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🔗 Civil engineering

A green wave occurs when a series of traffic lights (usually three or more) are coordinated to allow continuous traffic flow over several intersections in one main direction.

Any vehicle travelling along with the green wave (at an approximate speed decided upon by the traffic engineers) will see a progressive cascade of green lights, and not have to stop at intersections. This allows higher traffic loads, and reduces noise and energy use (because less acceleration and braking is needed). In practical use, only a group of cars (known as a "platoon", the size of which is defined by the signal times) can use the green wave before the time band is interrupted to give way to other traffic flows.

The coordination of the signals is sometimes done dynamically, according to sensor data of currently existing traffic flows - otherwise it is done statically, by the use of timers. Under certain circumstances, green waves can be interwoven with each other, but this increases their complexity and reduces usability, so in conventional set-ups only the roads and directions with the heaviest loads get this preferential treatment.

In 2011, a study modeled the implementation of green waves during the night in a busy Manchester suburb (Chorlton-cum-Hardy) using S-Paramics microsimulation and the AIRE emissions module. The results showed using green wave signal setups on a network have the potential to:

• Reduce CO₂, NO_x and PM₁₀ emissions from traffic.
• Reduce fuel consumption of vehicles.
• Be used on roads that intersect with other green waves.
• Reduce the time cars wait at side roads.
• Give pedestrians more time to cross at crossings and help them to cross streets as vehicles travel in platoons
• Control the speed of traffic in urban areas.
• Reduce component wear of vehicles and indirect energy consumption through their manufacture

A green wave in both directions may be possible with different speed recommendations for each direction, otherwise traffic coming from one direction may reach the traffic light faster than from the other direction if the distance from the previous traffic light is not mathematically a multiple of the opposite direction. Alternatively a dual carriageway may be suitable for green waves in both directions if there is sufficient space in the central reservation to allow pedestrians to wait and separate pedestrian crossing stages for each side of the road.

Green waves are sometimes used to facilitate bicycle traffic. Copenhagen, Amsterdam, San Francisco, and other cities may synchronize traffic signals to provide a green light for a flow of cyclists. On San Francisco's Valencia Street, the signals were retimed in early 2009 to provide a green wave in both directions, possibly the first street in the world with a two-way green wave for cyclists. In Copenhagen, a green wave on the arterial street Nørrebrogade facilitates 30,000 cyclists to maintain a 12 mph (19.3 km/h) speed for 2.5 kilometers. In Amsterdam, cyclists riding at a speed of 15 to 18 km/h will be able to travel without being stopped by a red signal. Tests show that public transport can benefit as well and cars may travel slightly slower.

In Vienna, Austria a stretch of cycle path on Lassellestrasse in the 2nd district has a display that tells cyclists their speed and the speed they must maintain to make the next green light.

Frederiksberg, a part of Copenhagen, the capital of Denmark, has implemented a green wave for emergency vehicles to improve the public services.

In the UK, in 2009, it was revealed that the Department for Transport had previously discouraged green waves as they reduced fuel usage, and thus less revenue was raised from fuel taxes. Despite this government Webtag documents were only updated in 2011. It is still unclear if the economic appraisal software used to apply these guidelines has also been updated and if the new guidelines are being applied to new projects.

In a more limited sense, the term Green wave has also been applied to railroad travel. For several years starting in the 1960s, the German Federal Railway maintained an advertising campaign featuring the slogan garantiert grüne Welle (Guaranteed Green Wave), which communicated the notion of speed, limited delays and open track blocks to potential customers choosing between train and automobile travel, and was featured prominently in promotional materials ranging from posters to radio jingles.

🔗 United States 🔗 Companies 🔗 Telecommunications 🔗 United States/Colorado

Qwest Communications International, Inc. was a large United States telecommunications carrier. Qwest provided local service in 14 western and midwestern U.S. states: Arizona, Colorado, Idaho, Iowa, Minnesota, Montana, Nebraska, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming.

On April 22, 2010, CenturyLink announced it would acquire Qwest in a stock transaction. The merger closed on April 1, 2011. Qwest began doing business as CenturyLink in August 2011.

