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Pentagon pauses development of its go-to data analytics tool – Defense One

Updated: 6:10 p.m. ET.

The Pentagon is pausing development of Advanaits default data-analytics platformso it can be upgraded to handle increased demand, according to an internal email obtained by Defense One.

In the June 3 email, the Pentagons new chief data and artificial intelligence officerdirected developers of the Advana platform to pause much of the active work and additional features until infrastructure changes are complete.

The pause will force users who were banking on forthcomingfeatures, tools, or applications to use existing Advana tools to do their work. The email did not indicate when development might resume.

The Department's demand for enterprise data and analytics services have outgrown the original architecture of the Advana platform, wrote Radha Iyengar Plumb, reflecting a review of the infrastructure, tech tools, and applications that her office relies on soon after taking office in April.

I have directed the team to focus on upgrading the technical framework of Advana to better meet the requirements of our customers and the whole Department, and develop a sustainable enterprise solution for the future, Plumb wrote. To accelerate these platform enhancements, I have also directed the team to reprioritize activities from continuing to build on Advana's current platform to focus on its future platform engineering activities. As part of this, we are looking hard at a variety of applications in the Advana ecosystem.

During the developmental pause, the Pentagon will evaluate whether the applications are stable and should be integrated on the upgraded infrastructure, or if they would fit better elsewhere in DODs tech enterprise.

I understand this will disrupt the planned uses and services for your teams, and I did not make this decision lightly. I understand from my team that this reverses prior commitments madeand want to acknowledge the impacts this may have on your roadmaps and leadership obligations, Plumb wrote. My team and l are dedicated to working with you all to identify alternative hosting environments for your use case or transition your use case to generally available tools on [Advana] until we are able to relook at bringing new vendor pilots onto the future infrastructure environment.

The Advana platform has been an important part of the Pentagons data and analytics efforts, getting its start with financial data management and growing to include other areas across the defense enterprise. In a 2021 memo, Deputy Defense Secretary Kathleen Hicks billed Advana as the Pentagons go-to data analytics platform, and said defense organizations must get executive approval to use other platforms.

The Advancing Analytics (Advana) platform is the single enterprise authoritative data management and analytics platform for the Secretary of Defense, Deputy Secretary of Defense, and Principal Staff Assistants (PSAs), with inputs from all DoD Components, Hicks wrote. The use of other data management and analytics platforms must be approved by the DoD CDO and appropriate Component CDO to ensure adherence to an open data standard architecture.

Researchers have suggested that unified data analytics platforms that tie disparate systems together, like Advana, could help lessen the workloads of Pentagon employees. Moreover, Advana is the authoritative source for reporting Ukraine supplemental funding, and has been praised for its ability to keep track of weapons shipmentsbut criticized for the added burden to soldiers.

Having a mission-ready enterprise analytics infrastructure is critical to the Departments goal of leveraging data and AI from the boardroom to the battlefield, a senior defense official told Defense One.

In May, the Pentagon announced a new initiative, called the Open Data and Applications Government-owned Interoperable Repositories framework, designed to bring data analytics across the defense enterprise. The goal is to build on Hicks 2021 data memo to create a multi-vendor analytics ecosystem.

Advana is an important part of that data infrastructure layer and application environment. Over the past two years, the Advana platform scaled rapidly from initial capability based on pilot projects and prototypes to an enterprise-wide data and analytics environment the Department now uses to inform decision making at all levels, the official said, adding that the planned upgrades will help support Advanas rapid user growth.

These changes will result in improvements, such as accelerating the onboarding of new use cases for enterprise customers and opening up the Advana data platform to third party software development at a larger scale than we enjoy today. Our work to upgrade the platform will impact back-end engineering and, for the most part, no Advana customers will experience a degradation of existing services.

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EPAM’s Acquisition of Odysseus: Revolutionizing Life Sciences with AI & Analytics – TimesTech

EPAM Systems,a leading digital transformation services and product engineering company announced its acquisition ofOdysseus Data Services, a top health data analytics company.Odysseus will expand EPAMs ability to transform the life sciences value chain through advanced data analytics, data methods and artificial intelligence

We are pleased to have Odysseus join EPAM. With their strong capabilities in Real-World Evidence and Real-World Data the glue between multiple segments of the life sciences value chain our natural synergies make this an exciting time to add this to our portfolio to help our clients achieve better outcomes, said Greg Killian, Senior Vice President of Life Sciences at EPAM. Acquires Odysseus.We see the next wave of innovation based on standardized data powering AI and GenAI to improve life sciences research, clinical studies and post-market surveillance. EPAM Acquires Odysseus. Based on the combined strengths of EPAM and Odysseus, we are well positioned to lead that innovation.

