Stane&#x27;s company designed and manufactured a suit of powered armor, the &quot;Savage Steel&quot; battle-suit, based on technology stolen from Stark Enterprises. Different members of the Cabal all took turns wearing the suit, including Paul Trent and former members Harry Lennox, Johnny Leone, and Jimmy Zafar.

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Best AI photography and editing software for automotive dealers. Spyne AI helps car dealerships with the virtual studio, 360 spin, and inspection - Try for free

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Spyne helps you capture breathtaking images of your car like never before! It’s the ultimate AI photography and editing app for Android. Powered by advanced AI technology, Spyne empowers you to transform your car photos into professional-grade masterpieces.

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Spyne Company Profile - Office Locations, Competitors, Revenue, ...

Spyne | 46,930 followers on LinkedIn. Spyne is helping businesses create high-quality product visuals at scale with AI | One of the biggest pain points for any online seller is to showcase their products in the best possible manner to grab customer attention and increase conversions.

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Spyne is a Deep Tech startup helping businesses and marketplaces create and upgrade high-quality product images and videos at scale with AI.

Spyne - Crunchbase Company Profile & Funding

Spyne - Provider of smart camera for internet cataloging. Raised a total funding of $7.75M over 3 rounds from 47 investors.

Spyne - Company Profile - Tracxn

A transport and architecture agnostic rpc library that focuses on exposing public services with a well-defined API.

spyne · PyPI

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Spyne Documentation — spyne 2.11.0 documentation

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Penetration Testers’ Guide to Windows 10 Privacy & Security

Guía de los probadores de penetración para la privacidad y seguridad de Windows 10

Staking is a well-known strategy for earning yield among crypto users. In a nutshell, it is a process where you hold coins in a wallet to help the blockchain network keep it safe and running smoothly, and the best part is, you can get rewarded for it! Unfortunally, there are some limitations to staking. For example, you can’t trade or use the coins you’ve staked until the staking period ends. But what if I told you that it is possible to bypass these limitations and use the liquidity of staked coins to increase your income? This is where liquid staking derivatives (LSDs) come in. LSDs are tokens that are issued to you by the liquid staking protocol in place of your native tokens of the current network. The price of a derivative token is pegged to its underlying asset and is usually valued at around a 1:1 ratio. Once you’ve got your hands on some LSDs, you’ll start earning staking rewards every day, which will increase your wallet balance over time. But there’s more! You can also use these tokens in the DeFi market to get some yield opportunities. Examples of Liquid Staking Protocols There are many different liquid staking protocols, allowing you to stake assets on different networks and receive LSDs in return, here are a few of them: Lido is a liquid staking protocol for networks that provides staking solutions for crypto assets such as Ethereum ( ), Solana ( ), Polygon ( ), Polkadot ( ), and Kusama ( ). When staking, Lido users will receive stTokens (stETH, stMATIC, etc.) which represent their staked native assets (+ aggregated rewards). Lido Proof of Stake (PoS) ETH SOL MATIC DOT KSM Stader is a non-custodial smart contract-based multichain staking platform. It currently supports the following assets: Polygon ( ), BNB, Terra 2.0 ( ), Fantom ( ), NEAR Protocol ( ), Hedera (HBAR). After the user’s assets have been staked, Stader mints [Token]X derivatives (e.g.: BNBX, HBARX), which will be placed in the user’s wallet. Stader protocol MATIC LUNA FTM NEAR pSTAKE is another liquid staking protocol that enables you to access liquidity for your staked assets. By using pSTAKE, you can safely stake your PoS assets and receive the protocol’s staking derivatives — stkASSETs (such as stkBNB, stkATOM, and stkETH). pSTAKE Using LSDs in DeFi After depositing your assets into any of the mentioned liquid staking protocols, such as Lido, Stader, or pSTAKE, you will receive liquid staking derivatives in return, let’s explore how we can make them work for us. There are several strategies we can use. For example, we can deposit our LSDs into a lending protocol to receive constant rewards. Alternatively, we could borrow against them (by using the LSDs as collateral), allowing us to obtain more assets and continue to utilize the DeFi market. Moreover, we can borrow additional underlying assets, restake them, get LSDs, and lend them again to make a leverage staking with higher rewards. Another option is to provide our LSD tokens to the liquidity pool (LP) of a decentralized exchange (DEX) and receive incentives through trading fees. By doing so, we contribute to the liquidity of the DEX and earn rewards for facilitating swaps and get income from staking at the same time. Each of these strategies has its own pros and cons. Let’s examine them in more detail. Supply liquidity pools A liquidity pool is a collection of assets locked in a smart contract, used to provide liquidity for trading on a DEX. Usually, it’s a pair of two tokens that can be seamlessly exchanged with one another. The strategy is to supply a pair of LSD tokens and an underlying token (such as ETH and Lido’s stETH) to the pool. Why this pair? As mentioned earlier, the value of the LSD token is pegged to the value of the underlying assets, which can lead to lower impermanent losses. For Lido’s stETH/ETH pair, the most popular LP is Curve’s stETH-ETH liquidity pool that allows to simultaneously accrue trading fees, protocol incentives (CRV, LDO), as well as stETH staking reward. This yield strategy is fairly easy to execute, as it requires only a few transactions: receiving LSD tokens via staking/exchange and supplying the pair to a liquid pool. As a result, we can pay a minimum amount of network fees. However, there are some disadvantages to be aware of: Although impermanent losses are minimized compared to other conventional liquidity pools, they can still occur. Most exchanges require a 1:1 ratio of tokens to deposit them into liquidity pools. This means that you need to have almost 50% of your assets unstaked. Leveraged staking This strategy can be implemented using lending protocols. The basic idea is that lending protocols can accept LSDs as collateral, which allows users to borrow assets while still earning rewards from staking. By borrowing the underlying asset and staking it again, users can earn more LSDs. This cycle can be repeated as long as the loan health factor remains within an acceptable range. The algorithm for this strategy looks like this: Stake the underlying assets and receive LSDs. Use the received LSDs as collateral for borrowing from the lending protocol. Borrow as much of the underlying assets as the collateral factor allows. Repeat the process from step 1. By using this strategy, we can potentially earn more rewards than we normally get from staking. Why? And how can it be profitable, since we have to pay the loan (borrow interest)? Our collateralized LSDs generate rewards. Besides dividends from staking, we also receive a part of the interest paid by borrowers, and some lending protocols may incentivize users with additional rewards. Furthermore, since the value of the borrowed assets is always less than the value of collateral we put up for the loan (as the DeFi loan is always overcollateralized), and the interest on the loan is usually lower than the cumulative yield earned on the collateral, we are able to achieve a better APY in the end. Are there any pitfalls? Yes, there are a few things to keep in mind when using this strategy: Although the values of LSDs and the underlying asset are pegged to each other, liquidation is still possible, and the health factor of the loan must be constantly monitored to avoid losing the collateral. Borrow APY can suddenly rise, and in such cases, the strategy may become unprofitable. Then it may be necessary to withdraw funds and look for another profitable lending protocol or even another strategy. The implementation of this strategy involves many steps, and each step is a separate transaction for which you have to pay fees. This can be very expensive, especially when it comes to a blockchain like Ethereum, where the transaction fees can be high. Therefore, it’s important to carefully consider the fees involved and make sure they don’t eat into your profits. None of the LSDs strategies can be considered truly passive, as they require continuous monitoring of profitability dynamics, calculation of final earnings while taking into account impermanent losses and network fees, as well as keeping a close eye on the status of the protocols being used. However, there are options available to simplify this process such as protocols that support automated yield farming strategies. We also see potential in the automation of such strategies and are currently planning to add them to the Eonian protocol. More related info Want to learn more about lending protocols and leveraged yield farming? Check out “ ”. The Ultimate Guide to Leveraged Yield Farming Curious about impermanent losses and how they can impact your yield? Read our recent article, “ ”. Why do half of Uniswap v3 users lose money? Also published here.

