GMUBS Mining explained that the biggest risk of Ethereum mining is that after the Ethereum network switches to PoS, mining will cease to exist. By the time ETH 2.0 arrives, users can deposit their ETH holdings into nodes for rewards. That is, ETH 2.0 will replace PoW with PoS. At that point, GPU miners may no longer be able to mine Ethereum and need to be re-adapted to other tokens or other uses.

With ETH prices surging, the risk of a price correction is high. Some miners believe that ETH is growing too fast, and once the price pulls back, mining profits will plummet. To deal with such risks, users can hedge their ETH holdings at high prices to obtain a certain return. For risk-averse users, GMUBS usually recommends hedging mining earnings for 3-6 months to recover most of the cost, and then staking subsequent profits for higher returns.

Both of the above factors are objective and should be considered in conjunction with your investment preferences and market insight.

As a non-standard device, GPU mining machines are difficult to operate and maintain. For mining farms, GPU mining has high requirements for operation and maintenance. Therefore, miners need to look for farms that are experienced in this area. Also, they should find a reliable GPU miner manufacturer to source a good GPU with sufficient hash rate. Additionally, the superior GPU facilitates smooth operation and maintenance and ensures standard hash rate performance.

GMUBS – Mining software for eth miners

Harley Mining, the official partner of GMUBS, started as a mining farm in 2014 and has extensive experience in building, operating and maintaining mining machines, especially those focused on GPU mining. Given that GPU mining has higher requirements for facilities, personnel, repairs and maintenance, thanks to the cooperation with big-name manufacturers, GMUBS has many advantages in GPU hosting. At present, Harley Miner manages GPU miners in order to do a good job in operation and maintenance, mainly based on five indicators: operation rate, mining machine compliance rate, online computing power performance, total computing power performance, and theoretical total computing power performance. In terms of maintenance, GMUBS will set up a professional maintenance team on each farm. In addition, we have set up a maintenance office at GMUBS Shenzhen headquarters to ensure timely disposal of damaged rigs. In short, with the hosting service of GMUBS, minor repairs and repairs on the farm can be completed immediately; major repairs are handled at the GMUBS headquarters, professional support, cost-effective, and higher stability.

If you are interested in GPU mining, please contact GMUBS and we will help you develop a partnership plan that fits your budget to seize the opportunity of GPU mining.

Dr. Jonathan Kenigson, FRSA

C. R. Rao laid the foundation for modern Information Geometry in 1945 while a research student at Andhra and Calcutta – a task that involved the employment of Manifold Theory in conditions of divergence and entropy. The initial ideas employed in the paradigm involve: (1). Positive-Definite Information Matrices as Norms on Function Spaces; (2). Function Spaces that are restricted to the context(s) of spaces of manifolds of Distribution Functions. In particular, (2) assumes the form of spaces of Normal Distributions, whose global geometry is isomorphic to the “Upper-Half-Plane” Model of Hyperbolic Geometry with the classical Poincaré metric. In a “qualitative” sense, the distinction between two distributions may be interpreted as a proportion of the difference of their relative Information Entropies (IE). This interpretation lends itself well to modern computation and AI because such entropies, while long-known, have just begun to be explored in Quantum contexts. Information from network topologies and architectures explored from categorical contexts are devoid of this expression. In the former paradigm, entropy cannot be quantified in an integrable sense because of the lack of a measure-theoretic expression of the relevant categorical quantities. The Riemannian structure of the Information Manifold, however, renders it a Topological Space, and, more particularly, a “Metric Space,” whose distance function is positive-definite and derived from the concavity conditions implied in the “Fisher-Rao Information Matrix.” Any Information Manifold also possesses a “coherent” integral structure whose “Lebesgue-Measurable” sets are determined canonically by the “Ring of Outer Measures” on the manifold. The resulting Lebesgue Integral has properties that permit efficient computation as guaranteed by abstract Measure Theory – namely, arbitrary pointwise approximation by binary step functions and strong restrictions on information convergence demanded by such results as Fatou’s Lemma and the Lebesgue Dominated Convergence Theorem.

Entropies that generalize the Rényi entropy (like the Kullback-Leibler Divergence), may be constructed on the tangent spaces of an Information Manifold as Surface Integrals. The resulting paradigm permits novel applications of Ergodic metrization. Categorical and Measure-Theoretic notions may be introduced induced through integral Prokhorov metrization and then rendered algebraically by the introduction of Markov Transition Kernels between the manifolds. This approach retains the rich geometry of Rao’s paradigm without sacrificing the expediency and applicability of Categorical notions of transition functions. Given a countable sequence of random variables on a probability space (that are not necessarily identically distributed), one can employ Fisher-Rao theory to construct a sequence of Information Manifolds in the metric tensor defined by the Fisher Information. This manifold sequence is associated with a sequence of spaces of probability measures defined on the Borel subsets induced by each metric. Each of these is capable of Prokhorov Metrization. We introduce a modern measure theory to rigorously define transition kernels between the Fisher-Rao Manifolds, associating a generalized Markov process with each transition.