Headquartered in Cambridge, Massachusetts, Odysseus generates healthcare data insights and evidence for clients through skilled data science and analytics, software engineering and data management and ontology and vocabulary management.EPAM Acquires Odysseus. The companys focus on a standardized and systematic approach to healthcare data analytics is the foundation for a better understanding of the inner workings of healthcare interventions in drug treatment, drug safety and efficacy, epidemiological research, provider support, quality measurements and cost reduction. EPAM Acquires Odysseus. Odysseus is an active member of the Observational Health Data Sciences and Informatics (OHDSI) collaborative and is intimately involved in the open standards and open science community through participation in research and development, including OMOP CDM, open source tools and methods.

Were excited to join the EPAM family, said Gregory Klebanov, CEO of Odysseus. With EPAMs strong foundation in AI, EPAM Acquires Odysseus. machine learning, data analytics and data management and cloud infrastructure combined with our healthcare data analytics and Real World Evidence expertise, we can address the whole life sciences value chain more comprehensively.

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Improving Business Performance with Machine Learning | by Juan Jose Munoz | Jun, 2024 – Towards Data Science

Because we are using an unsupervised learning algorithm, there is not a widely available measure of accuracy. However, we can use domain knowledge to validate our groups.

Visually inspecting the groups, we can see some benchmarking groups have a mix of Economy and Luxury hotels, which doesn't make business sense as the demand for hotels is fundamentally different.

We can scroll to the data and note some of those differences, but can we come up with our own accuracy measure?

We want to create a function to measure the consistency of the recommended Benchmarking sets across each feature. One way of doing this is by calculating the variance in each feature for each set. For each cluster, we can compute an average of each feature variance, and we can then average each hotel cluster variance to get a total model score.

From our domain knowledge, we know that in order to set up a comparable benchmark set, we need to prioritize hotels in the same Brand, possibly the same market, and the same country, and if we use different markets or countries, then the market tier should be the same.

With that in mind, we want our measure to have a higher penalty for variance in those features. To do so, we will use a weighted average to calculate each benchmark set variance. We will also print the variance of the key features and secondary features separately.

To sum up, to create our accuracy measure, we need to:

To keep our code clean and track our experiments , lets also define a function to store the results of our experiments.

Now that we have a baseline, lets see if we can improve our model.

Up until now, we did not have to know what was going on under the hood when we ran this code:

To improve our model, we will need to understand the model parameters and how we can interact with them to get better benchmark sets.

Lets start by looking at the Scikit Learn documentation and source code:

There are quite a few things going on here.

The Nearestneighbor class inherits fromNeighborsBase, which is the case class for nearest neighbor estimators. This class handles the common functionalities required for nearest-neighbor searches, such as

The Nearestneighbor class also inherits fromKNeighborsMixin and RadiusNeighborsMixinclasses. These Mixin classes add specific neighbor-search functionalities to the Nearestneighbor

Based on our scenario, KNeighborsMixin provides the functionality we need.

We need to understand one key parameter before we can improve our model; this is the distance metric.

The documentation mentions that the NearestNeighbor algorithm uses the Minkowski distance by default and gives us a reference to the SciPy API.

In scipy.spatial.distance, we can see two mathematical representations of "Minkowski" distance:

uv p=( i u iv i p ) 1/p

This formula calculates the p-th root of the sum of powered differences across all elements.

The second mathematical representation of Minkowski distance is:

uv p=( i w i(u iv i p )) 1/p

This is very similar to the first one, but it introduces weights wi to the differences, emphasizing or de-emphasizing specific dimensions. This is useful where certain features are more relevant than others. By default, the setting is None, which gives all features the same weight of 1.0.

This is a great option for improving our model as it allows us to pass domain knowledge to our model and emphasize similarities that are most relevant to users.