Exploring Liquid Staking Derivatives: Introduction, Benefits, and Strategies

Michael Gerling MD: Advancing Modern Orthopedic and Spine Care

In the cutting-edge landscape of surgical robotics, the groundbreaking development of robot guided facet joint decortication and bone graft delivery capabilities stands as a testament to visionary technical leadership and innovative project management. Led by Saideep Nakka as Technical Lead, this pioneering advancement has set new benchmarks in robotic spine surgery, marking a significant milestone in the industry's evolution toward more sophisticated surgical automation. The project's success has fundamentally transformed the approach to spine procedures, introducing unprecedented precision and automation to critical surgical steps that traditionally relied heavily on manual technique. The project presented unprecedented challenges from its inception, requiring the seamless integration of complex hardware systems across multiple development sites to support a feature that had never before been implemented in robotic spine surgery. As Technical Lead for all hardware development, Saideep Nakka assumed responsibility for coordinating cross-site engineering teams, reporting directly to senior leadership, while ensuring the project maintained its ambitious goals of creating the market's first robotic system capable of bone cutting with integrated graft delivery. The complexity of the undertaking was magnified by the need to maintain precise control over bone removal depth while ensuring consistent graft material delivery – technical challenges that had previously proven insurmountable in robotic systems. At the core of this success story was Saideep's approach to technical leadership and stakeholder management. His leadership philosophy centered on systematic concept selection and comprehensive system design that prioritized user needs while maintaining technical feasibility. Through his guidance, engineering teams across different sites were unified under a clear vision, fostering an environment of collaborative innovation and careful consideration of system-level interactions. This approach was particularly crucial given the intricate interface between hardware systems, Robotic arm, software, and user interface elements that needed to work in perfect harmony. The technical complexity of the project demanded innovative solutions at every turn. Saideep implemented a rigorous evaluation framework for concept selection, incorporating multiple criteria including surgical workflow integration, technical feasibility, and risk assessment. His systematic approach to trade-off analysis ensured that engineering decisions were made with full consideration of their system-wide implications, from mechanical performance to user experience. This methodical process was instrumental in navigating the challenges of integrating precise bone cutting capabilities with controlled graft delivery mechanisms – a combination that had never before been achieved in robotic spine surgery. Cross-site coordination became a cornerstone of the project's success. Saideep established regular technical forums where engineering teams from different locations could share progress, discuss challenges, and collaborate on solutions. These sessions proved invaluable in maintaining alignment across teams working on different aspects of the system, from mechanical design to control systems integration. His ability to facilitate clear communication and decision-making across diverse technical teams ensured that development progress remained synchronized despite the geographical distribution of resources. The results were both groundbreaking and impactful to patient outcomes. Through meticulous attention to technical detail and cross-functional coordination, Saideep Nakka drove the development of a revolutionary feature that expanded the capabilities of spine robotic systems. His leadership ensured that the complex integration of hardware components aligned perfectly with the new software release, resulting in a seamless launch that set new standards for robotic spine surgery. The system's ability to precisely decorticate facet joints and deliver bone graft material to pre-planned depths represented a significant leap forward in surgical automation. The technical achievements of the project were matched by its clinical impact. Surgeons who adopted the new capability reported significant improvements in procedural workflow and consistency. The ability to automate these critical steps in spine surgery not only enhanced precision but also reduced procedure time and improved reliability. This clinical success served to validate the technical decisions made throughout the development process and demonstrated the real-world value of the innovation. The impact of this innovation was recognized at the highest levels of the medical device industry. Being first-to-market with robot guided facet joint decortication and bone graft delivery capabilities represented a significant competitive advantage and demonstrated the organization's commitment to pushing the boundaries of surgical robotics. The success of this feature launch not only enhanced the system's capabilities but also strengthened market confidence in robotic technology as the future of spine surgery. The achievement has since become a reference point for innovation in surgical robotics, inspiring further advancements in automated surgical procedures. The project's success catalyzed a broader transformation in the field of robotic spine surgery. The demonstrated ability to automate complex surgical tasks with high precision has accelerated the adoption of robotic systems in spine procedures. Healthcare providers have increasingly recognized the value of robotic assistance in improving surgical outcomes, leading to greater investment in robotic technology across the industry. This shift has validated the project's ambitious goals and confirmed its role in shaping the future of spine surgery. For Saideep Nakka, this project marked a pivotal moment in his career trajectory. His ability to lead complex cross-site projects involving software, hardware, and firmware integration demonstrated his capacity for managing multifaceted technical challenges while maintaining clear communication across diverse teams. The experience gained from this project has positioned him for even more complex integration projects, further cementing his reputation as a leader in medical device innovation. His success in navigating both technical and organizational challenges has established him as a go-to leader for pioneering development efforts in surgical robotics. Beyond the immediate success of the feature launch, the project stands as a model for effective technical leadership in medical device development. Saideep's methodical approach to concept selection and system design, combined with his ability to foster clear understanding of technical trade-offs across development teams, provides a blueprint for future innovations in surgical robotics. His leadership style, characterized by systematic decision-making and inclusive collaboration, has become a template for managing complex technical projects in the medical device industry. Looking ahead, this achievement sets a new standard for what's possible in robotic spine surgery. The successful integration of bone cutting and graft delivery capabilities has opened new possibilities for automated surgical procedures, while the project's execution demonstrates how focused technical leadership can drive breakthrough innovations in healthcare technology. As the field of surgical robotics continues to evolve, this project serves as a powerful example of how visionary leadership and systematic development approaches can advance the boundaries of medical technology. About Saideep Nakka A distinguished leader in medical device innovation, Saideep Nakka combines deep technical expertise with outstanding project leadership capabilities. His career has been marked by successful management of complex technical integrations, particularly in surgical robotics and minimally invasive surgical systems. His collaborative approach to leadership, evidenced by his successful coordination of cross-site engineering teams and stakeholder management, has resulted in groundbreaking innovations that advance the field of surgical robotics. Saideep's commitment to technical excellence and innovation is demonstrated through his consistent success in leading multifaceted development projects and mentoring engineering teams toward breakthrough achievements in medical technology. His ability to bridge technical complexity with practical clinical needs has established him as a respected leader in the medical device industry, consistently delivering innovations that improve surgical outcomes and advance patient care. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. here here

