Cybersecurity professionals from across Northern Ireland gathered at Titanic Belfast on February 13 for Check Point Software's second Cyber Leader Summit, a hig

Cybersecurity Leaders Convene in Belfast to Tackle Evolving Digital Threats - IT Security Guru

The platform automates critical compliance processes, making it easier for organizations to stay aligned with evolving standards such as the NIST Cybersecurity Framework. The platform uses AI to help organizations efficiently manage risk while ensuring their security governance remains up to ...

Bridging the Cyber Security Talent Gap with AI and Experts - Check Point Blog

Thailand is facing a cybersecurity crisis, with cyberattacks in the country occurring 70% more frequently than the global average, according to a report by Check Point Software Technologies.

Thailand grapples with a cybersecurity crisis as attacks soar 70% higher than the global average - Thailand Business News

In a parallel analysis published by Trend Micro, the cybersecurity company said it observed the updated Shadowpad malware being used to deploy the NailaoLocker ransomware after exploiting weak passwords and bypassing multi-factor authentication.

China-Linked Attackers Exploit Check Point Flaw to Deploy ShadowPad and Ransomware

HAMPTON, Va., Feb. 19, 2025 /PRNewswire/ -- Old Point Financial Corporation declared a quarterly cash dividend of $0.14 per share on its common stock to be paid on March 28, 2025 to shareholders of record as of March 5, 2025.

Old Point Financial Corporation Announces Quarterly Dividend

Armis, Check Point, CrowdStrike, Deloitte and WWT adopt Nvidia's cybersecurity AI platform, for energy, utilities and manufacturing facilities

The Tech Leaders Adopting Nvidia’s Cybersecurity AI | Technology Magazine

Check Point Software Technologies Ltd. (NASDAQ:CHKP) provides global IT security solutions, including cloud, network, mobile, endpoint, and IoT protections, along with security architecture, gateways, and support services.

Check Point Software (CHKP) – Strategic Alliance with Wiz to Strengthen Cloud Security - Insider Monkey

The strategic partnership aims to create the industry's most comprehensive cloud protection solution.

Check Point and Wiz join forces to challenge Palo Alto in cloud security | Ctech

Point

point

Can Real-Time Cybersecurity Work in Web3? The blockchain ecosystem has been defined by its ability to decentralize power. But in doing so, it’s also opened itself to a growing volume of exploits, smart contract bugs, and wallet-level attacks. This raises a central question: Can real-time cybersecurity protocols, like those used in Web2 infrastructure, be implemented in decentralized systems? Fuse, a layer 2 blockchain focused on crypto payments, believes the answer lies in collaboration. On April 15, it announced a partnership with Check Point Software to deploy the first advanced blockchain firewall designed to prevent threats in real time. The move signals a shift from reactive security to proactive defense. Fuse Check Point Software Rather than detecting malicious transactions after they occur, the partnership aims to stop them before execution. According to Fuse, the approach draws on Check Point’s three decades of experience in threat intelligence and firewall technology—now applied to a blockchain setting. What Makes This Firewall Different? Traditional blockchain security measures, such as smart contract audits and static code analysis, occur post-deployment. These are essential but insufficient, especially as attacks become more dynamic. The proposed solution is a dedicated AI-powered firewall that functions at the infrastructure level, integrated across the Fuse network. Mark Smargon, CEO of Fuse, explained the intent behind the move: “Prevention is always better than a cure, particularly with crypto networks that serve as the backbone for global payments. With Check Point providing a dedicated security layer, we’re confident that we can not only deter hackers, who are becoming increasingly sophisticated, but pioneer a cybersecurity model that will become the gold standard for protecting web3 protocols.” “Prevention is always better than a cure, particularly with crypto networks that serve as the backbone for global payments. With Check Point providing a dedicated security layer, we’re confident that we can not only deter hackers, who are becoming increasingly sophisticated, but pioneer a cybersecurity model that will become the gold standard for protecting web3 protocols.” “Prevention is always better than a cure, particularly with crypto networks that serve as the backbone for global payments. With Check Point providing a dedicated security layer, we’re confident that we can not only deter hackers, who are becoming increasingly sophisticated, but pioneer a cybersecurity model that will become the gold standard for protecting web3 protocols.” The security layer will leverage Check Point’s proprietary threat prevention models, which are trained on historical and real-time data to block transactions flagged as malicious. According to Dan Danay, Head of Web 3.0 Security at Check Point, this type of security architecture mirrors the evolution from static antivirus tools to real-time cloud-based monitoring in the traditional web: Check Point’s “Just as robust cybersecurity powered the rise of Web 2.0, real-time prevention will be key to Web3’s mainstream adoption.” “Just as robust cybersecurity powered the rise of Web 2.0, real-time prevention will be key to Web3’s mainstream adoption.” “Just as robust cybersecurity powered the rise of Web 2.0, real-time prevention will be key to Web3’s mainstream adoption.” How the Threat Prevention Layer Works Rather than functioning as a simple list of known bad actors, the security layer integrates with Fuse's transaction pipeline to actively inspect intent. It leverages AI-powered threat engines and real-time data feeds from Check Point’s global cyber intelligence network. Fuse's approach does not stop at smart contracts. Wallet interactions, dApp behavior, and node communications will also be subject to live monitoring. According to the teams, this breadth of coverage is key for reducing attack surfaces and enabling developer trust. The firewall will not act as a gatekeeper to decentralization but as an internal risk assessment tool. Developers, node operators, and users will retain autonomy but can opt into an additional layer of security baked directly into the protocol. Ember Nodes and the Future of Fuse’s Network The announcement comes shortly after the launch of Fuse’s Ember Nodes, a governance and validation system backed by venture participants such as Collider Ventures, TRGC, and Blockchain Founders Fund. These nodes allow users to acquire ownership and participate in decision-making, a process that could be enhanced by real-time threat analytics. With thousands of active users and a growing number of stablecoin transactions processed daily, Fuse has chosen to build security into the core of its payment-focused infrastructure. This is a notable departure from retrofitting security tools after scaling. The hope is that by implementing this level of scrutiny now, Fuse can avoid the pattern observed in other networks—where vulnerabilities are only addressed after they are exploited. Broader Implications for Web3 Infrastructure Security remains one of the largest friction points for Web3 adoption. Protocols like Fuse that prioritize infrastructure-level security may begin to redefine baseline expectations for operational safety across chains. While Fuse is currently focused on B2B and B2C payments, the model it’s implementing with Check Point could apply across other verticals: gaming, decentralized finance, and supply chain platforms. Whether the industry will follow remains to be seen. But with regulatory scrutiny increasing and users demanding stronger protections, moving from detection to prevention might become not just a differentiator—but a requirement. Final Thoughts In my view, this partnership sets a precedent for what blockchain networks should begin to consider non-negotiable. Security shouldn’t be a feature, it should be infrastructure. Fuse's integration with Check Point isn’t about marketing a firewall to web3 audience, it’s about acknowledging that decentralization without defense is unsustainable in the long term. If other L2s or payment blockchains follow suit, we may finally see a turning point where security architecture in Web3 begins to match the financial value these systems are expected to move. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO business blogging program business blogging program

Fuse and Check Point Are Trying to Build the First Blockchain Firewall—Here’s What That Means

Το Fuse και το Check Point προσπαθούν να χτίσουν το πρώτο τείχος προστασίας Blockchain - Εδώ είναι τι σημαίνει αυτό

Fuse y Check Point están tratando de construir el primer firewall de Blockchain: esto es lo que significa

Fuse ja Check Point yrittävät rakentaa ensimmäisen Blockchain-palomuurin - tässä on mitä se tarkoittaa

FuseとCheck Pointは、最初のブロックチェーンファイアウォールを構築しようとしています。

Fuse와 Check Point은 첫 번째 Blockchain 방화벽을 구축하려고합니다.

