied on a large language model called GPT-3.5, which sure blew all our minds. But on Tuesday 14th of March 2023, 4 months after the first launch of ChatGPT, OpenAI is pepping things up with the release of GPT-4, a more capable model than previous versions. Initially designed for engaging with GPT-3.5, the chatbot now functions as a medium to interact with GPT-4. https://twitter.com/sama/status/1635687853324902401?embedable=true What can GPT-4 do? The same thing that GPT-3.5 can do, but more. The company said in a blog post, “GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.” Since 2018, OpenAI has been disseminating GPT language models, but of all Open AI's huge language models, GPT-4 is not just the newest, but also the most potent. OpenAI reported that it used Microsoft Azure to train the model. Here are a couple of ways GPT-4 compares to GPT- 3.5 GPT-4 accepts visual inputs, and GPT-3.5 doesn’t Given that GPT-4 is "multimodal," as specified by OpenAI, and can comprehend many "modalities" of information, it can be prompted to produce content by both visual and textual cues while GPT-3.5 was essentially able to read and write text. Nevertheless, the company is not deploying its image-description capability immediately, and the GPT-4 version remains available only to subscribers of OpenAI's ChatGPT Plus users and developers, and only supports text. Compared to GPT-3.5, GPT-4 has a larger buffer Simply put, GPT-4 has a longer memory than the previous model. With GPT-3.5 and the previous iteration of ChatGPT, the maximum was 4,096 "tokens," or roughly 8,000 words, while the maximum token count for GPT-4 is 32,768, or around 64,000 words. That’s 8 times more memory capacity. GPT-4 has a variety of "personas." or steerability, unlike GPT-3.5 Steerability refers to the ability to control or influence the output of a language model in a specific direction or style. In other words, it allows users to guide the language model to produce text that fits a particular tone, style, or topic. While GPT-3 does not have built-in steerability features, GPT-4 does.  With GPT-4, developers and ChatGPT users can specify their AI's style and task by giving such directions in the "system" message, as opposed to the traditional ChatGPT personality with a defined verbosity, tone, and style. The company stated that although this feature is currently only accessible to developers, it will soon be made available to ChatGPT users as well. GPT-4 has a broader language range. This means that GPT-4 can understand and generate text in a wider variety of languages than its predecessor GPT-3.5. This is a significant advancement in the field of natural language processing.  According to OpenAI, GPT-4 beats the English-language performance of GPT-3.5 and other LLMs in 24 out of the 26 languages they tested. As a result, GPT-4 can respond accurately to a large number of multiple-choice questions in 26 different languages. GPT-4 has lesser ‘hallucinations’ on facts Even while it does not fully stop hallucinations or making up facts, GPT-4 considerably lessens them in comparison to earlier variants. The company said the updated language model continues to "hallucinate" facts and exhibit flawed logic, though not as bad as GPT-3.5. Hallucinations in the context of language model software refer to the generation of text that appears to be coherent and meaningful but is not actually supported by the input data. This can occur when a language model generates text that is not grounded in the context or is not consistent with the semantic meaning of the input text. In other words, a language model may sometimes generate text that is not accurate or plausible based on the information it has been trained on, resulting in what is commonly referred to as "hallucinations." The model also incorporates social biases and adversarial prompts in addition to the above limitations. OpenAI’s blog post stated, “GPT-4 scores 40% higher than the latest GPT-3.5 on  internal adversarial factuality evaluations.” According to LLMs have learned to mimic human prejudices of race, gender, religion, and class as a result of their training on internet text and visuals. Evidently, AI systems have the potential to perpetuate and amplify existing biases in society, rather than helping to mitigate them. The Washington Post, GPT-4 also has additional restrictions similar to those found in GPT-3.5, one of which is that it still lacks knowledge of events that occurred after September 2021. The ability of others to teach it new things is also limited by the fact that it does not learn from its experiences. Regardless, GPT-4 is still smarter; as it is more accurate, more precise, and has better expertise. The language model has also shown that it is capable of acing exams. According to OpenAI, it scored at the 90th percentile on a mock bar exam, the 93rd percentile on an SAT reading test, and the 89th percentile on an SAT math test. Where is GPT4 currently being used? GPT-4 has the potential to be used in a wide range of applications that require natural language understanding and generation. The language-learning software Duolingo, the payment provider Stripe, the online learning platform Khan Academy, and the financial services company Morgan Stanley were among the companies mentioned in OpenAI's statement that are currently using the new model. Microsoft also reported that its chatbot, BingAI has been using GPT-4 all along, even before the model's public release. “We are happy to confirm that the new Bing is running on GPT-4, which we’ve customized for search, If you’ve used the new Bing preview at any time in the last five weeks, you’ve already experienced an early version of this powerful model.” Yusuf Mehdi, corporate vice president, and chief marketing officer for consumers at Microsoft, wrote in a statement. https://www.youtube.com/watch?v=hdhZwyf24mE&embedable=true GPT-4 "excels in tasks that require advanced reasoning, complicated instruction understanding, and more creativity," according to OpenAI. Users have reported using GPT-4 in a variety of inventive ways in the brief time since its release: https://twitter.com/skirano/status/1635736107949195278?s=20&embedable=true https://twitter.com/YosarianTwo/status/1635780666632687617?s=20&embedable=true The possibilities for this new tool are believed to be endless. https://twitter.com/mckaywrigley/status/1635740909383061504?s=20&embedable=true How can GPT-4 be accessed? Fundamentally, to access ChatGPT, you must first register on the OpenAI , but doing so gives you the option of using only GPT-3.5. You will require a paid subscription to ChatGPT Plus, the more expensive variation of ChatGPT, in order to use GPT-4. website Since Microsoft has confirmed that the new Bing runs on the new model of GPT, it is obvious then that Millions of Bing users can already access it through the chatbot. https://twitter.com/JordiRib1/status/1635694953463705600?embedable=true The new model is also accessible via API, the company stated that they are prioritizing API access for developers that give outstanding model assessments to OpenAI Evals during the slow rollout of GPT-4 in order to learn how they may enhance the model for everyone.

