Turning Neuromorphic Dynex Chips into Torch Layers We just finalised the Dynex Neuromorphic torch layer, which can be used in any NN model. Welcome to hybrid models, neuromorphic-, transfer- and federated-learning with @PyTorch

It's never too early to get into Dynex - credits to our community 😊

Mad Electron Engineering & @y3tiCrypto taking a look at the new Dynex SDK and PoUW 🔥🔥🔥

www.youtube.com

Deepminerz pool just released its integrated, Dynex job dashboard. Good job @y3tiCrypto, the community will love it!

pool.deepminerz.com

⚠️REMINDER: Ceasing Support for Old Miner Versions TODAY 13:00 UTC ⚠️ Remember: To allow for a smooth transition period, old miner versions will be still supported up until July 27th, 13:00 UTC. After that, old miners will not be able to connect to the Dynex Network any more. From this point onward, the entire Dynex network will support Ising/QUBO sampling, used for machine learning and other optimization tasks.

Flux Vs Dynex | Finding Scams

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via @YouTube

What a nice surprise! @BingXOfficial is listing $DNX:

bingx.com

COMMUNITY VOTE - HASHRATE DISTRIBUTION The community has decided: On 7/7/2023 we asked to vote on potential measurements: Most voted for an upper bound of 40% of the total network-hash-rate on pools. We appreciate your contributions and mindfulness and will take this majority vote as a decision. All pools are therefore required to implement measurements to limit their hash-rate share to a maximum of 40.00% of the overall network. Albeit pool operators are free to decide on how to enforce this requirement, we also have prepared code snippets which can be used for simplicity. The planned network upgrade on July 27th, 13:00 UTC is an optimal deadline for these measurements to be implemented. Healthy hash-rate distribution is a core requirement for any successful blockchain, we would like to thank everyone for being supportive and understanding.

SRB Miner v2.3.1 just released DynexSolve-ML support Updated algorithm 'dynex' to support DynexSolve 2.3.0 (new job types, FP64, etc.):

github.com

$DNX is now 2nd most mined coin on hiveOS stats - let‘s go!

We just made it in the top 500 on @CoinMarketCap - in just 10 months. We are extremely grateful to have such a wonderful, supportive and devoted #Dynex community! Thank you!

Testing the Dynex QBoost feature on 6,000 GPUs💪 Based on Google and D-Wave's paper, it ensembles a method to make binary classification amenable to quantum computing:

arxiv.org

Dynex Machine Learning Released ++ New Miner Version Released ++ Pools Upgraded ++ Ceasing Support for Old Miner Versions ++ Dynex SDK Today marks a major milestone in the development of Dynex. We are thrilled to announce that the Dynex Platform now supports machine learning tasks (DynexSolveML). Thousands of hours went into our massive platform upgrade. In a coordinated, joint effort over the last weeks, our science, research & development team was working tirelessly on making the Dynex SDK (beta) compatible with existing Quantum computing frameworks - making it possible to run originally developed algorithms for Quantum machines on our platform by simply using Python code. Without the limitations of scaling, error-correction and accessibility. The miner developers didn't spare any effort to incorporate and optimise the Dynex Ising/QUBO sampler into their miners and our pool operators upgraded their infrastructure to support the new miner versions. A big thank you at this point to everyone who contributed! $DNX #Dynex #Quantumcomputing #MachineLearning #ArtificialIntelligence #PoUW

Dynex Machine Learning - we are getting ready. With the Dynex #SDK is is possible to compute algorithms, which where originally designed for Quantum computers, directly on the Dynex Neuromorphic Platform - without the limitations of scalability, noise and accessibility. Stay tuned for our announcement.

Nice Sunday chat with @djwaters, thank you for your time! check out the recording:

youtu.be

Quantum Integer Factorization on the Dynex Platform 🔥 Identifying new methods for integer factorization plays an important role in modern information security. Shor’s algorithm is perhaps the most well-known method for integer factorization. An equally powerful model of quantum computing is the adiabatic quantum computing (AQC) model, which can also solve the integer factorization problem. In this example, we show how to convert an arbitrary integer factorization problem to an executable Ising model and tested it on the Dynex Neuromorphic Platform:

github.com

Breast Cancer Prediction on the Dynex Platform🧬 By applying a quantum feature selection algorithm on deep learning models, the feature space is being condensed and better model accuracy achieved. Check out our latest Dynex SDK example:

github.com

Does Dynex pass the Howey Test? Legal opinion suggests that Dynex will not be deemed as a security✅ Why is this important? Because it is favourable for DNX not to be qualified as a security. The law-firm we hired performed the Howey Test on Dynex and concluded, per their legal view, DNX should not be deemed as a security per the U.S federal securities laws.