I present herein a paradigm for this attempt that could permit generalization of existing results to the study of Information Manifolds generated in Quantum Computing – in which, among other differences, the underlying manifolds are not comprised of Normal Distributions but of Wavefunctions from some underlying computational process that generates functional data capable of at least positive-semidefinite metrization in the manner of a Riemannian Manifold. Mathematically, the generalized Kolmogorov Central Limit Theorems are sufficiently robust to permit strong statements about Convergence in Law that generalize the classical CLT to situations in which the underlying random variables are not Independent and Identically Distributed (IID). Relevant resources for each step of the process are presented below. Initially, one begins with the notion of probability as a derivation on the Lebesgue Measures. Even though it is quite old, the Kolmogorov framework is still extremely useful. The best reference for this work available in modern translation is likely still Andrei Nikolajevich Kolmogorov’s 1950 masterwork, Foundations of the Theory of Probability (In Russian). The properties of a random variable on a measurable space are lucidly explored by Bert Fristedt and Lawrence Gray’s 1996 text, A modern approach to probability theory published by Birkhäuser in Boston. Convergence results should encapsulate the measure-theoretic properties of the base space in Lebesgue terms so that a general Smooth Orientable Manifold can be created from local spaces. It is the opinion of the current author that the most lucid account of convergence in such generality is furnished in Billingsley, Patrick (1999). Convergence of probability measures. 2nd ed. John Wiley & Sons. pp.1-28. Once an Information Manifold with a given metric tensor exists, one may set about to explore various divergences induced by: (1). Fisher Information and (2). Quasi-Canonical divergences that exist on the tangent spaces, like the Kullback-Leibler Divergence. It is the current author’s opinion that the best resources devoted to this approach are Nagaoka Hiroshi’s 2000 monograph, Methods of information geometry, Translations of mathematical monographs; v. 191, and Shun’ichi Amari’s 1985 introductory applied text Differential-geometrical methods in statistics published by Springer-Verlag in Berlin. Metrization of the spaces of Borel measures on a sequence of Information Manifolds is possible using so-called Prokhorov Processes. Billingsley’s (1999) treatment is particularly salient because convergence on Riemannian Manifolds also induces convergence on sequences of measures, which are themselves defined on the underlying rings of Sigma Algebras for each manifold’s integral structure.

More abstractly, the current author finds that the Fisher Information can be defined on the tangent space S of arbitrary Radon measures using the Radon-Nikodym Theorem. The clearest and most concise existing resource for this treatment may be found in Mitsuhiro Ito and Yuichi Shishido’s 2008 article, “Fisher information metric and Poisson kernels” in the journal Differential Geometry and Its Applications. 26 (4): 347 – 356. The notion of “Poisson Kernel” is intended in the singular sense of a classical Normal Distribution rather than the sense of Kernel-Superpositions that arise in (for instance) Jacobi Theta representations of pure imaginary arguments. From this vantage, one defines an arbitrary f-Divergence on each Statistical Manifold S(X) in the manner of Coeurjolly, J-F. & Drouilhet, R. (2006). “Normalized information-based divergences”.arXiv:math/0604246. A Categorical view of the Markov Kernels and their transition states may be derived intuitively or read from F. W. Lawvere (1962). “The Category of Probabilistic Mappings” by any available publisher. This resource is free in the public domain in the USA. Further submissions will further elucidate the details of the proposed paradigm.

Structured data has become essential in a modern SEO strategy. They are increasingly important following the almost daily evolution of search engines like Google in their way of collecting and presenting information on the web. 

How about discovering a way to take up more space in search engine results pages? This is possible through the use of structured data.

The competition is always tougher on Google, a study reveals that 64% of marketers are actively working on the SEO of their site. It is therefore necessary to keep abreast of new developments and, above all, to put in place the best practices of the moment to gain visibility. And some of them are particularly effective. Remember that structured data makes it easier for google to organized search to find and understand the content on your site and assess its relevance. 

Bruce jones seo will help you modify your website through their Structured Data Services that bring your website SEO to the next level. In this article you will discover the interest of this structured data for SEO and how to use it to drastically improve your SEO. 

Why use structured data in SEO?

During your Internet browsing, you have certainly already dealt with structured data, via the rich results of search engines. For example, the little stars that tell you the rating of Internet users, or the stock availability of a product or its price, directly displayed in the SERPs, without having to click on the page to get this information.

As search engines continue to evolve with each new update, integrating voice search, voice assistants and mobile search, your website could quickly find itself overtaken by events if you do not use structured data. . 

Clearer and more understandable content in the eyes of Google means that you are already more likely to be well indexed and positioned on the SERPs than your competitors who do not pay much attention to this point.

Add to that an improved visual presentation of your results (images, reviews, product information, etc.) and you are much closer to achieving your goal of driving more traffic to your site.

Thanks to the new way Google works and the development of the semantic web (a vision of the web where machines are able to fully understand the true meaning of data and the relationships between them to return better information in less time) structured data can help you get quick SEO wins with less effort. 

Indeed, having quality structure and content behind your website can help you show up on search results that you aren’t even targeting. Search engines try to give a better flow of information, and offer the best response to the search intent.

This means they search for information more intelligently across many more sources and can identify logical connections between different content. This is what allows Google to present more and more rich results on the SERPs in an effort to provide a better experience to its users.

You too can get a spot among those particular results by optimizing your pages with the right structured data services.