Leadership Success Story: Saideep Nakka's Pioneer Innovation in Robotic Spine Surgery

Erfolgsgeschichte der Führung: Saideep Nakkas Pionier-Innovation in der Robotic Spine Surgery

Historia de éxito de liderazgo: la innovación pionera de Saideep Nakka en la cirugía robótica de la columna vertebral

A historia de éxito do liderado: a innovación pioneira de Saideep Nakka na cirurxía robótica da columna vertebral

नेतृत्व की सफलता की कहानी: Saideep Nakka के रोबोटिक रीढ़ सर्जरी में अग्रणी नवाचार

リーダーシップ成功物語:サイデープ・ナッカのロボット脊椎外科革新

Алдыңғы мақалаЧТО НАДО ДЕЛАТЬ ДЛЯ РАЗВИТИЯ ПРОМЫШЛЕННОСТИ?

리더십 성공 스토리: Saideep Nakka의 로봇 척추 수술 혁신

Tantara fahombiazana amin'ny fitondran-tena: Saideep Nakka ny Innovation Pioneer amin'ny Robotic Spine Surgery

Эрүүл мэндийн амжилт түүх: Saideep Nakka-ийн Robotic Spine Surgery-ийн шинэчлэл

Povestea de succes a liderilor: Inovația pionieră a lui Saideep Nakka în chirurgia robotică a coloanei vertebrale

Historia e suksesit të udhëheqjes: Inovacioni pionier i Saideep Nakka në kirurgjinë robotike të shpinës