Fuse и Check Point се обидуваат да изградат прв блокчејн заштитен ѕид – еве што значи тоа

Fuse болон Check Point нь анхны Blockchain Firewall бий болгохын тулд туршиж байна - Энд энэ нь гэсэн үг юм

Fuse и Check Point пытаются построить первую защитную стену Blockchain — вот что это значит

Фусе и Цхецк Поинт покушавају да изграде први Блоцкцхаин Фиревалл - Ево шта то значи

Fuse اور Check Point نے پہلی Blockchain فائیو ویئر ویئر کو تعمیر کرنے کی کوشش کی ہے - یہاں یہ کیا مطلب ہے

Fuse và Check Point đang cố gắng xây dựng tường lửa Blockchain đầu tiên - đây là ý nghĩa của nó

Mastering Exposure Points for Accurate Mobile A/B Testing

Currently in 2025, Artificial Intelligence (AI) is advancing rapidly, and it looks like this year could very well be a landmark year. While AI has already gotten everyone impressed by making significant strides in deep learning, natural language processing (NLP), and generative AI (case in point the Ghibli-isation of social media recently), two upcoming innovations seem extremely promising when it comes to truly push the boundaries. They are Multimodal AI and Self-Supervised Learning (SSL). These advancements are going to end up making AI smarter, more intuitive, and capable of mirroring human learning patterns like never before. The Rise of Multimodal AI: A shift towards human-like understanding For years, AI has operated and functioned largely within different silos. FYI silos are isolated datasets and systems that do not integrate well or at all with each other. This leads to some models excelling at processing text, while others specializing in images or audio, but few AI models have emerged that can integrate all these separate functions seamlessly. Now, we finally have a breakthrough in this field, in the form of multimodal AI, that aims at breaking these barriers, and enables AI to interpret and synthesize information across various formats at the same time. Why Multimodal AI matters Can you imagine an AI assistant that not only comprehends your words but also picks up on subtle and emotive cues such as your facial expressions and tone of your voice to get a better read on your emotions. Or a search engine that allows you describe a scene in words and instantly locates relevant images and videos from the vast expanse of internet search engines, thereby saving you time by combing through hundreds of online sources in a matter of milliseconds. Multimodal AI is in the process of bringing us closer to a world where AI doesn’t just process data, but rather it comprehends the context of within which the data exists, helping make interactions more natural, intelligent and eventually useful. How is multimodal AI accelerating in 2025? How is multimodal AI accelerating in 2025? Multimodal AI is no longer just a theoretical concept anymore. As of right now, it’s in the process of being integrated into real-world tech through various applications. But what’s enabling its rapid growth in 2025? Next up we shall take a quick look at a few of the technological advancements that are responsible for this heightened acceleration of multimodal AI. First up, we have the next-gen AI chips that are the so advanced in their in hardware design that they are making it possible to process multimodal inputs in real time, which in turn helps reduce lag and increase efficiency. Then, we have the rich multimodal datasets. The access to large-scale datasets containing text, images, and audio is allowing AI models to develop deeper contextual understanding. Moreover, cross-modal learning innovations is allowing AI to become better at transferring knowledge across different formats, while making it more adaptable and reducing its reliance on extensive labelled data. Finally, innovations like Edge AI integration is helping create smarter AI assistants on mobile devices, wearables, and IoT systems. Soon this integrated AI will be capable of processing multiple data types locally without having to rely on any cloud-based storage. What are the ways in which multimodal AI can be applied in real life? What are the ways in which multimodal AI can be applied in real life? Innovations that result in scientific advancement are doubly celebrated in society when they have positive real- world applications. Here are some of the ways in which multimodal AI should be able to help. Healthcare Diagnostics: Soon, AI-powered systems will be able to analyze the various elements of the prescription procedure like medical scans, doctor’s notes, and genetic data comprehensively, and that will lead to more accurate diagnoses and time sensitive treatment plans.
Self-Driving Cars: Autonomous vehicles will soon be able to merge visual data from cameras, audio cues from the environment, and radar signals and assist with navigating complicated road conditions safely.
Education & Accessibility: AI tutors will soon be able to assist teachers in designing lesson plans based on both spoken and written inputs. Meanwhile, real-time sign language translation and integration of our sensory outputs will improve accessibility for the hearing-impaired and other differently abled peoples.
E-Commerce & Retail: Image based product searches will soon become the new normal. Multimodal AI will make shopping more intuitive and personalized based on the customer’s shopping preference history and will also be able to bounce off appropriate suggestions on the basis of specific prompts.
Security & Surveillance: Future AI systems will be able to seamlessly integrate key security features like video footage and audio clips, along with contextual text-based alerts to improve threat identification and response during emergencies. Healthcare Diagnostics: Soon, AI-powered systems will be able to analyze the various elements of the prescription procedure like medical scans, doctor’s notes, and genetic data comprehensively, and that will lead to more accurate diagnoses and time sensitive treatment plans. Self-Driving Cars: Autonomous vehicles will soon be able to merge visual data from cameras, audio cues from the environment, and radar signals and assist with navigating complicated road conditions safely. Education & Accessibility: AI tutors will soon be able to assist teachers in designing lesson plans based on both spoken and written inputs. Meanwhile, real-time sign language translation and integration of our sensory outputs will improve accessibility for the hearing-impaired and other differently abled peoples. E-Commerce & Retail: Image based product searches will soon become the new normal. Multimodal AI will make shopping more intuitive and personalized based on the customer’s shopping preference history and will also be able to bounce off appropriate suggestions on the basis of specific prompts. Security & Surveillance: Future AI systems will be able to seamlessly integrate key security features like video footage and audio clips, along with contextual text-based alerts to improve threat identification and response during emergencies. As 2025 comes to a close, multimodal AI won’t just be a cool new experimental feature, it will become one of the most important components in all AI-driven products. This will make digital experiences like browsing, shopping, life planning, and self-learning more seamless and natural than ever before. Self-Supervised Learning: The what, the why and the how of it all. Self-Supervised Learning: The what, the why and the how of it all. One of the biggest setbacks faced by AI today is its dependence on large labelled datasets. Self-Supervised Learning (SSL) is changing this arena by training AI to learn from raw, unlabelled data. This process is intended to mirror humans in the phase where they explore and understand the world. How does SSL work? How does SSL work? SSL works by enabling AI in uncovering patterns and relationships within data without relying on human interventions. Consider the learning processes and patterns of children, they don’t need someone to label every object they see. Their brains can be conditioned to recognize similarities, differences, and structures over time. AI is now learning in a similar way utilising different methods. First we have contrastive learning, this learning process involves teaching AI to differentiate between similar and dissimilar data points, improving contextual understanding. Then we have masked autoencoders whereby the AI is enabled to predict missing elements in a dataset, much like how we fill in gaps in a partially obscured image or sentence. Finally we have pretext tasks, through which AI models are trained so that they can solve tasks like predicting the next frame in a video or reconstructing scrambled text, leading to more robust representations. Why we think SSL will dominate in 2025? Why we think SSL will dominate in 2025? Self-supervised learning is rapidly moving from the research labs to into real-world applications. Some of the factors that explains why 2025 will be the year SSL takes centre stage are elimination of labelling bottlenecks. AI will soon be able to learn from vast amounts of unstructured data without requiring any human intervention. That should ideally accelerate development. Then comes the improved adaptability whereby SSL-powered AI will require minimal retraining when applied to new tasks, helping it become highly versatile.Moreover, breakthroughs in foundation models like large-scale models trained with SSL will eventually outperform traditional supervised learning in various domains, including language and vision. Soon, AI assistants will adapt to individual users more efficiently, learning from their behaviour rather than relying on predefined rules, thereby allowing better customization options. Transformative Impact of SSL Transformative Impact of SSL In the medical research and drug discovery industries AI will soon be able to analyze scientific literature, clinical trials, and genetic data without the need for extensive manual curation. This will help with accelerating medical breakthroughs. In the finance & fraud detection sector, SSL will enhance fraud detection systems by identifying subtle patterns in transaction data, reducing false positives while improving real-time fraud prevention. In the content generation and creativity sector, AI-powered music, art, and writing tools will become increasingly more sophisticated. Soon they will be learning creative styles without requiring labelled datasets. In autonomous systems like robotics and drones AI will be able to  navigate and adapt to their surroundings more effectively, while learning from unlabelled sensor data. In the cybersecurity industry, AI-driven security systems will detect emerging cyber threats without relying on predefined attack patterns, improving resilience against evolving threats. This will be ideal for most major companies, nations and banking systems everywhere. Conclusion Conclusion It looks like 2025 will prove be a transformative year for AI, as Multimodal AI and Self-Supervised Learning integrates into evolving AI systems. These developments will inevitably push AI towards more intuitive, efficient, and human-like intelligence. This newfound form of thinking and intelligence will impact industries ranging from healthcare to finance, security, and beyond. As these technologies continue to evolve, we can expect AI to continue becoming an even more integral part of our lives. Imagine a ChatGPT that can think in videos, generate medical surgery procedures and cardio-vascular health plans, detect evolving viruses, and generate rich historical and cultural content about your upcoming vacation spot. That is the world that these innovations are leading us into. Disclaimer: The opinions expressed here are my own and do not necessarily reflect the views of CVS Health or its affiliates. Disclaimer: The opinions expressed here are my own and do not necessarily reflect the views of CVS Health or its affiliates.