Welcome new Dynex Website 🚀🚀 We are extremely excited to announce that our fresh new website design is finally live. Thank you @onuro and team for the hard work during the last month to make it happen! PS: there will still be some updates here and there, be gentle :)

Deep Learning: Quantum Boltzmann Machine (QBM) running on Dynex we just completed the next Python example code, the results are 🔥🔥 - Converges to optimal MSE after just 19(!) training epochs - Optimal MSE significantly better than traditional RBM trained over 500 epochs

github.com

Quantum & neuromorphic computing hold the potential to revolutionise the world by unlocking unprecedented computational power and enabling breakthroughs in fields such as optimization, cryptography, drug discovery, and machine learning.

Community AMA: 2023/06/27 20:30-21:30 (GMT+08:00) China Standard Time - Beijing Paopao invites you to participate in the Tencent Conference Conference theme: Papa-Dynex Questions and Answers Meeting time: 2023/06/27 20:30-21:30 (GMT+08:00) China Standard Time - Beijing Click the link to join the meeting, or add to the meeting list:

meeting.tencent.com

#TencentMeetings: 807-878-293 Copy this information, open Tencent Meeting on your mobile phone to participate ... 跑跑 邀请您参加腾讯会议 会议主题：跑跑-Dynex问题解答 会议时间：2023/06/27 20:30-21:30 (GMT+08:00) 中国标准时间 - 北京 点击链接入会，或添加至会议列表：

meeting.tencent.com

#腾讯会议：807-878-293 复制该信息，打开手机腾讯会议即可参与

Quantum & Neuromorphic computing models outperform traditional methods We ran simulations for the Standard Banknote Authentication dataset and measured the following Key Performing Indicators (KPIs): Accuracy: the fraction of samples that have been classified correctly Precision: proportion of correct positive identifications over all positive identifications Recall: proportion of correct positive identifications over all actual positives F1 score: harmonic mean of the model's precision and recall The attached summary shows that Quantum (D-Wave) and Neuromorphic (Dynex) based SVM model training is superior to traditional support vector machines. We used Scikit-learn's LIBSVM using Sequential Miinimal Optimisation as benchmark.

Quantum computing algorithms for machine learning harness the power of quantum mechanics to enhance ML. This applies also to Dynex neuromorphic computing - here is an example of a Support-Vector-Machine (SVM) Implementation in Python using the Dynex SDK:

github.com

Deep learning models often require extensive computational resources and time-consuming training processes. Dynex's ability to explore multiple possibilities simultaneously and perform parallel processing accelerates deep learning training, such as optimization and hyperparameter tuning, leading to faster convergence and more efficient training of deep neural networks.

Dynex: 150,000+ GPUs constitute one combined neuromorphic computing network using our proprietary PouW algorithm DynexSolve, providing an efficient Ising/QUBO sampler to customers. Simply unparalleled.

Quantum Tunneling and Superposition: D-Wave's quantum annealing approach takes advantage of quantum phenomena like quantum tunneling and superposition. It allows to traverse barriers and explore a wider solution space, increasing the likelihood of finding optimal solutions. Superposition allows the qubits to exist in multiple states simultaneously, enabling parallel processing and speeding up computation. Dynex provides a scalable alternative, based on #neuromorphic computing on hundreds of thousands GPUs.

$DNX is ranked #3 of all GPU mined coins with 7% market share🚀 Remember, please also join smaller pools - a healthy hash rate distribution should be <35% per pool. We appreciate your support!

We are making progress: significantly improved machine learning models and faster training. Dynex is empowering Decentralised Science 🧬

Using the Dynex Platform to determine optimal locations to build new electric vehicle charging stations💫

github.com

🤝 Partnership Announcement 🚀🔗 OctaSpace and Dynex are joining forces to revolutionize the AI/ML landscape! 🧠💡 Stay tuned for our upcoming announcement, as we unveil an innovative collaboration that harnesses the power of neuromorphic dynamics. Together, we'll accelerate solving discrete optimization, sampling, and machine learning problems. Dynex & OctaSpace: a partnership destined to potentially impact society as we know it.