领导力成功故事:Saideep Nakka在机器人脊髓外科领域的先驱创新

The U.S. AI sector constantly shifts, and startups attract significant investments. In 2025 alone, at least nine AI companies have raised over $100 million each. Anthropic secured an astounding $3.5 billion in Series E funding, while Together AI obtained $305 million in a Series B round. The influx of capital has prompted startups to manage their image, capture the proper attention, and carve out a distinct presence in a cutthroat market. Startups look to public relations to help them stand out and build credibility. Companies that effectively showcase their value can get featured on Forbes, Entrepreneur, and other major publications. These opportunities attract investors and set them apart from the competition. Spynn Publicity, a PR agency with deep media connections, helps companies craft compelling messages that highlight their strengths while making AI advancements more accessible to the public. The PR Challenges in AI’s Expanding Market AI companies operate in a crowded space, making it difficult to attract attention. Many introduce complex technologies that are not always easy to explain. On top of that, AI raises questions about ethics and data privacy, which means companies need clear and honest communication. Spynn Publicity tackles these challenges by linking technical breakthroughs and mainstream audiences. For example, when an AI startup specializing in predictive analytics partnered with a healthcare provider, Spynn helped create an angle showing a tech milestone in saving lives. This shift in framing helped the company secure coverage in national publications and build credibility beyond the AI sector. Matteo Ferretti, CEO of Spynn Publicity, says, “AI companies need to tell stories that connect with investors and everyday people. Explaining technology isn’t enough. Startups need to show how it improves lives.” How AI Startups Can Use PR to Get Ahead PR goes beyond news coverage. AI companies need a clear strategy to help people understand their work and why it matters. Many startups assume that securing media coverage is enough, but without a well-crafted story, attention can be fleeting. Spynn Publicity helps AI leaders secure speaking opportunities at events, develop editorial content, and get featured on news sites, which establishes them as respected voices in AI. For example, after Spynn facilitated a CEO’s op-ed in a major tech publication, the startup saw a surge in inbound investor inquiries and strategic partnership opportunities. Matteo Ferretti also highlights the importance of media relationships, “Working closely with journalists allows us to match stories with the right publications. A compelling pitch is good, but building lasting trust with reporters secures consistent media coverage.” Why Spynn Publicity is a Leader in AI PR Spynn Publicity has an extensive network and a proven record of placing clients in notable publications. AI startups looking to expand their reputation can benefit from this level of access. Consider other startup companies following the lead of AI giants. Spynn helps shape their public image by telling compelling stories about their work. Instead of presenting AI as abstract technology, Spynn highlights its human impact, whether AI-driven solutions for sustainable energy or innovations improving cybersecurity. This approach builds trust with investors, policymakers, and the public. Matteo Ferretti explains, “AI PR requires balance. Startups must celebrate breakthroughs while addressing challenges like bias and transparency. The ones that get this right take the lead in technology and trust.” Building a Reputation That Lasts Reputation in AI is found in trust as much as in progress. Many startups focus on product launches, but trust is built between those milestones. Investors, customers, and regulators want confidence that a company can deliver its promises while handling ethical and security concerns. Startups that communicate their values and address industry challenges head-on build credibility that lasts. Consistent messaging helps AI companies stand out in a competitive space. Spynn often works with startups to refine their messaging before a big announcement, ensuring that every statement aligns with the company’s long-term vision. Engaging with media, participating in industry discussions, and sharing real-world impact stories reinforce a company’s reputation and make its technology more relatable. Spynn Publicity helps AI startups turn knowledge into influence. When a startup specializing in AI-powered legal analytics sought to differentiate itself, Spynn positioned its founders as respected voices in legal tech, helping them secure features in major business publications. A well-established reputation attracts attention and strengthens trust, ensuring that startups remain relevant and respected as the industry shifts. What’s Next for AI PR? AI startups have major opportunities ahead, but standing out is getting more difficult. The next wave of AI PR will be shaped by regulation, ethical debates, and how well startups communicate their impact. With more funding pouring in, competition is increasing, making strategic communication essential for success. Startups that sharpen their messaging and engage the right audiences will have the advantage. Companies must balance promoting their innovations with addressing public concerns, including transparency in AI development, data security, and regulatory compliance. Spynn Publicity helps AI startups stay ahead by securing media visibility, crafting strong narratives, and positioning leaders as trusted voices. The startups that invest in PR today will shape the industry’s future. The U.S. AI sector constantly shifts, and startups attract significant investments. In 2025 alone, at least nine AI companies have raised over $100 million each. Anthropic secured an astounding $3.5 billion in Series E funding, while Together AI obtained $305 million in a Series B round. The influx of capital has prompted startups to manage their image, capture the proper attention, and carve out a distinct presence in a cutthroat market. Startups look to public relations to help them stand out and build credibility. Companies that effectively showcase their value can get featured on Forbes , Entrepreneur, and other major publications. These opportunities attract investors and set them apart from the competition. get featured on Forbes get featured on Forbes Spynn Publicity, a PR agency with deep media connections, helps companies craft compelling messages that highlight their strengths while making AI advancements more accessible to the public. The PR Challenges in AI’s Expanding Market The PR Challenges in AI’s Expanding Market AI companies operate in a crowded space, making it difficult to attract attention. Many introduce complex technologies that are not always easy to explain. On top of that, AI raises questions about ethics and data privacy, which means companies need clear and honest communication. Spynn Publicity tackles these challenges by linking technical breakthroughs and mainstream audiences. For example, when an AI startup specializing in predictive analytics partnered with a healthcare provider, Spynn helped create an angle showing a tech milestone in saving lives. This shift in framing helped the company secure coverage in national publications and build credibility beyond the AI sector. Matteo Ferretti, CEO of Spynn Publicity, says, “ AI companies need to tell stories that connect with investors and everyday people. Explaining technology isn’t enough. Startups need to show how it improves lives. ” AI companies need to tell stories that connect with investors and everyday people. Explaining technology isn’t enough. Startups need to show how it improves lives. How AI Startups Can Use PR to Get Ahead How AI Startups Can Use PR to Get Ahead PR goes beyond news coverage. AI companies need a clear strategy to help people understand their work and why it matters. Many startups assume that securing media coverage is enough, but without a well-crafted story, attention can be fleeting. Spynn Publicity helps AI leaders secure speaking opportunities at events, develop editorial content, and get featured on news sites , which establishes them as respected voices in AI. For example, after Spynn facilitated a CEO’s op-ed in a major tech publication, the startup saw a surge in inbound investor inquiries and strategic partnership opportunities. get featured on news sites get featured on news sites Matteo Ferretti also highlights the importance of media relationships, “ Working closely with journalists allows us to match stories with the right publications. A compelling pitch is good, but building lasting trust with reporters secures consistent media coverage .” Working closely with journalists allows us to match stories with the right publications. A compelling pitch is good, but building lasting trust with reporters secures consistent media coverage Why Spynn Publicity is a Leader in AI PR Why Spynn Publicity is a Leader in AI PR Spynn Publicity has an extensive network and a proven record of placing clients in notable publications. AI startups looking to expand their reputation can benefit from this level of access. Consider other startup companies following the lead of AI giants. Spynn helps shape their public image by telling compelling stories about their work. Instead of presenting AI as abstract technology, Spynn highlights its human impact, whether AI-driven solutions for sustainable energy or innovations improving cybersecurity. This approach builds trust with investors, policymakers, and the public. Matteo Ferretti explains, “ AI PR requires balance. Startups must celebrate breakthroughs while addressing challenges like bias and transparency. The ones that get this right take the lead in technology and trust. ” AI PR requires balance. Startups must celebrate breakthroughs while addressing challenges like bias and transparency. The ones that get this right take the lead in technology and trust. Building a Reputation That Lasts Building a Reputation That Lasts Reputation in AI is found in trust as much as in progress. Many startups focus on product launches, but trust is built between those milestones. Investors, customers, and regulators want confidence that a company can deliver its promises while handling ethical and security concerns. Startups that communicate their values and address industry challenges head-on build credibility that lasts. Consistent messaging helps AI companies stand out in a competitive space. Spynn often works with startups to refine their messaging before a big announcement, ensuring that every statement aligns with the company’s long-term vision. Engaging with media, participating in industry discussions, and sharing real-world impact stories reinforce a company’s reputation and make its technology more relatable. Spynn Publicity helps AI startups turn knowledge into influence. When a startup specializing in AI-powered legal analytics sought to differentiate itself, Spynn positioned its founders as respected voices in legal tech, helping them secure features in major business publications. A well-established reputation attracts attention and strengthens trust, ensuring that startups remain relevant and respected as the industry shifts. What’s Next for AI PR? What’s Next for AI PR? AI startups have major opportunities ahead, but standing out is getting more difficult. The next wave of AI PR will be shaped by regulation, ethical debates, and how well startups communicate their impact. With more funding pouring in, competition is increasing, making strategic communication essential for success. Startups that sharpen their messaging and engage the right audiences will have the advantage. Companies must balance promoting their innovations with addressing public concerns, including transparency in AI development, data security, and regulatory compliance. Spynn Publicity helps AI startups stay ahead by securing media visibility, crafting strong narratives, and positioning leaders as trusted voices. The startups that invest in PR today will shape the industry’s future.

Spynn Publicity Helps AI Startups Win the PR Battle in the US Funding Surge

Spynn Publicity pomaže startupima s umjetnom inteligencijom da osvoje PR bitku u SAD-u

Spynn Publicity ayuda a las startups de IA a ganar la batalla de las relaciones públicas en el auge de la financiación en EE. UU.