GPT-4 Launches as the Next Generation of Artificial Intelligence Large Language Models

GPT-4 কৃত্রিম বুদ্ধিমত্তা বড় ভাষার মডেলের পরবর্তী প্রজন্ম হিসাবে চালু হয়েছে

GPT-4 wird als nächste Generation großer Sprachmodelle mit künstlicher Intelligenz eingeführt

GPT-4 se lanza como la próxima generación de modelos de lenguaje grande de inteligencia artificial

Lancement de GPT-4 en tant que nouvelle génération de grands modèles de langage d'intelligence artificielle

GPT-4 ने आर्टिफिशियल इंटेलिजेंस के बड़े भाषा मॉडल की अगली पीढ़ी के रूप में लॉन्च किया

次世代の人工知能大言語モデルとして GPT-4 を発表

GPT-4, 차세대 인공 지능 대형 언어 모델 출시

GPT-4 é lançado como a próxima geração de grandes modelos de linguagem de inteligência artificial

GPT-4, Yeni Nesil Yapay Zeka Büyük Dil Modelleri Olarak Piyasaya Sürüldü

GPT-4 ra mắt dưới dạng Mô hình ngôn ngữ lớn trí tuệ nhân tạo thế hệ tiếp theo

GPT-4 作为下一代人工智能大型语言模型推出

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 10 Elliptic and spiral galaxies It seems that it partially explains the classification of galaxies, harboring supermassive black holes at their cores. Indeed, on the one hand, supermassive black holes of multi-solitonic type could generate elliptic galaxies. On the other hand, multi-gyratonic black holes could generate spiral galaxies. The number of spirals and their configuration is related in this case to the number of multiversal layers and the harmony of the spheres (cf. sections 4 and 9). Relativistic jets, emitting by supermassive black holes at galaxy cores, also suggest their gyratonic nature.