Fresh from our lab💥We just folded the HIV virus RNA on the Dynex platform: 46.85s (versus 48.37s on the D-Wave quantum annealing machine). Not bad given that we used only on a small part of the Dynex network...

Dear DNX miners, please also join smaller pools! A healthy hash rate distribution should be <35% per pool. We appreciate your support!

And we are growing : DNX is the 3rd mined altcoin mined with GPU 🔥 7% of all GPUs are mining Dynex. Let's go! 🔥🔥

Dynex GUI Wallet v2.2.2(rev.B) released 💫 We are so excited to let you know that the updated version of our GUI wallet has just been released. You can easily build from source or use the release binaries, which is currently available for Ubuntu 22.04. Windows binary will follow shortly:

github.com

We just folded the HIV virus RNA on the D-Wave quantum annealing machine, one of the leading quantum computing companies. Impressive computation time of 48.37s. Very curious to see how that will compare to computing the same on the Dynex platform on 150+ Million Dynex chips...

Raw neuromorphic power: Just 8 months after launch we have more than 100,000 GPUs empowering the Dynex computing platform. It’s Time to Start Your Neuromorphic Journey!

New Dynex SDK Example: RNA FOLDING 💫 Finds the optimal stem configuration of the Tobacco Mild Green Mosaic Virus RNA sequence using the Dynex platform

github.com

Dynex SDK Example: Predicting Survival of Titanic Passengers:

github.com

Can DNX be considered a security in different jurisdictions? Our analysis suggest not *** The US Security and Exchange Commission has put the financial world on notice. With almost $400 million in liquidations, the last 24 hours showed no mercy to Bitcoin and altcoin traders. We reviewed the applicable regulations in multiple jurisdictions to assess the risk of DNX being considered a security. US regulators including SEC agree that Bitcoin isn't a security. It was started by an unknown person and does not exist as a way to raise money for a specific project. The UK's Financial Conduct Agency regulates digital assets it considers investments that come with rights to repayment or a share in profits, while 'payment tokens' like BTC or 'utility tokens' that provide access to a service are unregulated. Singapore considers coins that are digital representations of other assets to be securities. European lawmakers new common cryptocurrency legal framework is equally in line with the UK regulators. Looking at DNX: - An independent legal opinion from a reputable lawfirm classifies DNX to be a utility coin in all major jurisdictions; - As there was no ICO, no IEO, no pre-mining, no VC funding and no private sales, DNX does not exist as way to raise money for the project; - DNX provide customers access to run computations on the Dynex platform; and - having no staking mechanism, DNX is a pure Proof-of-Useful-Work mineable coin Considering the above we are confident that DNX is not at risk of being categorised as a security and remains a safe native level one coin for all participants.

Happy weekend Dynex family! Check out our new content on our website

The Dynex SDK & Machine Learning We've started providing content and resources to familiarise developers and data scientists with the upcoming approach. If you're interested in learning more, check out

dynexcoin.org

DynexSDK GitHub Repo in the works ✍️

github.com

is the Git repository to help users and customers build and run their computations on the Dynex platform. It is work in progress, examples and guides are being uploaded once they are available. Check from time to time for updates. The DynexSDK is also the interface for Dynex Machine Learning, which uses the Dynex Annealer at its core.

Fresh new design coming: Dynex Mobile Wallet ... stay tuned...

* DNX is now trading on MEXC * 💥 Happy trading!

twitter.com

Dynex & Python 🖥 As we are currently busy working on "Dynex Machine Learning", a full integration to compute on the Dynex Platform using Python is part of the release. The following example shows how to use Dynex to solve a Binary Quadratic Model. The Dynex Python SDK allows users to interact with the Dynex Annealer through Python programs. This python program introduces a Quadratic Model (QM) and submits it to the annealer to find the minimum energy value for that model. You will find these functions also being part of Machine Learning.

Dynex GUI Wallet update sneak preview 💥 Making good progress! Expect new features like: ✅ import/export from mnemonic passphrase ✅ export/import from keys ✅ setting tx fees ✅ save paymentID in address book ✅ choosing network card and more

Just a little more than 20 hours left for the 50,000$ DNX airdrop ⚡

Listing announcement: $DNX will start trading on MEXC on June 6th, 12:00 (UTC) 💫💫💫