2025 Might Be AI’s Tipping Point

Turning AI Into Better Thinkers With Pointer-Based Memory

Authors:
(1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia;
(2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia;
(3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia;
(4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia;
(5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. Table of Links Abstract & Introduction Methods Methods Part 2 Experimental Results Experimental Results Part 2 Related Works, Discussion, & References Appendix A, B, & C Appendix D 3.2 Dyck Language Recognition 3.3 Compositional Learning SCAN In this task , one needs to map an input sentence into an output sequence of commands [Lake and Baroni, 2018]. The sequences are compositional, consisting of reusable parts. For example, in one case, “jump twice” should be mapped to “JUMP JUMP” and in another, “walk twice” becomes “WALK WALK”. We focus on the “length split” datasets where the training sequences are shorter than the test ones with 11 length modes L = 22, 24, .., 40 [Newman et al., 2020]. We adopt the benchmark, training procedure and baselines prepared by Csord´as et al. [2021], which achieves strong results under standard s2s learning. Here, our aim is not to break SOTA, which can be achieve by hybrid-symbolic architectures [Chen et al., 2020, Shaw et al., 2021]. Instead, we focus on improving Transformer generalization in this task, hence the baselines are chosen as several variants of Transformers (TRM) targeted to sequence extrapolation, including those using Relative Positional Encoding (RPE [Dai et al., 2019]) and Universal Transformer (U. TRM [Dehghani et al., 2018]), which is an advanced Transformer variant that recurrently processes each token, and can dynamically adjust the number of processing steps. Following Csord´as et al. [2021], each baseline is trained 5 times for 50K steps and the resulting model after training is used for evaluation (no validation). Here, we use Transformer as the Encoder, which is the same as the TRM, and stack the Controller to another Transform decoder (see details in Appendix D.3). Hence, the only difference is the decoding where PANM leverages pointer manipulation. Table 2 shows that PANM outperforms other baselines in the hardest settings when the training length is up-to 22, 24, and 25. For 22 and 24 cases, general models like PANM cannot show perfect generalization because some testing compositions is entirely discarded from the train set. In easier settings, PANM shares the perfect median accuracy with the sophisticated U. TRM + RPE although it does not use RPE. Remarkably, despite sharing the same encoder, TRM performs much worse than PANM and even fails to learn in easy modes (33, 36, 40), indicating the importance of pointer handling in this testbed. One problem for other baselines is the EOS decision (when to generate ending token), which requires length tracking [Newman et al., 2020]. As they do not have content free sequence iteration mechanisms, it is extremely hard to trace the length without overfitting to the training data. On the other hand, PANM can hypothetically generate pointer incrementally and capture the difference between the last and the first pointers, i.e. the input length, and infer the output sequence length based on that information. Mathematical Problems We test our model on mathematics [Saxton et al., 2018] where the input/output are questions and answers about math and each token is a character. For example, What is − 5 − 110911? → −110916 (add or sub) and What is the hundreds digit of 31253? → 2 (place value). The task requires not only math reasoning, but also natural language understanding. We follow the training from Csord´as et al. [2021] to conduct experiments on 2 subsets: add or sub (a.s) and place value (p.v), and compare our method with Transformer-based baselines. Here, we focus on the extrapolating test set involving larger numbers, more numbers, more compositions, and thus longer input sequences than the training. We use TRM + RPE as the Encoder and the Controller is added to a normal TRM decoder. As shown in Table 2, on place value, PANM does not suffer from performance crash as TRM + RPE (0% test accuracy, as admitted in the paper [Csord´as et al., 2021] even though it uses the same encoder). PANM achieves similar results as U. TRM+ RPE on add or sub while outperforming it by 11% on place value. We also examine PANM with the original Transformer and report additional results in Appendix D.3. 3.4 Other NLP Tasks Question Answering Our objective is to explore the PANM’s generalization beyond obviously compositional data by applying it in a more practical setting of question answering. For this purpose, we utilize two datasets, namely bAbI [Weston et al., 2015] and SQUAD 1.1 [Rajpurkar et al., 2016] where the input sequence is a context paragraph and a question, and the output is the answer. To add complexity to the task, we ensure the length of test sequence is greater than that of the training by sorting the context paragraph by length and splitting the sorted data into 0.8/0.2 and 0.5/0.5 ratio. Details of the data/task are in Appendix D.4. In bAbI, we configure the PANM similarly to the one described in § 3.3 using Transformer backbone, and test the models after 100-epoch training. The models predict the answer tokens given the context and question tokens. As shown in Table 3 and Appendix Fig. 5 (right), PANM helps Transformer generalize better, consistently improving around 6% and 5% using 0.8/0.2 and 0.5/0.5 splits, respectively. Notably, PANM’s testing loss is not diverged quickly as Transformer’s, indicating PANM’s capability of reducing overfitting. In SQUAD, we use BERT as the backbone to predict the start and the end of the answer as in Kenton and Toutanova [2019]. PANM-assisted model outperforms the baselines by 1% and 2% exact match accuracy, respectively (Table 4). The improvement is significant as BERT is a big foundation model already pretrained with big data and robust against novel test data. Machine Translation Here, we want to verify the PANM in machine translation and show that PANM can work with different number layers of Transformer. The results are presented in Fig. 3 (b) where we report the model perplexity on Multi30K (en-de) dataset. The 30K-sample dataset is sorted by input length and split into training and testing s.t. testing sequences are longer, similar to QA task. The results demonstrate PANM can consistently improve the generalization performance of Transformer across different split ratios and the number of encoder/decoder layers. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia; (2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia; (3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia; (4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia; (5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. Authors: Authors: (1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia; (2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia; (3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia; (4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia; (5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. Table of Links Abstract & Introduction Abstract & Introduction Methods Methods Methods Part 2 Methods Part 2 Experimental Results Experimental Results Experimental Results Part 2 Experimental Results Part 2 Related Works, Discussion, & References Related Works, Discussion, & References Appendix A, B, & C Appendix A, B, & C Appendix D Appendix D 3.2 Dyck Language Recognition 3.3 Compositional Learning SCAN In this task , one needs to map an input sentence into an output sequence of commands [Lake and Baroni, 2018]. The sequences are compositional, consisting of reusable parts. For example, in one case, “jump twice” should be mapped to “JUMP JUMP” and in another, “walk twice” becomes “WALK WALK”. We focus on the “length split” datasets where the training sequences are shorter than the test ones with 11 length modes L = 22, 24, .., 40 [Newman et al., 2020]. We adopt the benchmark, training procedure and baselines prepared by Csord´as et al. [2021], which achieves strong results under standard s2s learning. Here, our aim is not to break SOTA, which can be achieve by hybrid-symbolic architectures [Chen et al., 2020, Shaw et al., 2021]. Instead, we focus on improving Transformer generalization in this task, hence the baselines are chosen as several variants of Transformers (TRM) targeted to sequence extrapolation, including those using Relative Positional Encoding (RPE [Dai et al., 2019]) and Universal Transformer (U. TRM [Dehghani et al., 2018]), which is an advanced Transformer variant that recurrently processes each token, and can dynamically adjust the number of processing steps. Following Csord´as et al. [2021], each baseline is trained 5 times for 50K steps and the resulting model after training is used for evaluation (no validation). Here, we use Transformer as the Encoder, which is the same as the TRM, and stack the Controller to another Transform decoder (see details in Appendix D.3). Hence, the only difference is the decoding where PANM leverages pointer manipulation. SCAN Table 2 shows that PANM outperforms other baselines in the hardest settings when the training length is up-to 22, 24, and 25. For 22 and 24 cases, general models like PANM cannot show perfect generalization because some testing compositions is entirely discarded from the train set. In easier settings, PANM shares the perfect median accuracy with the sophisticated U. TRM + RPE although it does not use RPE. Remarkably, despite sharing the same encoder, TRM performs much worse than PANM and even fails to learn in easy modes (33, 36, 40), indicating the importance of pointer handling in this testbed. One problem for other baselines is the EOS decision (when to generate ending token), which requires length tracking [Newman et al., 2020]. As they do not have content free sequence iteration mechanisms, it is extremely hard to trace the length without overfitting to the training data. On the other hand, PANM can hypothetically generate pointer incrementally and capture the difference between the last and the first pointers, i.e. the input length, and infer the output sequence length based on that information. Mathematical Problems We test our model on mathematics [Saxton et al., 2018] where the input/output are questions and answers about math and each token is a character. For example, What is − 5 − 110911? → −110916 (add or sub) and What is the hundreds digit of 31253? → 2 (place value). The task requires not only math reasoning, but also natural language understanding. We follow the training from Csord´as et al. [2021] to conduct experiments on 2 subsets: add or sub (a.s) and place value (p.v), and compare our method with Transformer-based baselines. Here, we focus on the extrapolating test set involving larger numbers, more numbers, more compositions, and thus longer input sequences than the training. We use TRM + RPE as the Encoder and the Controller is added to a normal TRM decoder. As shown in Table 2, on place value, PANM does not suffer from performance crash as TRM + RPE (0% test accuracy, as admitted in the paper [Csord´as et al., 2021] even though it uses the same encoder). PANM achieves similar results as U. TRM+ RPE on add or sub while outperforming it by 11% on place value. We also examine PANM with the original Transformer and report additional results in Appendix D.3. Mathematical Problems 3.4 Other NLP Tasks Question Answering Our objective is to explore the PANM’s generalization beyond obviously compositional data by applying it in a more practical setting of question answering. For this purpose, we utilize two datasets, namely bAbI [Weston et al., 2015] and SQUAD 1.1 [Rajpurkar et al., 2016] where the input sequence is a context paragraph and a question, and the output is the answer. To add complexity to the task, we ensure the length of test sequence is greater than that of the training by sorting the context paragraph by length and splitting the sorted data into 0.8/0.2 and 0.5/0.5 ratio. Details of the data/task are in Appendix D.4. In bAbI , we configure the PANM similarly to the one described in § 3.3 using Transformer backbone, and test the models after 100-epoch training. The models predict the answer tokens given the context and question tokens. As shown in Table 3 and Appendix Fig. 5 (right), PANM helps Transformer generalize better, consistently improving around 6% and 5% using 0.8/0.2 and 0.5/0.5 splits, respectively. Notably, PANM’s testing loss is not diverged quickly as Transformer’s, indicating PANM’s capability of reducing overfitting. In SQUAD , we use BERT as the backbone to predict the start and the end of the answer as in Kenton and Toutanova [2019]. PANM-assisted model outperforms the baselines by 1% and 2% exact match accuracy, respectively (Table 4). The improvement is significant as BERT is a big foundation model already pretrained with big data and robust against novel test data. Question Answering bAbI SQUAD Machine Translation Here, we want to verify the PANM in machine translation and show that PANM can work with different number layers of Transformer. The results are presented in Fig. 3 (b) where we report the model perplexity on Multi30K (en-de) dataset. The 30K-sample dataset is sorted by input length and split into training and testing s.t. testing sequences are longer, similar to QA task. The results demonstrate PANM can consistently improve the generalization performance of Transformer across different split ratios and the number of encoder/decoder layers. Machine Translation This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Researchers Built a Pointer System That Helps AI Generalize Better