Hyperverse, 5-dimensional Gravity and Multiverses: Elliptic and Spiral Galaxies

Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Improved Compound Spiral Car Spring for Railway Carriages. here Improved Compound Spiral Car Spring for Railway Carriages. Our engravings illustrate an improved compound car-spring, which appears to possess all the requisites of a first-class spring, combining in its construction extreme simplicity with great strength, and a feature whereby the power of the spring increases with increase of the load, and , so that its flexibility remains nearly constant for all loads. vice versâ Fig. 1 is a perspective view of this spring, with a portion of the side of the case broken out to show the interior arrangement of the spiral springs. Fig. 2 is a section of the compressing plate. Fig. 3 is a plan view, showing the arrangement of the tubes which enclose the springs. The case is cast in two pieces. Its vertical wall is cast in a single piece, and has at the top a flange or bead extending inwardly, against which the compressing plate abuts when the spring is not compressed, as shown in Fig. 2. A bottom plate completes the case. The spiral components of the spring are inclosed in tubes, as shown in Figs. 1 and 3. It is not deemed essential that these tubes should be seamless, or that their edges, brought together in bending, should be soldered, brazed, or welded. They act merely as guides to compel the component springs to expand or contract in vertical lines, and need only be strong enough for that purpose. The compressing plate is formed with concentric steps or ledges, as shown in Fig. 2, so that with light loads, only a portion of the component spirals act. With a heavier load a new series of spirals is brought into action, and so on, till the spring is loaded to its full capacity. This feature is novel, and as important as novel, as it gives the spring a far more easy and flexible carriage, with light loads, than would be the case if all the spirals were permitted to act. In putting the spring together, the vertical part of the case is inverted. The compressing plate is then placed within the case, resting upon the inner flange of the case above described. The tubes with their inclosed springs are then arranged in position, as shown in the plan view, Fig. 3. The bottom plate of the case is then placed in position, and held to its place by lugs and rivets, as shown in Fig. 1; the spring is then ready for use. The employment of tubes in the manner described, enables springs of the greatest practical length to be used, without the sectional or division plates met with in other spiral car springs. A greater and easier movement is therefore obtained. These springs can, it is claimed, compete in price with any spring in market, and are guaranteed by the manufacturers. Patented through the Scientific American Patent Agency, December 27, 1870, by Albert Potts, whom address for further information, No. 490 North Third street, Philadelphia, Pa. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American, Volume XXIV., No. 12, March 18, 1871. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19180/pg19180-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Improved Compound Spiral Car Spring for Railway Carriages

