For All Time (Extended Mix) Fixed
A remix album is an album consisting of remixes or rerecorded versions of an artist's earlier released material. The first act who employed the format was American singer-songwriter Harry Nilsson (Aerial Pandemonium Ballet, 1971). As of 2007, the best-selling remix album of all time is Michael Jackson's Blood on the Dance Floor: HIStory in the Mix (1997).COLABORATION Vol.1 Sahrul Projectt
For All Time (Extended Mix)
Aerial Pandemonium Ballet (1971) by Harry Nilsson is credited as the first remix album. It was released after the successes of "Everybody's Talkin'" and The Point!, when he decided that his older material had started to sound dated. Neu!'s Neu! 2 (1973) has also been described as "in effect the first remix album", as many tracks see the duo "speed up, slow down, cut, doctor, and mutilate the material, sometimes beyond recognition".
In the 1980s, record companies would combine several kinds of electronic dance music, such as dance-pop, house, techno, trance, drum and bass, dubstep, hardstyle, and trap into full-length albums, creating a relatively low-overhead addition to the catalogs and balance sheets. Soft Cell's Non Stop Ecstatic Dancing (1981) and The Human League's Love and Dancing (1982) are credited for inventing the modern remix album. Since this time, this kind of release is not only seen as an easy cash-in for an artist and their label, but also as an opportunity to provide a second lease of life for a record. In the world of reggae music, it is not uncommon for a whole album to be remixed in a dub style.
But with the series coming out, it may be the perfect time for those who having been putting off watching The Lord of the Rings trilogy to finally see what every fantasy fan has been talking about for almost 70 years since the first book was released (1954). And to be honest, as sacrilegious as it may be to super fans, the theatrical version is a much better choice for those who are not familiar to the story, characters, or world.
In addition to making a first-time watch more suspenseful, the theatrical version cuts out less important details to help make the story and its characters more digestible and understandable. Once first-time viewers have seen the theatrical versions, if they like them and want to find out more, they could watch the extended versions. However, there are a few changes from book-to-movie that if someone wants to know about, they can read the books.
If a viewer really loves a movie, then why not have more of that movie and those characters they really love? Fans of the story and its characters know how perfectly cast the movies were, so seeing some of the extra scenes where more information was shared about a side character in the extended version is worth the extra time. But again, the theatrical version is a better introduction to Middle Earth.
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With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this setting, outlier detection is frequently important, and while solutions based on neural networks exist, they leave room for improvement in terms of both accuracy and efficiency. With the objective of achieving such improvements, we propose a diversity-driven, convolutional ensemble. To improve accuracy, the ensemble employs multiple basic outlier detection models built on convolutional sequence-to-sequence autoencoders that can capture temporal dependencies in time series. Further, a novel diversity-driven training method maintains diversity among the basic models, with the aim of improving the ensemble's accuracy. To improve efficiency, the approach enables a high degree of parallelism during training. In addition, it is able to transfer some model parameters from one basic model to another, which reduces training time. We report on extensive experiments using real-world multivariate time series that offer insight into the design choices underlying the new approach and offer evidence that it is capable of improved accuracy and efficiency. This is an extended version of "Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles", to appear in PVLDB 2022.
Imagine if you will, early settlements with concentric rings depicting inputs and outputs required to sustain their inhabitants. The rings would be, for the most part, overlapping at the boundary of the settlement, as these places were totally dependent upon local resources and the necessity of managing outputs locally. As time, growth and innovation marched forward, however, inputs began to be sourced from farther away and outputs, whether intended or not, began to disperse across greater distances as well. The radii of concentric rings now travel up and out to such great distances that they intersect with rings in other countries, far surpassing local sourcing and sinking capacities. The carrying capacity of local environments is no longer a limiting factor to growth, and the unseen and unsensed dispersements have led to blind spots in how we plan and design cities with rather ubiquitous qualities and patterns today.
The nature-health component of livable cities is a human centric model that can seem to describe nature as an amenity solely for our benefit. Nature, at times and in limited applications, may be nothing more than window dressing for the sake of health benefit or productivity, but the aspiration should be to think of the multi-faceted benefits of healthy natural ecosystems integrated within cities. 041b061a72