What You Have to Know About the New Features in Go 1.24: Cleanups and Weak Pointers

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Methodology 2 Methodology 3 Hardware 3 Hardware 4 Software 4 Software 5 Network 5 Network 6 Consensus 6 Consensus 7 Cryptocurrency Economics 7 Cryptocurrency Economics 8 Client API 8 Client API 9 Governance 9 Governance 10 Geography 10 Geography 11 Case Studies 11 Case Studies 12 Discussion and References 12 Discussion and References A. Decentralization and Policymaking A. Decentralization and Policymaking B. Software Testing B. Software Testing C. Brief Evaluations per Layer C. Brief Evaluations per Layer D. Measuring decentralization D. Measuring decentralization E. Fault Tolerance and Decentralization E. Fault Tolerance and Decentralization 4 Software Software development is another dimension of distributed ledgers that has been associated with potential centralization [44,147,11]. Diverse software development and usage is a core element of stability and safety of distributed ledgers, as it increases resilience to catastrophic bugs in a product’s code. A vulnerability in one implementation may jeopardize a part of the system, but if the system is sufficiently decentralized, such vulnerabilities would not escalate to systemic threats. Following, we discuss the development of core blockchain software components, namely transaction validation and PoW mining (via full nodes) and management of keys and digital assets (via wallets).[7] Protocol Participation. The principal type of software in blockchain systems is the full node. Full nodes implement the ledger protocol by: i) keeping a local chain; ii) validating new transactions; iii) extending the local chain with new blocks; iv) participating in the consensus mechanism to incorporate new blocks. To analyze decentralization, we identify two resources of interest: 1) (number of) full nodes; 2) participating power (e.g., computational or stake) that is hosted on full nodes. The relevant parties are: 1) full node software implementations; 2) full node software developers. Protocol Participation. Protocol Participation. Relying on a handful of full node implementations introduces safety, liveness, and stability hazards. A bug that fails to validate correct transactions would hurt the system’s liveness, whereas accepting incorrect transactions could hurt the system’s safety and stability, e.g., via a network split or token forgery. Such bugs have been observed in Bitcoin Core and could have resulted in Denial-of-Service (DoS) attacks [75] and token forgery [48]. Implementation bugs could also threaten privacy, if nodes reveal information about message origin (e.g., IP addresses). Similar threats arise when code gets reused across different projects. Often, a new blockchain is only a “derivative”, that is a project that started by forking an existing codebase, including copyrighted information [147]. Such projects often remain unpatched, so bugs in the initial implementation tend to spill over [44]. Vulnerabilities may also arise when adapting an existing implementation in a new setting, e.g., copying Bitcoin’s code and replacing PoW with PoS [96]. Thus, widespread usage of multiple node implementations, developed by different teams, is a hallmark of a secure and reliant ecosystem. Asset Management. Distributed ledgers are mostly used for bookkeeping of digital asset transactions, so securely managing and transferring assets is a core necessity. Digital assets are typically managed by private keys and represented via addresses. The software responsible for managing keys and addresses is the wallet [100] and its principal functionalities are: i) store the user’s keys; ii) prove ownership of the assets (managed by the keys); iii) issue transactions that transfer assets to other accounts; iv) retrieve the user’s (keys’) balance and history information. Therefore, the resource of interest is the set of all assets managed via the ledger and the relevant parties are, accordingly: i) wallet implementations; ii) developers of wallet software. Asset Management. Asset Management. The wallet is a major point of security consideration, so multiple properties rely on it. A bug which e.g., corrupts the user’s keys could not only prohibit a user from transacting with their assets (liveness hazard), but forever lose access to them (stability hazard). This was demonstrated in 2017, when the “Parity” Ethereum wallet saw a vulnerability that allowed a user to take ownership of multiple assets and then lock them [89]; as a result, 300m worth of Ethereum tokens were forever lost. In another example, some Bitcoin wallets possibly displayed incorrect balance, effectively enabling a double spending attack [116]. Consequently, if a few implementations are predominantly used, a vulnerability could result in assets being unusable or stolen. Such vulnerability could also turn into a systemic point of failure, with the whole ledger becoming unusable.[8] Authors:
(1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk);
(2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk);
(3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). Authors: Authors: (1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk); (2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk); (3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [7] Appendix B also explores software testing. [8] A prime such example was “The DAO”, which in 2016 attracted nearly 14% of all Ethereum tokens and, when hacked, instigated a change in Ethereum’s consensus layer and a hard fork which split the network [169].

Blockchain’s Single Point of Failure? The Software Running It

I remember reading some years ago an L1’s proposal to migrate to an L2. That's where I came across the term decentralized sequencers. At the time, it seemed like an interesting but somewhat distant concept—an optimization rather than a necessity. But today, as rollups solidify their role as the dominant scaling solution for Ethereum and beyond, the importance of decentralized sequencers has grown considerably. decentralized sequencers For all the innovation that rollups bring, they still carry a significant centralization risk: most of them rely on a single, privileged sequencer. While this approach helps improve transaction speed and cost efficiency, it reintroduces some of the trust assumptions that blockchains were designed to eliminate. The debate around decentralized sequencers is not just about removing centralization for the sake of ideology; it’s about solving real bottlenecks in performance, censorship resistance, and fair transaction ordering. In this deep dive, we'll explore why sequencers matter, how decentralized sequencer networks work, the emerging designs in this space, and what challenges remain before they see widespread adoption. What is a sequencer and why does it matter? What is a sequencer and why does it matter? Sequencers play a critical role in rollup architectures. In simple terms, a sequencer is responsible for: Ordering transactions before they are posted on L1.


Providing rapid confirmations to users, even before final settlement on Ethereum.