If we look at the formulas, we see the parameter. p. This parameter affects the "path" the algorithm takes to calculate the distance. By default, p=2, which represents the Euclidian distance.

You can think of the Euclidian distance as calculating the distance by drawing a straight line between 2 points. This is usally the shortest distance, however, this is not always the most desirable way of calculating the distance, specially in higher dimention spaces. For more information on why this is the case, there is this great paper online: https://bib.dbvis.de/uploadedFiles/155.pdf

Another common value for p is 1. This represents the Manhattan distance. You think of it as the distance between two points measured along a grid-like path.

On the other hand, if we increase p towards infinity, we end up with the Chebyshev distance, defined as the maximum absolute difference between any corresponding elements of the vectors. It essentially measures the worst-case difference, making it useful in scenarios where you want to ensure that no single feature varies too much.

By reading and getting familiar with the documentation, we have uncovered a few possible options to improve our model.

By default n_neighbors is 5, however, for our benchmark set, we want to compare each hotel to the 3 most similar hotels. To do so, we need to set n_neighbors = 4 (Subject hotel + 3 peers)

Based on the documentation, we can pass weights to the distance calculation to emphasize the relationship across some features. Based on our domain knowledge, we have identified the features we want to emphasize, in this case, Brand, Market, Country, and Market Tier.

Passing domain knowledge to the model via weights increased the score significantly. Next, lets test the impact of the distance measure.

So far, we have been using the Euclidian distance. Lets see what happens if we use the Manhattan distance instead.

Decreasing p to 1 resulted in some good improvements. Lets see what happens as p approximates infinity.

To use the Chebyshev distance, we will change the metric parameter to Chebyshev. The default sklearn Chebyshev metric doesnt have a weight parameter. To get around this, we will define a custom weighted_chebyshev metric.

We managed to decrease the primary feature variance scores through experimentation.

Lets visualize the results.

Using Manhattan distance with weights seems to give the most accurate benchmark sets according to our needs.

The last step before implementing the benchmark sets would be to examine the sets with the highest Primary features scores and identify what steps to take with them.

These 18 cases will need to be reviewed to ensure the benchmark sets are relevant.

As you can see, with a few lines of code and some understanding of Nearest neighbor search, we managed to set internal benchmark sets. We can now distribute the sets and start measuring hotels' KPIs against their benchmark sets.

You dont always have to focus on the most cutting-edge machine learning methods to deliver value. Very often, simple machine learning can deliver great value.

What are some low-hanging fruits in your business that you could easily tackle with Machine learning?

World Bank. World Development Indicators. Retrieved June 11, 2024, from https://datacatalog.worldbank.org/search/dataset/0038117

Aggarwal, C. C., Hinneburg, A., & Keim, D. A. (n.d.). On the Surprising Behavior of Distance Metrics in High Dimensional Space. IBM T. J. Watson Research Center and Institute of Computer Science, University of Halle. Retrieved from https://bib.dbvis.de/uploadedFiles/155.pdf

SciPy v1.10.1 Manual. scipy.spatial.distance.minkowski. Retrieved June 11, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.minkowski.html

GeeksforGeeks. Haversine formula to find distance between two points on a sphere. Retrieved June 11, 2024, from https://www.geeksforgeeks.org/haversine-formula-to-find-distance-between-two-points-on-a-sphere/

scikit-learn. Neighbors Module. Retrieved June 11, 2024, from https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors

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Improving Business Performance with Machine Learning | by Juan Jose Munoz | Jun, 2024 - Towards Data Science

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Crypto: Here are the Dangers of Ethereum’s Pectra Update! – Cointribune EN

13h00 3 min of reading by Eddy S.

As Ethereum prepares for the Pectra crypto update in early 2025, a recent research report published by Liquid Collective and Obol has revealed several associated risks. The report emphasizes the importance of client, operator, and cloud diversity, as well as concerns regarding the low adoption of Distributed Validator Technology (DVT).

According to the report, a significant bug in a dominant client could lead to substantial slashing penalties and network instability. As a fundamental element of Ethereums consensus mechanism, staking via a single node operator can expose the staked crypto assets to downtime and slashing risks.

