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Digital subscriber line (DSL; originally digital subscriber loop) is a family of technologies that are used to transmit digital data over telephone lines. In telecommunications marketing, the term DSL is widely understood to mean asymmetric digital subscriber line (ADSL), the most commonly installed DSL technology, for Internet access. DSL service can be delivered simultaneously with wired telephone service on the same telephone line since DSL uses higher frequency bands for data.
In software architecture, publish–subscribe is a messaging pattern where publishers categorize messages into classes that are received by subscribers. This is contrasted to the typical messaging pattern model where publishers sends messages directly to a subscribers. Similarly, subscribers express interest in one or more classes and only receive messages that are of interest, without knowledge of which publishers, if any, there are. Publish–subscribe is a sibling of the message queue paradigm, and is typically one part of a larger message-oriented middleware system.
Very high-speed digital subscriber line (VDSL) and very high-speed digital subscriber line 2 (VDSL2) are digital subscriber line (DSL) technologies providing data transmission faster than the earlier standards of asymmetric digital subscriber line (ADSL) G.992.1, G.992.3 (ADSL2) and G.992.5 (ADSL2+). VDSL offers speeds of up to 52 Mbit/s downstream and 16 Mbit/s upstream, over a single twisted pair of copper wires using the frequency band from 25 kHz to 12 MHz.
In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and distributed data processing. Stream processing systems aim to expose parallel processing for data streams and rely on streaming algorithms for efficient implementation.
Functional reactive programming (FRP) is a programming paradigm for reactive programming (asynchronous dataflow programming) using the building blocks of functional programming (e.g. map, reduce, filter). FRP has been used for programming graphical user interfaces (GUIs), robotics, games, and music, aiming to simplify these problems by explicitly modeling time. The original formulation of functional reactive programming can be found in the ICFP 97 paper Functional Reactive Animation by Conal Elliott and Paul Hudak.
Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.