Spynn Publicity- ը օգնում է AI Startups- ում հաղթել US Funding Surge- ում

Publisitas Spynn Membantu Startup AI Memenangkan Pertarungan Humas di AS yang Melonjak Pendanaan

Spynn Publicity が AI スタートアップの米国での資金調達急増における PR 戦争での勝利を支援

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„Spynn Publicity“ padeda AI startuoliams laimėti viešųjų ryšių kovą dėl JAV finansavimo bangos

Spynn Publicity ajuda startups de IA a vencer a batalha de RP no aumento de financiamento dos EUA

Spynn Publicity AI Startups US Funding Surge இல் PR Battle வெற்றி பெற உதவுகிறது

Spynn Publicity AI سٹارٹ اپ کو امریکی فنڈنگ میں اضافے میں PR کی جنگ جیتنے میں مدد کرتی ہے۔

Spynn Publicity AI Startups o‘z qo‘yga qo‘yga qo‘yga qo‘yga USA

Spynn Publicity giúp các công ty khởi nghiệp AI giành chiến thắng trong trận chiến PR trong sự gia tăng tài trợ của Mỹ

Table of Links Abstract and 1 Introduction 2 Preliminaries 3. Revisiting Normalization 3.1 Revisiting Euclidean Normalization 3.2 Revisiting Existing RBN 4 Riemannian Normalization on Lie Groups 5 LieBN on the Lie Groups of SPD Manifolds and 5.1 Deformed Lie Groups of SPD Manifolds 5.2 LieBN on SPD Manifolds 6 Experiments 6.1 Experimental Results 7 Conclusions, Acknowledgments, and References APPENDIX CONTENTS A Notations B Basic layes in SPDnet and TSMNet C Statistical Results of Scaling in the LieBN D LieBN as a Natural Generalization of Euclidean BN E Domain-specific Momentum LieBN for EEG Classification F Backpropagation of Matrix Functions G Additional Details and Experiments of LieBN on SPD manifolds H Preliminary Experiments on Rotation Matrices I Proofs of the Lemmas and Theories in the Main Paper A NOTATIONS For better clarity, we summarize all the notations used in this paper in Tab. 6. B BASIC LAYERS IN SPDNET AND TSMNET SPDNet (Huang & Van Gool, 2017) is the most classic SPD neural network. SPDNet mimics the conventional densely connected feedforward network, consisting of three basic building blocks where max(·) is element-wise maximization. BiMap and ReEig mimic transformation and nonlinear activation, while LogEig maps SPD matrices into the tangent space at the identity matrix for classification. C STATISTICAL RESULTS OF SCALING IN THE LIEBN In this section, we will show the effect of our scaling (Eq. (14)) on the population. We will see that while the resulting population variance is generally agnostic, it becomes analytic under certain circumstances, such as SPD manifolds under LEM or LCM. As a result, Eq. (14) can normalize and transform the latent Gaussian distribution. The above lemma implies that when ∆ is a constant, Y also follows a Gaussian distribution. By Prop. C.3, we can directly obtain the following corollary. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors:
(1) Ziheng Chen, University of Trento;
(2) Yue Song, University of Trento and a Corresponding author;
(3) Yunmei Liu, University of Louisville;
(4) Nicu Sebe, University of Trento. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries 2 Preliminaries 3. Revisiting Normalization 3.1 Revisiting Euclidean Normalization 3.1 Revisiting Euclidean Normalization 3.2 Revisiting Existing RBN 3.2 Revisiting Existing RBN 4 Riemannian Normalization on Lie Groups 4 Riemannian Normalization on Lie Groups 5 LieBN on the Lie Groups of SPD Manifolds and 5.1 Deformed Lie Groups of SPD Manifolds 5 LieBN on the Lie Groups of SPD Manifolds and 5.1 Deformed Lie Groups of SPD Manifolds 5.2 LieBN on SPD Manifolds 5.2 LieBN on SPD Manifolds 6 Experiments 6 Experiments 6.1 Experimental Results 6.1 Experimental Results 7 Conclusions, Acknowledgments, and References 7 Conclusions, Acknowledgments, and References APPENDIX CONTENTS APPENDIX CONTENTS A Notations A Notations B Basic layes in SPDnet and TSMNet B Basic layes in SPDnet and TSMNet C Statistical Results of Scaling in the LieBN C Statistical Results of Scaling in the LieBN D LieBN as a Natural Generalization of Euclidean BN D LieBN as a Natural Generalization of Euclidean BN E Domain-specific Momentum LieBN for EEG Classification E Domain-specific Momentum LieBN for EEG Classification F Backpropagation of Matrix Functions F Backpropagation of Matrix Functions G Additional Details and Experiments of LieBN on SPD manifolds G Additional Details and Experiments of LieBN on SPD manifolds H Preliminary Experiments on Rotation Matrices H Preliminary Experiments on Rotation Matrices I Proofs of the Lemmas and Theories in the Main Paper I Proofs of the Lemmas and Theories in the Main Paper A NOTATIONS For better clarity, we summarize all the notations used in this paper in Tab. 6. B BASIC LAYERS IN SPDNET AND TSMNET SPDNet (Huang & Van Gool, 2017) is the most classic SPD neural network. SPDNet mimics the conventional densely connected feedforward network, consisting of three basic building blocks where max(·) is element-wise maximization. BiMap and ReEig mimic transformation and nonlinear activation, while LogEig maps SPD matrices into the tangent space at the identity matrix for classification. C STATISTICAL RESULTS OF SCALING IN THE LIEBN In this section, we will show the effect of our scaling (Eq. (14)) on the population. We will see that while the resulting population variance is generally agnostic, it becomes analytic under certain circumstances, such as SPD manifolds under LEM or LCM. As a result, Eq. (14) can normalize and transform the latent Gaussian distribution. The above lemma implies that when ∆ is a constant, Y also follows a Gaussian distribution. By Prop. C.3, we can directly obtain the following corollary. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) Ziheng Chen, University of Trento; (2) Yue Song, University of Trento and a Corresponding author; (3) Yunmei Liu, University of Louisville; (4) Nicu Sebe, University of Trento. Authors: Authors: (1) Ziheng Chen, University of Trento; (2) Yue Song, University of Trento and a Corresponding author; (3) Yunmei Liu, University of Louisville; (4) Nicu Sebe, University of Trento.