Mitigating MEV (Maximal Extractable Value) by controlling how transactions are arranged. Ordering transactions before they are posted on L1. Ordering transactions before they are posted on L1. Providing rapid confirmations to users, even before final settlement on Ethereum. Providing rapid confirmations to users, even before final settlement on Ethereum. Mitigating MEV (Maximal Extractable Value) by controlling how transactions are arranged. Mitigating MEV (Maximal Extractable Value) by controlling how transactions are arranged. Without sequencers, rollups would function much like L1 blockchains—transactions would have to wait for finalization before they can be considered confirmed, resulting in slower user experiences. A sequencer brings the benefit of near-instant finality, making rollups feel as fast as traditional Web2 applications. Okay, but why do we need decentralized sequencers? Well, decentralized sequencers implies the opposite exists too. What's interesting is that centralized sequencers are more scalable and even faster. But its a double edged sword, its advantages in terms of scaling, comes with significant risk. Censorship – A centralized sequencer can choose which transactions to include or exclude, leading to concerns about neutrality, fair access and defeats the purpose of decentralized technology.
Single point of failure – If the sequencer goes offline, transactions cannot be processed until it comes back. While some rollups have fallback mechanisms, they are inefficient and not ideal for mainstream adoption. Censorship – A centralized sequencer can choose which transactions to include or exclude, leading to concerns about neutrality, fair access and defeats the purpose of decentralized technology. Single point of failure – If the sequencer goes offline, transactions cannot be processed until it comes back. While some rollups have fallback mechanisms, they are inefficient and not ideal for mainstream adoption. This might also seem like an unlikely scenario but it has in fact happened before. Arbitrum’s sequencer once stalled, making the network unusable for over an hour. MEV exploitation – A centralized sequencer can extract value from users through unfair transaction ordering, front-running, or sandwich attacks. MEV exploitation – A centralized sequencer can extract value from users through unfair transaction ordering, front-running, or sandwich attacks. These risks make it pertinent that L2s move towards a decentralized sequencer architecture.While a number of chains are still using centralized sequencers like Arbitrum one, optimism and base, we have seen new players like Morph adopting the decentralized sequencer model. How do decentralized sequencers work? A decentralized sequencer network, distributes the sequencing function across multiple entities, removing the reliance on a single entity. Its essentially the opposite of centralized sequencers. Its more time consuming and more expensive (in comparison with centralized sequencers) but for what it lacks, it makes up for it with: Censorship resistance – No single entity can exclude transactions.
High availability – Even if one sequencer node goes down, others can take over. This eliminates the risk of network downtime.
Fair transaction ordering – Preventing MEV abuse and ensuring a level playing field. Censorship resistance – No single entity can exclude transactions. High availability – Even if one sequencer node goes down, others can take over. This eliminates the risk of network downtime. Fair transaction ordering – Preventing MEV abuse and ensuring a level playing field. Shared Sequencing In the shared sequencing approach, multiple sequencers collaborate to process transactions in a shared mempool. The sequencers collectively determine the order of transactions and propose blocks to the L1 chain based on a consensus mechanism. Advantages: Ensures consistent transaction ordering across all sequencers
Reduces the risk of MEV exploitation through a fair ordering mechanism
Enables seamless failover in case of individual sequencer failures\ Ensures consistent transaction ordering across all sequencers Reduces the risk of MEV exploitation through a fair ordering mechanism Enables seamless failover in case of individual sequencer failures\ Challenges: Requires a robust consensus mechanism to ensure fair participation and prevent attacks
Potential for lower throughput compared to centralized sequencing due to the coordination overhead
Increased communication complexity among sequencers Requires a robust consensus mechanism to ensure fair participation and prevent attacks Potential for lower throughput compared to centralized sequencing due to the coordination overhead Increased communication complexity among sequencers Example: The Morph rollup uses a shared sequencing model based on a proof-of-stake consensus among sequencers. Example The Future of Decentralized Sequencing As the Ethereum ecosystem continues to scale and mature, the need for decentralized sequencing solutions will only grow. While the current approaches offer significant improvements over centralized sequencers, there is still room for innovation and optimization. One area of active research is the development of hybrid sequencing models that combine the benefits of different approaches. For example, a system could use shared sequencing for high-value transactions that require strong censorship resistance, while employing a more centralized approach for low-value transactions to maximize throughput. Another promising direction is the integration of privacy-preserving technologies like zero-knowledge proofs into decentralized sequencers. This could enable users to submit transactions without revealing their identities or transaction details to the sequencers, further enhancing the censorship resistance and neutrality of the system. As the technology evolves, we can expect to see more rollups and L2 solutions adopting decentralized sequencing architectures. This will not only improve the security and fairness of these systems but also bring us closer to the vision of a fully decentralized and scalable blockchain ecosystem. Conclusion Decentralized sequencers are a critical component of the Ethereum scaling puzzle. By removing the reliance on a single, privileged entity, they mitigate the risks of censorship, single points of failure, and MEV exploitation. While the current approaches have their tradeoffs and challenges, they represent a significant step towards a more resilient and trustless blockchain infrastructure. As the space continues to innovate and mature, we can expect to see more advanced and efficient decentralized sequencing solutions emerge. These developments will not only benefit Ethereum but also have far-reaching implications for the broader blockchain ecosystem, enabling new use cases and unlocking the full potential of decentralized technologies.

Rollups Are the Future—But Their Biggest Bottleneck Might Be a Single Point of Failure