The report also critically discussed the need for a broad geographic distribution of validators and cloud providers! Citing recent outages, such as those at Hetzner and AWS. It explained that DVT can significantly support this strategy by enhancing validator resilience by reducing correlated risks.

Ethereums Pectra update combines the Prague and Electra updates, focusing on changes in the crypto networks execution and consensus layers, respectively. Pectra is scheduled to launch in the first quarter of 2025 and will include Ethereum Improvement Proposal (EIP)-7251. According to the report, the Pectra update will allow staking providers to consolidate their stake into fewer validators by increasing the maximum effective balance to 2,048 ETH.

Finally, the report highlights the correlated risks and protocol-level penalties that are increasingly significant for Ethereum staking. Each staker and service provider must rigorously evaluate correlation, diversity, and risk mitigation to prevent potential risks, even from trusted node operators. While long-term institutional adoption is critical, staking setups must prioritize diversity among node operators and validators.

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Le monde volue et l'adaptation est la meilleure arme pour survivre dans cet univers ondoyant. Community manager crypto la base, je m'intresse tout ce qui touche de prs ou de loin la blockchain et ses drivs. Dans l'optique de partager mon exprience et de faire connatre un domaine qui me passionne, rien de mieux que de rdiger des articles informatifs et dcontracts la fois.

DISCLAIMER

The views, thoughts, and opinions expressed in this article belong solely to the author, and should not be taken as investment advice. Do your own research before taking any investment decisions.

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Crypto: Ethereum unleashes a huge wave of liquidations! – Cointribune EN

14h00 3 min of reading by Eddy S.

Turmoil reigns again in the crypto markets, shaken by a violent financial storm. Indeed, a sudden surge in position liquidations has swept across the ecosystem in the past 24 hours. This unexpected tidal wave has brought Ethereum to the forefront, potentially marking a decisive turning point in the evolution of crypto investment trends.

According to the data, total liquidations jumped 78.8% in one day, surpassing 75 million dollars, even as total open interest declined by 0.35%, to 66 million. In detail, 43.9 million long positions and 31.6 million short positions were liquidated.

However, it is Ethereum that dominates, with over 19 million in liquidations, including 5.6 million in longs and 13.5 million in shorts. This amount is significantly higher than Bitcoins, at 8.2 million. This feverish surge undeniably propels Ethereum to the forefront of the crypto scene.

The main platforms concentrate most of the movements, with Binance capturing 38.7 million in liquidations and OKX 23 million. Beyond the figures, this episode could mark a major turning point in crypto strategies. The increased volatility of Ethereum, combined with its potential, could indeed prompt many investors to reconsider their allocations.

A dynamic of capital redistribution in favor of Ethereum and promising altcoins thus seems to be emerging. This show of strength could initiate a lasting paradigm shift, where diversification and calculated risk-taking become the new watchwords.

These jolts may be only the beginnings of a profound reconfiguration of balances in the crypto universe. By taking the lead in this surge of liquidations, Ethereum appears to send a powerful signal in favor of increased diversification of investment portfolios. A new era might thus dawn, where wise risk management and the exploration of varied opportunities become the new niches for success in these turbulent markets.

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Le monde volue et l'adaptation est la meilleure arme pour survivre dans cet univers ondoyant. Community manager crypto la base, je m'intresse tout ce qui touche de prs ou de loin la blockchain et ses drivs. Dans l'optique de partager mon exprience et de faire connatre un domaine qui me passionne, rien de mieux que de rdiger des articles informatifs et dcontracts la fois.

DISCLAIMER

The views, thoughts, and opinions expressed in this article belong solely to the author, and should not be taken as investment advice. Do your own research before taking any investment decisions.

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Ethereum’s Vitalik Buterin supports TiTok as blockchain app – crypto.news

According to Ethereum (ETH) co-founder Vitalik Buterin, the new image compression method Token for Image Tokenizer (TiTok AI) can encode images to a size large enough to add them onchain.

On his Warpcast social media account, Buterin called the image compression method a new way to encode a profile picture. He went on to say that if it can compress an image to 320 bits, which he called basically a hash, it would render the pictures small enough to go on chain for every user.

The Ethereum co-founder took an interest in TiTok AI from an X post made by a researcher at the artificial intelligence (AI) image generator platform Leonardo AI.