Basic Layers In SPDNET, TSMNET, and Statistical Results of Scaling in the LieBN

Apple CEO Tim Cook posted a message on X (formerly Twitter) on February 13, 2025, hinting at "the newest member of the family." The announcement preceded the official introduction of the iPhone 16e on February 19. Priced at $599, the device includes a 6.1-inch OLED display and the A18 chipset, making it a more affordable option in Apple's lineup. Public relations efforts surrounding the launch have drawn attention. Spynn Publicity, a high-tech PR agency specializing in media campaigns, has outlined a strategy that could influence how tech companies present new products to the public. Expanding Media Coverage Apple’s social media teaser generated initial interest, but Spynn Publicity suggests a long-term media plan to keep attention on the device. The agency's recommendations focus on distributing information through multiple channels to maintain visibility across different audiences. "A single post on social media can generate buzz, but keeping people engaged requires a structured effort. Our AI-driven system helps place in-depth reviews, expert insights, and feature stories in top publications, keeping the conversation active beyond the initial launch," says Matteo Ferretti, CEO of Spynn Publicity. The agency’s plan includes securing features in technology publications, lifestyle outlets, and business-focused media. Each would focus on different aspects of the iPhone 16e, including design, affordability, and Apple’s overall business strategy. One of the main goals is to get featured in Forbes and other high-profile publications to make sure the iPhone 16e receives authoritative coverage. Exclusive interviews and expert commentary are also part of the plan to sustain public interest. To build credibility, Spynn Publicity prioritizes coverage in widely recognized media outlets. It offers a money-back guarantee for placements, making sure coverage reaches intended audiences through established sources. Using Data for Precise Messaging Consumers have more choices than ever when buying a smartphone. Well-placed messaging can make a difference. Spynn Publicity believes data analytics can sharpen outreach efforts, allowing brands to focus on what customers actually care about. "Public relations is also about understanding what people want to hear. Analyzing social media sentiment, search trends, and customer feedback allows us to highlight the factors that matter most. If Apple applied this method, it could present the iPhone 16e as the right choice for mid-range buyers by addressing specific needs," Ferretti adds. This method also helps in choosing the right media outlets and influencers. Instead of relying on broad promotional efforts, Spynn Publicity suggests working with reviewers and content creators who have built trust with potential buyers. Exclusive reviews and comparisons with competing devices in respected publications would give consumers a clearer understanding of the iPhone 16e’s features. Marketing a Mid-Range Device While Maintaining Brand Identity The iPhone 16e includes technology typically found in higher-end models, which makes it challenging to present it as both affordable and high-quality. Spynn Publicity proposes a strategy that emphasizes accessibility without reducing the brand’s perceived value. "Advanced features shouldn’t only be available in the most expensive models. The A18 chip, Apple Intelligence, and a 48MP Fusion camera system provide high-performance capabilities to a wider audience. Our plan would present the iPhone 16e as an example of Apple’s ability to make advanced technology available to more people," Ferretti notes. To support this messaging, Spynn Publicity recommends media placements that focus on real-world applications. Collaborations with photographers could demonstrate camera performance, while productivity experts could explain workplace functionality. Tech influencers could also simplify complex features like Apple Intelligence to appeal to a broader audience. Some analysts caution that too much media coverage could create unrealistic expectations. "Apple already has a strong presence. If PR efforts build too much anticipation, people may be disappointed if the product does not fully align with consumer expectations," says an anonymous industry analyst. Ferretti maintains that transparency is a priority. "We’re delivering accurate and well-rounded information. A well-planned strategy provides multiple perspectives, helping consumers make informed decisions," he says. Long-Term Impact of PR Strategies Once the iPhone 16e reaches consumers, different PR methods will be tested. While Spynn Publicity is not directly involved in the launch, its proposed strategy reflects broader adjustments in how brands handle product announcements as media habits and consumer behavior shift. With carefully chosen media placements, data-backed messaging, and a structured narrative, PR firms aim to keep product interest alive beyond the initial announcement. Whether Apple applies similar methods in future campaigns remains to be seen, but discussions around public relations strategies continue. For companies seeking similar attention, choosing to hire a publicist can provide the expertise needed to sustain momentum and generate long-term interest. This article is published under HackerNoon’s Business Blogging program. Apple CEO Tim Cook posted a message on X (formerly Twitter) on February 13, 2025, hinting at "the newest member of the family." The announcement preceded the official introduction of the iPhone 16e on February 19. Priced at $599, the device includes a 6.1-inch OLED display and the A18 chipset, making it a more affordable option in Apple's lineup. Public relations efforts surrounding the launch have drawn attention. Spynn Publicity, a high-tech PR agency specializing in media campaigns, has outlined a strategy that could influence how tech companies present new products to the public. high-tech PR agency high-tech PR agency Expanding Media Coverage Apple’s social media teaser generated initial interest, but Spynn Publicity suggests a long-term media plan to keep attention on the device. The agency's recommendations focus on distributing information through multiple channels to maintain visibility across different audiences. "A single post on social media can generate buzz, but keeping people engaged requires a structured effort. Our AI-driven system helps place in-depth reviews, expert insights, and feature stories in top publications, keeping the conversation active beyond the initial launch," says Matteo Ferretti, CEO of Spynn Publicity. "A single post on social media can generate buzz, but keeping people engaged requires a structured effort. Our AI-driven system helps place in-depth reviews, expert insights, and feature stories in top publications, keeping the conversation active beyond the initial launch," The agency’s plan includes securing features in technology publications, lifestyle outlets, and business-focused media. Each would focus on different aspects of the iPhone 16e, including design, affordability, and Apple’s overall business strategy. One of the main goals is to get featured in Forbes and other high-profile publications to make sure the iPhone 16e receives authoritative coverage. Exclusive interviews and expert commentary are also part of the plan to sustain public interest. get featured in Forbes get featured in Forbes To build credibility, Spynn Publicity prioritizes coverage in widely recognized media outlets. It offers a money-back guarantee for placements, making sure coverage reaches intended audiences through established sources. Using Data for Precise Messaging Using Data for Precise Messaging Consumers have more choices than ever when buying a smartphone. Well-placed messaging can make a difference. Spynn Publicity believes data analytics can sharpen outreach efforts, allowing brands to focus on what customers actually care about. "Public relations is also about understanding what people want to hear. Analyzing social media sentiment, search trends, and customer feedback allows us to highlight the factors that matter most. If Apple applied this method, it could present the iPhone 16e as the right choice for mid-range buyers by addressing specific needs," Ferretti adds. "Public relations is also about understanding what people want to hear. Analyzing social media sentiment, search trends, and customer feedback allows us to highlight the factors that matter most. If Apple applied this method, it could present the iPhone 16e as the right choice for mid-range buyers by addressing specific needs," This method also helps in choosing the right media outlets and influencers. Instead of relying on broad promotional efforts, Spynn Publicity suggests working with reviewers and content creators who have built trust with potential buyers. Exclusive reviews and comparisons with competing devices in respected publications would give consumers a clearer understanding of the iPhone 16e’s features. Marketing a Mid-Range Device While Maintaining Brand Identity The iPhone 16e includes technology typically found in higher-end models, which makes it challenging to present it as both affordable and high-quality. Spynn Publicity proposes a strategy that emphasizes accessibility without reducing the brand’s perceived value. "Advanced features shouldn’t only be available in the most expensive models. The A18 chip, Apple Intelligence, and a 48MP Fusion camera system provide high-performance capabilities to a wider audience. Our plan would present the iPhone 16e as an example of Apple’s ability to make advanced technology available to more people," Ferretti notes. "Advanced features shouldn’t only be available in the most expensive models. The A18 chip, Apple Intelligence, and a 48MP Fusion camera system provide high-performance capabilities to a wider audience. Our plan would present the iPhone 16e as an example of Apple’s ability to make advanced technology available to more people," To support this messaging, Spynn Publicity recommends media placements that focus on real-world applications. Collaborations with photographers could demonstrate camera performance, while productivity experts could explain workplace functionality. Tech influencers could also simplify complex features like Apple Intelligence to appeal to a broader audience. Some analysts caution that too much media coverage could create unrealistic expectations. "Apple already has a strong presence. If PR efforts build too much anticipation, people may be disappointed if the product does not fully align with consumer expectations," says an anonymous industry analyst. "Apple already has a strong presence. If PR efforts build too much anticipation, people may be disappointed if the product does not fully align with consumer expectations," Ferretti maintains that transparency is a priority. "We’re delivering accurate and well-rounded information. A well-planned strategy provides multiple perspectives, helping consumers make informed decisions," he says. "We’re delivering accurate and well-rounded information. A well-planned strategy provides multiple perspectives, helping consumers make informed decisions," Long-Term Impact of PR Strategies Long-Term Impact of PR Strategies Once the iPhone 16e reaches consumers, different PR methods will be tested. While Spynn Publicity is not directly involved in the launch, its proposed strategy reflects broader adjustments in how brands handle product announcements as media habits and consumer behavior shift. With carefully chosen media placements, data-backed messaging, and a structured narrative, PR firms aim to keep product interest alive beyond the initial announcement. Whether Apple applies similar methods in future campaigns remains to be seen, but discussions around public relations strategies continue. For companies seeking similar attention, choosing to hire a publicist can provide the expertise needed to sustain momentum and generate long-term interest. hire a publicist hire a publicist This article is published under HackerNoon’s Business Blogging program. This article is published under HackerNoon’s Business Blogging program. This article is published under HackerNoon’s Business Blogging program. This article is published under HackerNoon’s Business Blogging program. Business Blogging