Qwest provided voice, Internet backbone data services, and digital television in some areas. It operated in three segments: Wireline Services, Wireless Services, and Other Services. The Wireline Services segment provided local voice, long-distance voice, and data and Internet (DSL) services to consumers, businesses, and wholesale customers, as well as access services to wholesale customers. The Wireless Services segment was achieved by a partnership with Verizon Wireless. Qwest also partnered with DirecTV to provide digital television service to its customers. In Phoenix, Denver, Salt Lake City, Boise, and Omaha, Qwest offered Qwest Choice TV (later also known as Qwest Digital Television), an IPTV service over DSL. This service was retired in October 2008 (after being no longer available to new customers in May 2008), leaving DirecTV as the only TV service Qwest provided. Qwest Choice TV customers were moved to DirecTV. The Other Services segment primarily involved the sublease of real estate assets, such as space in office buildings, warehouses, and other properties.

Qwest Communications also provided long-distance services and broadband data, as well as voice and video communications globally. The company sold its products and services to small businesses, governmental entities, and public and private educational institutions through various channels, including direct-sales marketing, telemarketing, arrangements with third-party agents, company's Web site, and partnership relations. As of September 13, 2005, Qwest had 98 retail stores in 14 states. Qwest Communications was headquartered in Denver, Colorado at 1801 California Street, in the second tallest building in Denver at 53 stories. The majority of Qwest occupational or non-management employees were represented by two labor unions; the Communications Workers of America and in Montana, the International Brotherhood of Electrical Workers. Qwest also had software development centers in Bangalore and Noida (New Delhi), India called Qwest Software Services.

🔗 United States 🔗 United States/Washington - Seattle 🔗 United States/Washington

Capitol Hill's mystery soda machine was a Coke vending machine in Capitol Hill, Seattle, that was in operation since at least the early 1990s until its disappearance in 2018. It is unknown who stocked the machine.

🔗 Computing

The Texas Instruments LPC Speech Chips are a series of speech synthesizer digital signal processor integrated circuits created by Texas Instruments beginning in 1978. They continued to be developed and marketed for many years, though the speech department moved around several times within TI until finally dissolving in late 2001. The rights to the speech-specific subset of the MSP line, the last remaining line of TI speech products as of 2001, were sold to Sensory, Inc. in October 2001.

🔗 Soviet Union 🔗 Socialism 🔗 Latvia 🔗 Estonia 🔗 Soviet Union/history of Russia 🔗 Soviet Union/Russia 🔗 Lithuania

The Baltic Way or Baltic Chain (also Chain of Freedom; Estonian: Balti kett; Latvian: Baltijas ceļš; Lithuanian: Baltijos kelias; Russian: Балтийский путь Baltiysky put) was a peaceful political demonstration that occurred on 23 August 1989. Approximately two million people joined their hands to form a human chain spanning 675.5 kilometres (419.7 mi) across the three Baltic states – Estonia, Latvia, and Lithuania, which were considered at the time to be constituent republics of the Soviet Union.

The demonstration originated in "Black Ribbon Day" protests held in the western cities in the 1980s. It marked the 50th anniversary of the Molotov–Ribbentrop Pact between the Soviet Union and Nazi Germany. The pact and its secret protocols divided Eastern Europe into spheres of influence and led to the occupation of the Baltic states in 1940. The event was organised by Baltic pro-independence movements: Rahvarinne of Estonia, the Tautas fronte of Latvia, and Sąjūdis of Lithuania. The protest was designed to draw global attention by demonstrating a popular desire for independence and showcasing solidarity among the three nations. It has been described as an effective publicity campaign, and an emotionally captivating and visually stunning scene. The event presented an opportunity for the Baltic activists to publicise the Soviet rule and position the question of Baltic independence not only as a political matter, but also as a moral issue. The Soviet authorities responded to the event with intense rhetoric, but failed to take any constructive actions that could bridge the widening gap between the Baltic republics and the rest of the Soviet Union. Within seven months of the protest, Lithuania became the first of the Soviet republics to declare independence.