The researcher, going by the handle @Ethan_smith_20, briefly explained how the method could help those interested in reinterpretation of high-frequency details within images to successfully encode complex visuals into 32 tokens.

Buterins perspective suggests the method could make it significantly easier for developers and creators to make profile pictures and non-fungible tokens (NFTs).

TiTok AI, developed by a collaborative effort from TikTok parent company ByteDance and the University of Munich, is described as an innovative one-dimensional tokenization framework, diverging significantly from the prevailing two-dimensional methods in use.

According to a research paper on the image tokenization method, AI enables TiTok to compress 256 by 256-pixel rendered images into 32 distinct tokens.

The paper pointed out issues seen with previous image tokenization methods, such as VQGAN. Previously, image tokenization was possible, but strategies were limited to using 2D latent grids with fixed downsampling factors.

2D tokenization could not circumvent difficulties in handling the redundancies found within images, and close regions were exhibiting a lot of similarities.

TiTok, which uses AI, promises to solve such an issue, by using technologies that effectively tokenize images into 1D latent sequences to provide a compact latent representation and eliminate region redundancy.

Moreover, the tokenization strategy could help streamline image storage on blockchain platforms while delivering remarkable enhancements in processing speed.

Moreover, it boasts speeds up to 410 times faster than current technologies, which is a huge step forward in computational efficiency.

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Leveraging The Spot Ethereum ETFs Rally: Where Should You Put $1,000 To Make Millions? – NewsBTC

The Spot Ethereum ETFs are expected to spark a significant run in the crypto market. ETFSwap (ETFS) has emerged as a good option for crypto investors looking to turn their $1,000 investment into millions. This crypto analyst also made a case for Chainlink (LINK) as a good investment option.

Given its unique offerings, ETFSwap (ETFS) undoubtedly has the potential to make life-changing gains for crypto investors. ETFS is the native token of the decentralized investment platform ETFSwap, which allows investors to invest in both exchange-traded funds (ETFs) and cryptocurrencies in one place.

The trading platform tokenizes ETFs like the Spot Ethereum ETFs and enables them to be traded on-chain alongside crypto assets. ETFSwap (ETFS) acts as the bridge between traditional finance (TradFi) and decentralized finance (DeFi), allowing investors to easily diversify their portfolios. Users can convert their ETFSwap (ETFS) tokens to ETFs or other cryptocurrencies.

Getting started on ETSwap (ETFS) is as straightforward as possible. The trading platform has made its Know-Your-Customer (KYC) requirements non-mandatory so users can quickly start investing once they get on board. ETFSwaps (ETFS) decentralized nature also means that investors can access the platform from anywhere in the world without the limitations of traditional financial systems.

The user experience on ETFSwap (ETFS) is superior to that of centralized trading platforms. Investors can buy, sell, and trade their ETFs at any time thanks to the platforms 24/7 liquidity. Users are no longer limited in their options as they can choose to convert their ETF holdings to fiat or diversify to crypto assets.

ETFSwap (ETFS) also provides investors with real-time data and analytics to guide them on their investment journey. The ETF Screener contains all the information investors need to make the right investment decision. Meanwhile, the ETF Filter helps investors narrow down their options in order to choose the ETF that matches their investment goals.

Additionally, investors can be assured that their portfolios will always align with their investment goals. ETFSwap (ETFS) boasts an automated portfolio management feature that includes an automatic rebalancing feature, which helps to adjust an investors portfolio in line with their risk appetite.

Investing in ETFSwap (ETFS) is more cost-efficient, considering how minimal transaction fees are on the blockchain. The transparent nature of blockchain technology also means that investors can be sure there are no hidden charges on the trading platform.

Meanwhile, it is worth mentioning that crypto investors have already been accumulating the ETFSwap (ETFS) token at a discounted price of $0.01831 because of the additional benefits they enjoy by holding the crypto token. ETFS token holders get discounted trading fees on their ETF trades. Holders will have governance rights and be eligible for a share of the revenue earned on the platform.

In a recent video, crypto analyst Altcoin Buzz suggested that Chainlink (LINK) was a better investment option over Ethereum (ETH) for those looking to leverage the Spot Ethereum ETFs hype. These Spot Ethereum ETFs are expected to trigger a significant rally for ETH and other altcoins. Altcoin Buzz believes investors are better off investing $1,000 in Chainlink (LINK) than Ethereum (ETH) since it is more undervalued.

