Public-key cryptography, or asymmetric cryptography, is the field of cryptographic systems that use pairs of related keys. Each key pair consists of a public key and a corresponding private key. Key pairs are generated with cryptographic algorithms based on mathematical problems termed one-way functions. Security of public-key cryptography depends on keeping the private key secret; the public key can be openly distributed without compromising security.In a public-key encryption system, anyone with a public key can encrypt a message, yielding a ciphertext, but only those who know the corresponding private key can decrypt the ciphertext to obtain the original message.For example, a journalist can publish the public key of an encryption key pair on a web site so that sources can send secret messages to the news organization in ciphertext. Only the journalist who knows the corresponding private key can decrypt the ciphertexts to obtain the sources' messages—an eavesdropper reading email on its way to the journalist cannot decrypt the ciphertexts. However, public-key encryption does not conceal metadata...

The Pickelhaube (pl. Pickelhauben; from German: Pickel, lit. 'point' or 'pickaxe', and Haube, lit. 'bonnet', a general word for "headgear"), also Pickelhelm, is a spiked leather or metal helmet that was worn in the 19th and 20th centuries by Prussian and German military officers, firefighters and police. Although it is typically associated with the Prussian Army, which adopted it in 1842–43, the helmet was widely imitated by other armies during that period. It is still worn today as part of ceremonial wear in the militaries of certain countries, such as Sweden, Chile, and Colombia. History Origins The Pickelhaube was originally designed in 1842 by King Frederick William IV of Prussia, perhaps as a copy of similar helmets that were adopted at the same...

A neural network can refer to either a neural circuit of biological neurons (sometimes also called a biological neural network), or a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information. Overview A biological neural network is composed...