After the Revolutions of 1989, 23 August has become an official remembrance day both in the Baltic countries, in the European Union and in other countries, known as the Black Ribbon Day or as the European Day of Remembrance for Victims of Stalinism and Nazism.

In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which a probability distribution is represented as an element of a reproducing kernel Hilbert space (RKHS). A generalization of the individual data-point feature mapping done in classical kernel methods, the embedding of distributions into infinite-dimensional feature spaces can preserve all of the statistical features of arbitrary distributions, while allowing one to compare and manipulate distributions using Hilbert space operations such as inner products, distances, projections, linear transformations, and spectral analysis. This learning framework is very general and can be applied to distributions over any space ${\displaystyle \Omega }$ on which a sensible kernel function (measuring similarity between elements of ${\displaystyle \Omega }$) may be defined. For example, various kernels have been proposed for learning from data which are: vectors in ${\displaystyle \mathbb {R} ^{d}}$, discrete classes/categories, strings, graphs/networks, images, time series, manifolds, dynamical systems, and other structured objects. The theory behind kernel embeddings of distributions has been primarily developed by Alex Smola, Le Song , Arthur Gretton, and Bernhard Schölkopf. A review of recent works on kernel embedding of distributions can be found in.

The analysis of distributions is fundamental in machine learning and statistics, and many algorithms in these fields rely on information theoretic approaches such as entropy, mutual information, or Kullback–Leibler divergence. However, to estimate these quantities, one must first either perform density estimation, or employ sophisticated space-partitioning/bias-correction strategies which are typically infeasible for high-dimensional data. Commonly, methods for modeling complex distributions rely on parametric assumptions that may be unfounded or computationally challenging (e.g. Gaussian mixture models), while nonparametric methods like kernel density estimation (Note: the smoothing kernels in this context have a different interpretation than the kernels discussed here) or characteristic function representation (via the Fourier transform of the distribution) break down in high-dimensional settings.

Methods based on the kernel embedding of distributions sidestep these problems and also possess the following advantages:

1. Data may be modeled without restrictive assumptions about the form of the distributions and relationships between variables
2. Intermediate density estimation is not needed
3. Practitioners may specify the properties of a distribution most relevant for their problem (incorporating prior knowledge via choice of the kernel)
4. If a characteristic kernel is used, then the embedding can uniquely preserve all information about a distribution, while thanks to the kernel trick, computations on the potentially infinite-dimensional RKHS can be implemented in practice as simple Gram matrix operations
5. Dimensionality-independent rates of convergence for the empirical kernel mean (estimated using samples from the distribution) to the kernel embedding of the true underlying distribution can be proven.
6. Learning algorithms based on this framework exhibit good generalization ability and finite sample convergence, while often being simpler and more effective than information theoretic methods

Thus, learning via the kernel embedding of distributions offers a principled drop-in replacement for information theoretic approaches and is a framework which not only subsumes many popular methods in machine learning and statistics as special cases, but also can lead to entirely new learning algorithms.

🔗 Time 🔗 Writing

Kairos (Ancient Greek: καιρός) is an Ancient Greek word meaning the right, critical, or opportune moment. The ancient Greeks had two words for time: chronos (χρόνος) and kairos. The former refers to chronological or sequential time, while the latter signifies a proper or opportune time for action. While chronos is quantitative, kairos has a qualitative, permanent nature. Kairos also means weather in Modern Greek. The plural, καιροί (kairoi (Ancient and Modern Greek)) means the times. Kairos is a term, idea, and practice that has been applied in several fields including classical rhetoric, modern rhetoric, digital media, Christian theology, and science.

🔗 Military history/United States military history

🔗 Novels

I, Libertine is a literary hoax novel that began as a practical joke by late-night radio raconteur Jean Shepherd who aimed to lampoon the process of determining best-selling books. After generating substantial attention for a novel that didn't actually exist, Shepherd approved a 1956 edition of the book written mainly by Theodore Sturgeon — which became an actual best-seller, with all profits donated to charity.