Meanwhile, the crypto analyst acknowledged the impact these Spot Ethereum ETFs would have on the Ethereum (ETH) ecosystem, given that new money is expected to flow in through these institutional investors. Altcoin Buzz also touched on the fact that these Spot Ethereum ETFs cannot stake their ETH tokens, suggesting that institutional investors might want to come on-chain to a platform like ETFSwap (ETFS) so they can invest in these Spot Ethereum ETFs and also stake their ETH tokens to earn yields.

ETFSwap (ETFS) stands out as a better investment opportunity, considering how it is way more undervalued compared to the likes of Ethereum (ETH) and Chainlink (LINK). ETFSwap (ETFS) is relatively new, which means it has more room to run than the others and will rise massively once the Spot Ethereum ETFs go live.

For more information about the ETFS Crypto Presale:

Visit ETFSwap Presale

Join The ETFSwap Community

Disclaimer:This is a paid release. The statements, views and opinions expressed in this column are solely those of the content provider and do not necessarily represent those of NewsBTC. NewsBTC does not guarantee the accuracy or timeliness of information available in such content. Do your research and invest at your own risk.

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$1 billion at stake! Ethereum dominates the crypto market in May! – Cointribune EN

4h00 3 min of reading by Eddy S.

In May 2024, the crypto sector experienced a wave of investments totaling 1 billion dollars, with Ethereum dominating the market. According to a report published by KuCoin Research, although this figure represents a slight decrease compared to April, it marks a 10.61% increase compared to May 2023.

The recent research highlights that projects related to Ethereum, Ethereum Virtual Machine (EVM)-compatible chains, and layer-2 (L2) networks continue to captivate the interest of institutional investors. These technologies offer scalable and versatile crypto platforms for decentralized applications, essential to the evolving blockchain ecosystem.

Despite the predominance of Ethereum-based investments, non-EVM chains such as Bitcoin, Solana, Fantom, and The Open Network (TON) have also maintained their popularity among crypto investors. With a total investment of 1.2 billion dollars in May, these networks are gaining traction due to their unique capabilities and growing ecosystems.

Due to concerns over the trend of low float and high fully diluted valuations (FDV) in the crypto market, many retail investors have turned to memecoins and celebrity-associated tokens. These assets, often launched with lower market caps and more accessible valuations, have provided an alternative to high valuation tokens with limited circulating supply.

Notcoin (NOT) is a remarkable example of this trend, as the token gained significant popularity in May. Unlike many tokens that gradually release their supply, Notcoin was launched with all tokens available from the start, which contributed to its rapid growth. On June 3, NOT became the fifth most traded crypto, surpassing the trading volume of the popular stablecoin USD Coin (USDC). Its price reached a new all-time high of $0.02896 on June 2, pushing its market cap above 2 billion dollars.

Crypto investments in May 2024 reflect the continued confidence of investors in Ethereum and associated technologies. And this, despite a slight decrease of 70 million dollars compared to the previous month. The future seems mixed but anything is possible in the cryptocurrency space.

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Le monde volue et l'adaptation est la meilleure arme pour survivre dans cet univers ondoyant. Community manager crypto la base, je m'intresse tout ce qui touche de prs ou de loin la blockchain et ses drivs. Dans l'optique de partager mon exprience et de faire connatre un domaine qui me passionne, rien de mieux que de rdiger des articles informatifs et dcontracts la fois.

DISCLAIMER

The views, thoughts, and opinions expressed in this article belong solely to the author, and should not be taken as investment advice. Do your own research before taking any investment decisions.

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Ethereum (ETH) Holders Accumulate, Anticipating Profits – BeInCrypto

Ethereum (ETH) price rise is on the cards, given the investors are exhibiting positive signs through their actions.

The question, however, is how far this potential rise can go before it comes to a halt.

Ethereums price may have a shot at climbing the charts soon as ETH holders continue to accumulate. The exchange net position change shows that outflows have dominated the market since the beginning of the month. In the past three weeks, over 1.1 million ETH worth over $3 billion have been moved out.

Outflows here do not point to money leaving the market but rather ETH leaving exchange wallets. This is a positive sign as it means one of two things: investors are buying ETH off exchanges or moving their holdings to cold wallets.