The news cycle is exceptionally choppy right now. Brands need to make critical decisions on where they stand and the course ahead. And if they don’t take a stand and choose where they’re headed, how long can they stay up and stay above the waters? When boating, or doing other things where a fall can be dangerous, there’s something called the three points of contact rule. The idea is your body should have at least three points of contact with something stable at all times. Anything less than that and a wave can come along and throw you. That’s how marketing should be done right now. Sales One of those points of contact is almost always going to be the deck of the boat. At least one of your feet is going to be planted there. In marketing or public relations that should be sales. Even if you’re a non-profit, it’s going to be where the money comes from. You can have points of contact elsewhere, but if revenue or sponsorship is not coming from somewhere, you don’t have a going concern. While that seems obvious, many marketing and most public relations teams don’t do this. Sales people can be a pain in the rear end. If they win business, they can say it was because of their efforts and marketing and sales didn’t really help. When sales aren’t coming in, well marketing isn’t doing its job and what in the world is public relations doing apart from absorbing budget? And the concreteness of sales’ metrics… either money is coming into the bank account or not, is scary if you don’t have a direct role in bringing in those dollars, but are rather supporting it indirectly. Too frequently marketing and public relations functions then try to set up metrics that they can control, and are at a safe distance from sales. Moving leads and prospects through a marketing funnel, impressions, clicks, etc. And there’s correlation between that and sales, but it sure is tenuous. Sales are sales are sales. And if you don’t have at least one of your points of contact on the deck of the boat formed by sales, it really doesn’t matter where the other points of contact are. Integration Beyond sales, the other points of contact can be any number of things that work for sales. It could be media relations, social media, email campaigns, collateral, your site, etc. But whatever it is it had better be connected to sales. And if you’re moving you’d better through a changing market and media environment, make sure those other points of contact are also connected to each other in addition to sales. This seems obvious, but if you have a newsletter, how many of its articles have been selected or written with prospects in your funnel in mind? Does your sales team know when the newsletter is going out and has it been written in a fashion where it organically can be forwarded to leads and prospects? Can a sales person send a message saying, “hey you know how we talked about Hackernoon the other day and how great it is as a resource? Here’s an article I found there that’s useful to marketers and public relations professionals and shows how brands should pay attention to places like Hackernoon to understand how they can become more proficient in today’s crazy environment.” Why now? And let’s come back to today’s crazy environment. Before Donald Trump's inauguration, what society seemed to respect in a brand, and what society expects now is in volatile flux. What’s the constant message and brand running through those three points of contact? Here's three constants we heard from Dominic-Madori Davis of TechCrunch and Alyse Killeen of Stillmark as they were interviewed by Meredith Beuchaw and Ed Zimmerman of Lowenstein Sandler LLP at VentureCrush recently: Your personal story and choices you've made anchor brand and the sale
Your primary, consistent, and differentiated value prop through those choices sharpens brand and backs the sale
Non-textual signals amplify brand and facilitate the sale In times like these, you are what you have chosen to do. And what you choose now will be what your brand is tomorrow and beyond. Davis said a personal story is part of what draws something to her journalistic attention. She also looks for stories she and readers would not have found on their own. Davis also went on to say today a journalist has a brand that transcends the news brand they work for. For her personal story, she shared how her professional journey started with screenwriting at University of Southern California. She was pulled into copy editing her student paper. The Varsity Blues scandals put a news spotlight on the campus (Melissa Korn's journalism is also great here for context). Does her initial choice of screenwriting tell us something about her investment in personal stories and having the space to explore the unexpected? Killeen shared the influence of working for her grandfather, and hearing people talk about the respect they had for his integrity, the choices he made. At Stillmark she said she focuses their brand on a primary value proposition. And she pointed out that in working with startup founders, what they think and want their primary value proposition to be, may change when they see why customers are coming to them. Davis highlighted how fashion choices, are an example of showing differentiation and a brand, rather than telling. You can watch the full panel here: https://www.youtube.com/watch?v=_LBEZl5dRGI&embedable=true Bringing together brand and sales Here’s the thing, sales ensure you are a going concern. Brand ensures your going concern will endure based on the memory of the choices you’ve made in the course of winning sales. If your sales don’t have integrity, if you sacrifice integrity for sales, the dollars are good now, but they’ll be cheaper later as your brand declines. For those three points of contact, make sure the point of contact you have with sales, is anchored in the right kind of sales, for the right reasons, backed with the kind of demonstrated integrity that will sustain your business and grow it dependably. The news cycle is exceptionally choppy right now. Brands need to make critical decisions on where they stand and the course ahead. And if they don’t take a stand and choose where they’re headed, how long can they stay up and stay above the waters? When boating, or doing other things where a fall can be dangerous, there’s something called the three points of contact rule. The idea is your body should have at least three points of contact with something stable at all times. Anything less than that and a wave can come along and throw you. That’s how marketing should be done right now. Sales Sales One of those points of contact is almost always going to be the deck of the boat. At least one of your feet is going to be planted there. In marketing or public relations that should be sales. Even if you’re a non-profit, it’s going to be where the money comes from. You can have points of contact elsewhere, but if revenue or sponsorship is not coming from somewhere, you don’t have a going concern. While that seems obvious, many marketing and most public relations teams don’t do this. Sales people can be a pain in the rear end. If they win business, they can say it was because of their efforts and marketing and sales didn’t really help. When sales aren’t coming in, well marketing isn’t doing its job and what in the world is public relations doing apart from absorbing budget? And the concreteness of sales’ metrics… either money is coming into the bank account or not, is scary if you don’t have a direct role in bringing in those dollars, but are rather supporting it indirectly. Too frequently marketing and public relations functions then try to set up metrics that they can control, and are at a safe distance from sales. Moving leads and prospects through a marketing funnel, impressions, clicks, etc. And there’s correlation between that and sales, but it sure is tenuous. Sales are sales are sales. And if you don’t have at least one of your points of contact on the deck of the boat formed by sales, it really doesn’t matter where the other points of contact are. Integration Integration Beyond sales, the other points of contact can be any number of things that work for sales. It could be media relations, social media, email campaigns, collateral, your site, etc. But whatever it is it had better be connected to sales. And if you’re moving you’d better through a changing market and media environment, make sure those other points of contact are also connected to each other in addition to sales. This seems obvious, but if you have a newsletter, how many of its articles have been selected or written with prospects in your funnel in mind? Does your sales team know when the newsletter is going out and has it been written in a fashion where it organically can be forwarded to leads and prospects? Can a sales person send a message saying, “hey you know how we talked about Hackernoon the other day and how great it is as a resource? Here’s an article I found there that’s useful to marketers and public relations professionals and shows how brands should pay attention to places like Hackernoon to understand how they can become more proficient in today’s crazy environment.” Why now? Why now? And let’s come back to today’s crazy environment. Before Donald Trump's inauguration, what society seemed to respect in a brand, and what society expects now is in volatile flux. What’s the constant message and brand running through those three points of contact? Here's three constants we heard from Dominic-Madori Davis of TechCrunch and Alyse Killeen of Stillmark as they were interviewed by Meredith Beuchaw and Ed Zimmerman of Lowenstein Sandler LLP at VentureCrush recently: Dominic-Madori Davis Dominic-Madori Davis TechCrunch TechCrunch Alyse Killeen Alyse Killeen Stillmark Stillmark Meredith Beuchaw Meredith Beuchaw Ed Zimmerman Ed Zimmerman Lowenstein Sandler LLP Lowenstein Sandler LLP VentureCrush VentureCrush Your personal story and choices you've made anchor brand and the sale Your primary, consistent, and differentiated value prop through those choices sharpens brand and backs the sale Non-textual signals amplify brand and facilitate the sale Your personal story and choices you've made anchor brand and the sale Your primary, consistent, and differentiated value prop through those choices sharpens brand and backs the sale Non-textual signals amplify brand and facilitate the sale In times like these, you are what you have chosen to do. And what you choose now will be what your brand is tomorrow and beyond. Davis said a personal story is part of what draws something to her journalistic attention. She also looks for stories she and readers would not have found on their own. Davis also went on to say today a journalist has a brand that transcends the news brand they work for. For her personal story, she shared how her professional journey started with screenwriting at University of Southern California. She was pulled into copy editing her student paper. The Varsity Blues scandals put a news spotlight on the campus ( Melissa Korn 's journalism is also great here for context). Melissa Korn Melissa Korn Does her initial choice of screenwriting tell us something about her investment in personal stories and having the space to explore the unexpected? Killeen shared the influence of working for her grandfather, and hearing people talk about the respect they had for his integrity, the choices he made. At Stillmark she said she focuses their brand on a primary value proposition. And she pointed out that in working with startup founders, what they think and want their primary value proposition to be, may change when they see why customers are coming to them. Davis highlighted how fashion choices, are an example of showing differentiation and a brand, rather than telling. You can watch the full panel here: https://www.youtube.com/watch?v=_LBEZl5dRGI&embedable=true https://www.youtube.com/watch?v=_LBEZl5dRGI&embedable=true Bringing together brand and sales Bringing together brand and sales Here’s the thing, sales ensure you are a going concern. Brand ensures your going concern will endure based on the memory of the choices you’ve made in the course of winning sales. If your sales don’t have integrity, if you sacrifice integrity for sales, the dollars are good now, but they’ll be cheaper later as your brand declines. For those three points of contact, make sure the point of contact you have with sales, is anchored in the right kind of sales, for the right reasons, backed with the kind of demonstrated integrity that will sustain your business and grow it dependably.