Sharing a new article by a seasoned software engineer and tech enthusiast Alex Babin (with his permission). As of 2023, microservices are all-pervasive in the IT world. According to , 85% of respondents from large companies (5,000 or more employees) claim they are currently using microservices architecture for their IT projects. Statista With microservices seemingly deemed to be a new norm in software development, why would anyone choose to get back to monolith? Well, there’s some substantial reasoning for this transition. As Eberhard Wolff argues in his paper , in the microservices architecture, “excessive effort is spent on matters that do not really help the project” — mostly due to its complexity and massive use of diverse tools, languages and frameworks. Why Microservices Fail: An Experience Report Today, I’d like to dive deeper into what makes monolith a better choice in certain scenarios. Let’s start! Common issues with microservices While microservices architecture offers numerous benefits, it also comes with some potential downsides that should be considered. Here are a few: Increased complexity Microservices require managing a larger number of services, each with its own deployment, monitoring, and management requirements. This introduces a higher level of complexity compared to monolithic architecture. With multiple services communicating with each other, there is a need for robust network communication and coordination mechanisms. Distributed system challenges Microservices architecture introduces the complexities of distributed systems, such as handling network latency, ensuring data consistency across services, and managing failures and errors. It requires implementing proper error handling, resilience mechanisms, and fallback strategies to maintain the overall system reliability and performance. Testing and debugging issues With microservices, testing and debugging become more complex due to the distributed nature of the architecture. Testing individual services in isolation and ensuring proper integration testing can be challenging. Debugging issues that span across multiple services requires additional effort and specialized tools to trace and diagnose problems. Development and deployment dependencies Microservices often require services to be developed and deployed independently. This can lead to dependencies between teams working on different services, potentially slowing down the development and deployment processes. Ensuring proper coordination and communication between teams becomes crucial to avoid compatibility issues and ensure smooth integration. A sample microservices architecture on Azure Kubernetes Service (AKS). Source: Azure documentation When does monolith offer a better fit? Let’s now consider some scenarios where monolithic architecture typically works better than microservices. Firstly, a monolith can be a better choice for , since it is easier to develop and deploy as the entire application is built as a single unit. This simplicity can result in faster development cycles and reduced complexity in deployment and management. projects with fast turnaround times Additionally, monolithic architecture can be more suitable for . It can also provide better performance as all components of the application are tightly integrated and can communicate with each other more efficiently. smaller applications or projects with limited scalability requirements Another typical use case for a monolithic architecture is when . In such scenarios, where the application’s functionality and features are not expected to change significantly over time, a monolith can offer a straightforward and predictable development process. the project has well-defined and stable requirements For example, Amazon recently to monolithic architecture for video monitoring stating that it “produced superior performance over a microservices and serverless-led approach.” Specifically, the tech team that moving their service to a monolith enabled them to reduce infrastructure costs by over 90% as well as increase the scaling capabilities. shifted back reported The updated architecture of Amazon’s audio/video monitoring service. Source: Amazon Prime Video blog So which one is better? The short answer is: the one that better works for your purposes! More specifically, while microservices can be an ideal choice for highly scalable and complex systems that require flexibility and independent service management, there’s no reason to downgrade monolith simply because it’s not so widely used anymore. In many scenarios like small-scale applications with limited resources or projects with well-defined and stable requirements, a monolithic architecture can actually be the optimal solution. It provides simplicity, easier development and debugging, and avoids the complexities of managing a distributed system. In case you’re not sure whether you’ll need to switch to microservices as your system is scaling, a good solution is to use a language such as that offers a powerful combination of features. It provides a strong module system with built-in tools for analyzing dependencies and simplifying function calls through its macro system. Additionally, Elixir’s built-in actor system enables concurrent and fault-tolerant execution of code, allowing for easy scalability and distribution across multiple machines. Elixir In general, starting with a monolithic architecture is often the optimal approach. However, if the need arises to extract certain components into separate services, you can do so with minimal code changes if you use Elixir. Furthermore, if the transition to microservices becomes necessary, you will already have the required modularity and DevOps setup in place.