Since the market is not entirely bearish, it is likely the former case.

However, there is one hiccup in this potential rise, and that is how far it could go. Looking at the overall profit ratio, it appears that any rally that ETH notes will be short-lived. This is because over 90% of the supply is already in profit, and crossing 95% will establish a market top.

A market top refers to the peak price level reached by a financial market or asset before a decline or correction begins. It indicates a potential reversal in bullish momentum and suggests a potential downturn in prices ahead.

Read More: How to Invest in Ethereum ETFs?

Thus, as long as ETH does not hit that threshold, it could chart gains.

Ethereums price is struggling to flip the 50% Fibonacci Retracement level into a support floor. Marked at $3,582, this level would enable ETH to initiate recovery and reclaim the profits it recently lost.

The bullishness exhibited by the investors could push the price beyond this resistance and towards $3,700. Ideally, this would enable a run up to $3,830, which is equivalent to the 61.8% Fib level.

Read More: Ethereum (ETH) Price Prediction 2024/2025/2030

However, failure to secure $3,582 as support would make it difficult for Ethereums price to note any rise. The likely outcome will be a gradual decline to 38.2% Fib line at $3,336. Losing this support as well would invalidate the bullish thesis completely.

Disclaimer

In line with the Trust Project guidelines, this price analysis article is for informational purposes only and should not be considered financial or investment advice. BeInCrypto is committed to accurate, unbiased reporting, but market conditions are subject to change without notice. Always conduct your own research and consult with a professional before making any financial decisions. Please note that ourTerms and Conditions,Privacy Policy, andDisclaimershave been updated.

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Spot Ethereum ETF Launch Date Possibly On July 2: Bloomberg Analyst – Benzinga

Bloomberg ETF analyst Eric Balchunas moved up the launch date for the highly anticipated spot Ether ETF to July 2.

What Happened: This adjustment comes after the SEC staff sent light comments on the S-1 filings to issuers, asking for revisions within a week.

Decent chance they work to declare them effective the next week and get it off their plate before the holiday weekend. Anything is possible but this is our best guess as of now, Balchunas tweeted.

Issuers had been awaiting comments from the SECs Corporation Finance (Corp Fin) division, which received the S-1 filings two weeks prior.

According to Balchunas, Corp Fin only recently began reviewing these documents due to a last-minute shift in priorities, likely driven by political factors.

SEC Chair Gary Gensler previously indicated that approvals could come over the course of this summer, while Senator Bill Hagerty suggested they would be finalized by the end of the summer.

Also Read: Bitcoin Plunges Below $66K: These Indicators Show If Its Time To Use The Escape Hatch

Recent Market Movements And Sentiment

In parallel, the digital asset market has been experiencing significant movements.

According to CoinShares, the market saw outflows totaling $600 million, the largest since March 22, 2024.

The outflows were primarily driven by a hawkish Federal Open Market Committee (FOMC) meeting, which prompted investors to reduce exposure to fixed-supply assets like Bitcoin BTC/USD.

Bitcoin alone saw outflows of $621 million, while Ethereum ETH/USD, LIDO LIDO/USD, and XRP XRP/USD received inflows of $13 million, $2 million, and $1 million respectively.

Regionally, the United States experienced the bulk of the outflows, totaling $565 million.

Other regions like Canada, Switzerland, and Sweden also saw negative sentiment, with outflows of $15 million, $24 million, and $15 million respectively.

Germany bucked the trend with inflows of $17 million.

The bearish sentiment towards Bitcoin also led to $1.8 million in inflows into short-Bitcoin products.

Future Implications

The adjustment in the Ether ETF launch date could provide a new catalyst for market dynamics, potentially influencing investor behavior and market sentiment.

The anticipation around the launch highlights the increasing integration of digital assets into mainstream financial products and the evolving regulatory landscape.

For those looking to gain deeper insights into these developments and the future of digital assets, Benzingas Future of Digital Assets event on Nov. 19 will be a key platform.

Read Next: Paul Ryan Urges America To Embrace Stablecoins To Maintain Dollar Dominance

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Spot Ethereum ETF Launch Date Possibly On July 2: Bloomberg Analyst - Benzinga

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