Spynn Publicity's Strategy to Make Apple's iPhone 16e Launch a PR Success

Spynn Publicity ukax Estrategia ukawa, Apple ukan iPhone 16e ukar PR ukan uñstayañataki

Spynn Publicity-nin Apple-ın iPhone 16e-ni PR uğuruna çevirmək üçün strategiyası

Стратегията на Spynn Publicity да направи iPhone 16e на Apple лансиран PR успех

Spynn Publicitys strategi for at få Apples iPhone 16e til at lancere en PR-succes

Στρατηγική της Spynn Publicity για να κάνει το iPhone 16e της Apple να ξεκινήσει μια επιτυχία δημοσίων σχέσεων

La estrategia de Spynn Publicity para que el lanzamiento del iPhone 16e de Apple sea un éxito de relaciones públicas

La stratégie de Spynn Publicity pour faire du lancement de l'iPhone 16e d'Apple un succès en matière de relations publiques

AppleのiPhone 16e発売をPR成功に導くSpynn Publicityの戦略

Apple හි iPhone 16e මහජන සම්බන්ධතා සාර්ථක කර ගැනීම සඳහා Spynn ප්‍රචාරණයේ උපාය මාර්ගය

Стратегија компаније Спинн Публицити да учини да Аппле-ов иПхоне 16е буде успешан са јавношћу

“Spynn Publicity” -iň “Apple” -iň “iPhone 16e” -ni PR üstünlik gazanmagy üçin strategiýasy

Chiến lược của Spynn Publicity để ra mắt iPhone 16e của Apple thành công về mặt quan hệ công chúng