Your Brand Is Sinking Without These Three Points of Contact

Table of Links Abstract and 1. Introduction Clock in Orbit
2.1 Coordinate Time
2.2 Local Frame for the Moon


Clock Rate Differences Between Earth and Moon


Clocks at Earth-Moon Lagrance Points
4.1 Clock at Lagrange point L1
4.2. Clock at Lagrange point L2
4.3. Clock at Lagrange point L4 or L5


Conclusions Appendix 1: Fermi Coordinates with Origin at the Center of the Moon Appendix 2: Construction of Freely Falling Center of Mass Frame Appendix 3: Equations of Motion of Earth and Moon Appendix 4: Comparing Results in Rotating and Non-Rotating Coordinate Systems Acknowledgments and References 4. CLOCKS AT EARTH-MOON LAGRANGE POINTS The Lagrange points offer a cost-effective and low-noise environment for stationing spacecraft with clocks because there is no net acceleration. As a result, the orbits of clocks at these points with respect to the Earth-Moon barycenter are well-determined and the corresponding frequency offsets are precisely determined. Therefore, the proper times of clocks at Lagrange points can be related to the proper times of clocks on the Earth and Moon with high precision. This information is crucial for synchronizing remote clocks in cislunar space. Both L4 and L5 are stable points, but L1 and L2 are only metastable locations. 4.1 Clock at Lagrange point L1 In this section, we consider a clock at L1 and compare its rate to a clock on Earth’s surface. It is most convenient to use a freely falling inertial frame centered at the center of mass of the Earth-Moon system for this calculation. First, we need the precise position of L1; most treatments assume circular motion, but this is inadequate for our purposes. We assume Keplerian elliptical orbits for the Earth and Moon about the center of mass, for which the Earth-Moon distance is given by Eq. (19). The radial velocity is then and the radial acceleration of the Moon relative to Earth is Assume that L1 is between the Earth and Moon and at a distance x1D from the Moon, the net gravitational force towards the Moon supplies the radial acceleration of L1, diminished by the centripetal acceleration due to rotation of the Earth-Moon line. This gives the condition Solving Eq. (39) for x1 gives x1 = 0.15093428(1). The metric, neglecting solar tides, is The motion is so slow that the potential terms in the last line can be neglected. The transverse and radial velocities of the clock due to the rotation of the Earth-Moon line are So, the total velocity squared is 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 The clock is equidistant from Earth and the Moon. The total velocity squared is the sum of the radial velocity squared and the transverse velocity squared: Authors:
(1) Neil Ashby, National Institute of Standards and Technology, Boulder, CO 80305 (Neil.Ashby@Colorado.Edu);
(2) Bijunath R. Patla, National Institute of Standards and Technology, Boulder, CO 80305 (bijunath.patla@nist.gov). This paper is available on arxiv under CC0 1.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Clock in Orbit
2.1 Coordinate Time
2.2 Local Frame for the Moon Clock Rate Differences Between Earth and Moon Clocks at Earth-Moon Lagrance Points
4.1 Clock at Lagrange point L1
4.2. Clock at Lagrange point L2
4.3. Clock at Lagrange point L4 or L5 Conclusions Clock in Orbit 2.1 Coordinate Time 2.2 Local Frame for the Moon Clock in Orbit Clock in Orbit 2.1 Coordinate Time 2.1 Coordinate Time 2.2 Local Frame for the Moon 2.2 Local Frame for the Moon Clock Rate Differences Between Earth and Moon Clock Rate Differences Between Earth and Moon Clock Rate Differences Between Earth and Moon Clocks at Earth-Moon Lagrance Points 4.1 Clock at Lagrange point L1 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 Clocks at Earth-Moon Lagrance Points 4.1 Clock at Lagrange point L1 4.1 Clock at Lagrange point L1 4.2. Clock at Lagrange point L2 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 4.3. Clock at Lagrange point L4 or L5 Conclusions Conclusions Conclusions Appendix 1: Fermi Coordinates with Origin at the Center of the Moon Appendix 1: Fermi Coordinates with Origin at the Center of the Moon Appendix 2: Construction of Freely Falling Center of Mass Frame Appendix 2: Construction of Freely Falling Center of Mass Frame Appendix 3: Equations of Motion of Earth and Moon Appendix 3: Equations of Motion of Earth and Moon Appendix 4: Comparing Results in Rotating and Non-Rotating Coordinate Systems Appendix 4: Comparing Results in Rotating and Non-Rotating Coordinate Systems Acknowledgments and References Acknowledgments and References 4. CLOCKS AT EARTH-MOON LAGRANGE POINTS The Lagrange points offer a cost-effective and low-noise environment for stationing spacecraft with clocks because there is no net acceleration. As a result, the orbits of clocks at these points with respect to the Earth-Moon barycenter are well-determined and the corresponding frequency offsets are precisely determined. Therefore, the proper times of clocks at Lagrange points can be related to the proper times of clocks on the Earth and Moon with high precision. This information is crucial for synchronizing remote clocks in cislunar space. Both L4 and L5 are stable points, but L1 and L2 are only metastable locations. 4.1 Clock at Lagrange point L1 In this section, we consider a clock at L1 and compare its rate to a clock on Earth’s surface. It is most convenient to use a freely falling inertial frame centered at the center of mass of the Earth-Moon system for this calculation. First, we need the precise position of L1; most treatments assume circular motion, but this is inadequate for our purposes. We assume Keplerian elliptical orbits for the Earth and Moon about the center of mass, for which the Earth-Moon distance is given by Eq. (19). The radial velocity is then and the radial acceleration of the Moon relative to Earth is Assume that L1 is between the Earth and Moon and at a distance x1D from the Moon, the net gravitational force towards the Moon supplies the radial acceleration of L1, diminished by the centripetal acceleration due to rotation of the Earth-Moon line. This gives the condition Solving Eq. (39) for x1 gives x1 = 0.15093428(1). The metric, neglecting solar tides, is The motion is so slow that the potential terms in the last line can be neglected. The transverse and radial velocities of the clock due to the rotation of the Earth-Moon line are So, the total velocity squared is 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 The clock is equidistant from Earth and the Moon. The total velocity squared is the sum of the radial velocity squared and the transverse velocity squared: Authors: (1) Neil Ashby, National Institute of Standards and Technology, Boulder, CO 80305 (Neil.Ashby@Colorado.Edu); (2) Bijunath R. Patla, National Institute of Standards and Technology, Boulder, CO 80305 (bijunath.patla@nist.gov). Authors: Authors: (1) Neil Ashby, National Institute of Standards and Technology, Boulder, CO 80305 ( Neil.Ashby@Colorado.Edu ); Neil.Ashby@Colorado.Edu (2) Bijunath R. Patla, National Institute of Standards and Technology, Boulder, CO 80305 ( bijunath.patla@nist.gov ). bijunath.patla@nist.gov This paper is available on arxiv under CC0 1.0 license. This paper is available on arxiv under CC0 1.0 license. available on arxiv available on arxiv

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