New Spiral Cycle: Why Microservices Are Overrated

The Story-book of Science by Jean-Henri Fabre, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE SPIRAL SNAIL CHAPTER LXXI. THE SPIRAL SNAIL “WHEN the snail crawls, it bears aloft, as you know, four horns.” “Horns that come out and go in at will,” added Jules. “Horns that the animal turns every way,” said Emile, “when you put the shell on the live coals. Then the snail sings be-be-be-eou-eou.” “Stop that cruel play, my child. The snail does not sing; it is complaining, in its own way, of the fiery tortures. Its slime, coagulated by the heat, first swells and then shrinks, and the air that escapes by little puffs produces that dying wail. “In one of La Fontaine’s fables, where there are so many good things about animals, he tells us that the lion, wounded by a horned animal, “Straight banished from his realm, ’t is said, All sorts of beasts with horns— Rams, bulls, goats, stags, and unicorns. Such brutes all promptly fled. A hare, the shadow of his ears perceiving, Could hardly help believing That some vile spy for horns would take them, And food for accusation make them. Adieu, said he, my neighbor cricket; I take my foreign ticket. My ears, should I stay here, Will turn to horns, I fear; And were they shorter than a bird’s, I fear the effect of words. These horns! the cricket answered; why, God made them ears; who can deny? Yes, said the coward, still they’ll make them horns, And horns, perhaps, of unicorns! In vain shall I protest. .  The translation is that of Elizur Wright, Jr., published by James Miller, New York, 1879. 4 “This hare evidently exaggerated things. Its ears have remained ears, to all observers. We do not know whether the snail exiled himself in these circumstances; man is almost unanimous in regarding as horns what the snail bears on its forehead. ‘You call those horns!’ the cricket would have exclaimed, being better advised than man; ‘you must take me for a fool.’” “Then they are not horns?” asked Jules. “No, my dear. They are at once hands, eyes, nose, and a cane for the blind. They are called tentacles. There are two pairs of unequal length. The upper pair is the longer and more remarkable. “Right at the end of each long tentacle you see a little black point. It is an eye as complete as that of the horse and ox, in spite of its minute dimensions. What is necessary for making an eye, you are far from suspecting. It is so complicated I will not try to tell you. And yet it is all to be found in that little black point that is scarcely visible. That is not all: beside the eye is a nose, that is to say an organ especially sensitive to odors. The snail sees and smells with the tips of its long tentacles.” “I have noticed that if you bring anything near the snail’s long horns, the animal draws them in.” Elephant “This combination of nose and eye can retreat, advance, go to meet an object, and catch odors from all sides. To find a similar nose, you must go from a snail to an elephant, whose trunk is an exceptionally long nose. But how much superior the snail’s is to the elephant’s! Sensitive to odors and light, eye and nose at the same time, it can retire within itself like the finger of a glove, disappear by reëntering the animal’s body, or come out from under the skin and lengthen itself like a telescope.” “I have often seen how the snail pulls his horns in,” observed Emile. “They fold back inward and seem to bury themselves under the skin. When anything annoys it, the animal puts its nose and eyes into its pocket.” “Precisely. To protect ourselves from too strong a light or an unpleasant odor, we shut our pupils and stop up our nose. The snail, if the light troubles or some smell displeases it, sheathes eyes and nose in their covering; it puts them into its pocket, as Emile says.” “It is an ingenious way,” Claire remarked. “You said, too,” interposed Jules, “that the horns served it as a blindman’s cane.” “The animal is blind when it has drawn in its upper tentacles, partly or wholly; it then has only the two lower ones, which explore objects by the touch better than does the cane of a blind man, for they are very sensitive. The two upper tentacles, besides their functions of eye and nose, also play the part of blindman’s cane, or, better still, that of a finger that touches and recognizes objects. You see, little Emile, one does not know everything about a snail when one knows its wail on the fire.” “I see. Who of us would have suspected that those horns are eyes, nose, blindman’s cane, fingers, all at the same time?” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jean-Henri Fabre (2018). The Story-book of Science. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/56795/pg56795-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