From Data Silos to Intelligent Systems: RAG and Knowledge Graph Synergy

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works 5 DISCUSSIONS 5.1 SYNERGY BETWEEN CREATION & COMPREHENSION? To elucidate the synergy between multimodal creation and comprehension, we make the comparison among three methods with DREAMLLM architecture, each utilizing identical training data yet differing in their learning objectives: a) the Creation-only baseline, focused solely on text/document-conditional image synthesis; b) the Comprehension-only baseline, dedicated to word generation exclusively; c) the Joint-learning method, which is the default setting of DREAMLLM learning both image and language modeling. Qualitative Analysis In Fig. 4, we compare answers to some examplar VQA tasks from comprehension-only and joint learning modules, respectively. It can be seen that: i) The joint-learning method exhibits superior multimodal comprehension, particularly in identifying subject relationships and attributes like object size. ii) In multimodal comprehension scenarios involving multiple image inputs, the joint-learning approach demonstrates enhanced precision. This improved performance is a natural outcome of I-GPT pretraining, allowing better modeling of multimodal correlations in various interleaved documents Multimodal In-Context Generation Multimodal in-context generation is a critical emerging capability for MLLMs (Bommasani et al., 2021; Alayrac et al., 2022). While significant strides have been made in in-context visual question answering, in-context image synthesis remains relatively lacking in exploration. The multimodal context-conditional image synthesis capabilities of DREAMLLM, as demonstrated in Fig. 5, offer promising insights into this domain. Tasks such as in-context image edition, subject-driven image generation, and compositional generation, however, pose significant challenges in a zero-shot setting, particularly without downstream fine-tuning as in DreamBooth (Ruiz et al., 2023) or attention modification techniques as in Prompt2Prompt (Hertz et al., 2023). Despite these hurdles, Fig. 5 illustrates DREAMLLM’s ability to generate images conditioned on the provided image context. This capability suggests promising potential for DREAMLLM in maintaining subject, identity, and semantic context, thereby paving a new way for resolving these complex tasks. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works 5 DISCUSSIONS 5.1 SYNERGY BETWEEN CREATION & COMPREHENSION? To elucidate the synergy between multimodal creation and comprehension, we make the comparison among three methods with DREAMLLM architecture, each utilizing identical training data yet differing in their learning objectives: a) the Creation-only baseline, focused solely on text/document-conditional image synthesis; b) the Comprehension-only baseline, dedicated to word generation exclusively; c) the Joint-learning method, which is the default setting of DREAMLLM learning both image and language modeling. Qualitative Analysis In Fig. 4, we compare answers to some examplar VQA tasks from comprehension-only and joint learning modules, respectively. It can be seen that: i) The joint-learning method exhibits superior multimodal comprehension, particularly in identifying subject relationships and attributes like object size. ii) In multimodal comprehension scenarios involving multiple image inputs, the joint-learning approach demonstrates enhanced precision. This improved performance is a natural outcome of I-GPT pretraining, allowing better modeling of multimodal correlations in various interleaved documents Qualitative Analysis Multimodal In-Context Generation Multimodal in-context generation is a critical emerging capability for MLLMs (Bommasani et al., 2021; Alayrac et al., 2022). While significant strides have been made in in-context visual question answering, in-context image synthesis remains relatively lacking in exploration. The multimodal context-conditional image synthesis capabilities of DREAMLLM, as demonstrated in Fig. 5, offer promising insights into this domain. Tasks such as in-context image edition, subject-driven image generation, and compositional generation, however, pose significant challenges in a zero-shot setting, particularly without downstream fine-tuning as in DreamBooth (Ruiz et al., 2023) or attention modification techniques as in Prompt2Prompt (Hertz et al., 2023). Despite these hurdles, Fig. 5 illustrates DREAMLLM’s ability to generate images conditioned on the provided image context. This capability suggests promising potential for DREAMLLM in maintaining subject, identity, and semantic context, thereby paving a new way for resolving these complex tasks. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

What Is the Synergy Between Creation & Comprehension? What You Need to Know

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works 4.2 TEXT-CONDITIONAL IMAGE SYNTHESIS Text-conditional image synthesis is one of the most commonly used techniques for creative content generation that follows human’s fabulous imaginations through free-form languages. We assess text-conditional image synthesis on the MS-COCO validation set (Lin et al., 2014) and LN-COCO, the COCO subset of Localized Narratives (PontTuset et al., 2020), following prior works (Xu et al., 2018; Yu et al., 2022b). The MS-COCO dataset primarily contains high-level image abstractions with shorter captions, whereas LN-COCO provides more comprehensive image descriptions (Yu et al., 2022b). DREAMLLM samples 8 images per text prompt on MSCOCO by CLIP score ranking, following previous works (Ramesh et al., 2022). On LN-COCO, DREAMLLM samples one image per prompt without CLIP ranking since the text is too long and exceeds the CLIP length limit. Note that Parti samples 16 images per prompt with CoCa (Yu et al., 2022a). Our evaluation metric is the zero-shot Fréchet Inception Distance (FID) (Heusel et al., 2017), the results of which are presented in Table 2. We note three key observations: i) Our DREAMLLM shows a significant FID improvement over the Stable Diffusion baseline after stage-I alignment, reducing the score by 3.67 and 11.83 on MS-COCO and LN-COCO respectively. Further, FID improvements of 3.97 and 13.73 are achieved after pretraining and supervised fine-tuning. The substantial improvement on LN-COCO underscores DREAMLLM’s superior capability in processing long-context information. ii) When compared to prior specialist models, DREAMLLM delivers competitive results based on the SD image decoder. iii) DREAMLLM consistently outperforms concurrent MLLMs-based image synthesis methods. For instance, DREAMLLM-7B surpasses Emu-13B by a significant 3.20 FID on MS-COCO. See qualitative results on text-to-image synthesis in Fig. 10 and Fig. 11 in Appendix B. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works 4.2 TEXT-CONDITIONAL IMAGE SYNTHESIS Text-conditional image synthesis is one of the most commonly used techniques for creative content generation that follows human’s fabulous imaginations through free-form languages. We assess text-conditional image synthesis on the MS-COCO validation set (Lin et al., 2014) and LN-COCO, the COCO subset of Localized Narratives (PontTuset et al., 2020), following prior works (Xu et al., 2018; Yu et al., 2022b). The MS-COCO dataset primarily contains high-level image abstractions with shorter captions, whereas LN-COCO provides more comprehensive image descriptions (Yu et al., 2022b). DREAMLLM samples 8 images per text prompt on MSCOCO by CLIP score ranking, following previous works (Ramesh et al., 2022). On LN-COCO, DREAMLLM samples one image per prompt without CLIP ranking since the text is too long and exceeds the CLIP length limit. Note that Parti samples 16 images per prompt with CoCa (Yu et al., 2022a). Our evaluation metric is the zero-shot Fréchet Inception Distance (FID) (Heusel et al., 2017), the results of which are presented in Table 2. We note three key observations: i) Our DREAMLLM shows a significant FID improvement over the Stable Diffusion baseline after stage-I alignment, reducing the score by 3.67 and 11.83 on MS-COCO and LN-COCO respectively. Further, FID improvements of 3.97 and 13.73 are achieved after pretraining and supervised fine-tuning. The substantial improvement on LN-COCO underscores DREAMLLM’s superior capability in processing long-context information. ii) When compared to prior specialist models, DREAMLLM delivers competitive results based on the SD image decoder. iii) DREAMLLM consistently outperforms concurrent MLLMs-based image synthesis methods. For instance, DREAMLLM-7B surpasses Emu-13B by a significant 3.20 FID on MS-COCO. See qualitative results on text-to-image synthesis in Fig. 10 and Fig. 11 in Appendix B. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

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