Throughout history, there have various iterations of sound money, from the Rai stones of the Yap islands to the gold standard.However, sound money has remained elusive over the last century following the proliferation of credit expansion through central banking fractional-reserve policies that lead to endemic inflation.Sound money is consistently touted as a necessary prerequisite to a prosperous society and a stable price mechanism in free market economies by the Austrian School of…
In this post, we examine applications of deep learning to three key biomedical problems: patient classification, fundamental biological processes, and treatment of patients. The objective is to predict whether deep learning will transform these tasks.
The post is based on the very comprehensive paper “Opportunities and obstacles for deep learning in biology and medicine”.
The paper places a high bar i.e. on the lines of Andy Grove’s inflection point to refer to a change in technologies or environment that requires a business to be fundamentally reshaped.
The three classes of applications are described as follows:
Disease and patient categorization: the accurate classification of diseases and disease subtypes.
Time series forecasting is a very fascinating task. However, build a machine-learning algorithm to predict future data is trickier than expected. The hardest thing to handle is the temporal dependency present in the data. By their nature, time-series data are subject to shifts. This may result in temporal drifts of various kinds which may become our algorithm inaccurate.
One of the best tips I recommend, when modeling a time series problem, is to stay simple. Most of the time the simpler solutions are the best ones in terms of accuracy and adaptability. They are also easier to maintain or embed and more persistent to possible data shifts. In this sense, the gold standard for time series modeling consists in the adoption of linear-based algorithms.
For one- or two-semester business statistics courses. Not a new book, but a popular one (8th edition.)
This text is the gold standard for learning how to use Excel in business statistics, helping students gain the understanding they need to be successful in their careers. The authors present statistics in the context of specific business fields; full chapters on business analytics further prepare students for success in their professions. Current data throughout the text lets students practice analyzing the types of data they will see in their professions. The friendly writing style include tips throughout to encourage learning.
The book also integrates PHStat, an add-in that bolsters the statistical functions of Excel.… Read more...
Cryptography today is based on veteran public key systems:
– Diffie-Hellman (1976).
– RSA (1977)
– Elliptical Curves (1985).
Elliptic curve cryptography is the most advanced cryptographic system available. Elliptic curves are rapidly replacing RSA as the gold standard for public key cryptography.
But all of them are more than 30 years old, and in fact the Elliptical Curves one has started to be implanted in a massive way now. Over time, we should see these systems in decline.
This in the case of symmetric cryptography and hash functions should theoretically be revised (at least duplicated) to prevent the effect of quantum computers. Although the time frame for this to happen is probably much longer than what is being published.
Los US poseían hasta principios de los 70 hasta dos tercios del oro mundial, como resultado de su política de tener al dólar respaldado. Eso lo hacía la base del sistema financiero, con un cambio estable dólar-oro de $35.
Todo lo demás estaba indexado al dólar y esto permitía a América pagar cualquier cosa al extranjero imprimiendo dólares. Con el tiempo esto hizo que el papel fuera valiendo cada vez menos, hasta que llegó un momento en el que los gobiernos extranjeros terminaron hartos.
En 1965 De Gaulle tomó 150 millones en dólares de reservas francesas y demandó su pago a América en oro, como estaba en su derecho de hacer. Al poco España hizo lo propio con otros 60$ millones, y luego siguieron muchos otros.
Aquello se tomó como un ataque al dólar en su momento pero era justo de hacer.… Read more...