THE SPIRAL SNAIL

SPIRAL AND PLANETARY NEBULÆ

On November 22nd 2022, AAVE v2, a lending platform on Ethereum, incurred bad debt resulting from a major liquidation event involving a single user who had borrowed close to $40M worth of CRV tokens. In light of this occurrence, , , and released a debunking the speculations surrounding the event. They introduced the concept of toxic liquidations, their prominence across many DeFi protocols, and how they can be mitigated. Daniele Pinna Jakub Warmuz Amit Chaudhary research paper What Really Happened During The Aave Exploit? The AAVE exploit was caused by , who is known for . Last November, Eisenberg attempted to exploit AAVE in a similar fashion. Using tranches made up of tens of millions of USDC as collateral, Eisenberg borrowed millions of Curve’s native token, CRV, on AAVE before sending it to OKX – probably to sell. Avraham Eisenberg exploiting Solana-based Mango Markets for more than $100 million Using USDC as collateral to borrow tokens and then sell them is a common method used by sophisticated players to make short positions in DeFi. While the attempted exploit caused volatility on CRV, the attack did not work. AAVE withstood the exploit, and the hacker was left with nearly $10m in losses. But, the protocol was left with almost $2m in bad debt –  debt that cannot be recovered. While Eisenberg’s true intentions are yet unknown, he successfully brought a lot of attention to bad debt and toxic liquidation spirals in DeFi. Toxic Liquidation Spirals Liquidation refers to a loss management process by which a smart contract sells a user’s crypto assets to cover the debt. In most cases, the protocol will allow someone else to repay the debt, at a discount, in exchange for the collateralized asset. These individuals are known as liquidators and are given a bonus, which is higher for volatile currencies, as an incentive. ratio refers to the ratio of the loan’s value to the value of the collateral. It is used by lenders to measure the risk involved when approving a loan. Once a user’s LTV ratio exceeds a threshold set by the protocol, liquidators are called in to intervene. This model is often effective and helps to create a balanced portfolio for the borrower while protecting the lender from bad debt. Loan-to-Value (LTV) The AAVE exploits were mostly blamed on speculative price action or irresponsible portfolio positioning. However, Daniele Pinna,  head of research and investment at 0VIX, is debunking that and shedding light onto a fundamental flaw in many DeFi protocols. is a DeFi liquidity market protocol built on Polygon, which offers next-level features like pre-mined rewards, toxicity numbers, and 24-hour liquidation probability. Learn more about 0VIX and its unique risk management features . 0VIX here In the research paper published by the team, they introduced the concept of a “toxic liquidations spiral”, where, under the right circumstances, an over-collateralized loan can become under-collateralized entirely due to the protocol’s mechanics, and guaranteeing the creation of bad debt in the process. Liquidations are considered toxic when the liquidation incentives worsen the liquidated user’s LTV and the final LTV is greater than the initial. As the LTV increases, the collateral becomes less likely to cover the cost of the loan. A toxic liquidation spiral depends mainly on the incentive given to liquidators for executing liquidations in relation to the loan-to-value of the user being liquidated. For fixed liquidation incentives such as is common on many lending markets, a threshold exists beyond which the user’s loan-to-value will be guaranteed to increase at each successive liquidation. This is represented mathematically as: Using AAVE’s exploit as a case study, it becomes clear how AAVE’s 4.5% incentive on each liquidation drove Avi’s LTV higher after it surpassed the 95.6% mark. While this is undoubtedly a high loan-to-value for a user to hold on a lending market, it still represented an over-collateralized portfolio position. Once the LTV became greater than 0.9569, even if asset prices were to remain static, the user’s portfolio was guaranteed to become under-collateralized entirely as a result of the liquidation incentives enforced by the protocol, thus incurring bad debt. This is the toxic liquidation spiral that resulted in AAVE’s $1.75 million debt and is a flaw prevalent in many DeFi platforms. The team suggested two options DeFi lending platforms have to avoid bad debt incurred from toxic liquidation spirals. Fixing The Toxic Liquidation Spiral There are two ways AAVE and other DeFi lending protocols can prevent bad debt accrual. The first method is quite simple. Once the LTV exceeds LTVuc (under-collateralized), the protocol should halt the trade. 0VIX’s research team simulated the results that would have occurred if this method had been adopted and found that the portfolio would have momentarily become under-collateralized (around 750k) before immediately returning to a healthy state on its own once the CRV/USDC ultimately corrected downwards. Of course, there are many statistical possibilities depending on the price action of the token and other variables. Using the market risk assessment methodology, most simulations resulted in zero bad debt accrual for AAVE just by halting liquidations. The second method suggests a change in the liquidation logic by introducing dynamic rather than typically fixed parameters for liquidation incentives. It is important to remember that toxic liquidations occur when incentives worsen rather than improve the user’s LTV ratio. The protocol can avoid toxic liquidation spirals if these incentives are constantly adjusted to fit the formula below. In addition, the closing factor, which limits the number of collateralized tokens that can be liquidated at once, can also be made variable. The aim is to progressively liquidate larger portions of a user’s portfolio as it comes closer to becoming under-collateralized. By comparing the results of toxic liquidation spirals and mitigation policies, we find that there is a trade-off. There are higher chances of generating bad debt (∼ 85%) but more certainty on the size of that debt (tail mean = $820k) when toxic liquidation spirals occur. In contrast, the mitigation policies discussed offer considerably lower chances of generating bad debt (∼ 19%) while exhibiting more risk on the worst-case outcomes (tail mean= $1.4M), assuming no other open market interventions occur. The research by 0VIX has identified a significant problem and possible solutions to a shared flaw in DeFi lending platforms. Rather than dismissing AAVE’s exploit as a result of irresponsibility, volatility or speculative price action, protocols can now seek mitigation techniques and prevent bad debt. More information about individual DeFi protocol’s bad debt figures can be found in RiskDAO’s . bad debt dashboard

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