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Tooby was known for his work with his collaborators to integrate cognitive science, cultural anthropology, evolutionary biology, paleoanthropology, cognitive neuroscience, and hunter-gatherer studies to create the new field of evolutionary psychology, toward the goal of the progressive mapping of the universal evolved cognitive and neural architecture that constitutes human nature and provides the basis of the learning mechanisms responsible for culture. This involves using knowledge of specific adaptive problems our hunter-gatherer ancestors encountered to experimentally map the design of the cognitive and emotional mechanisms that evolved among our hominid ancestors to solve them.

On the July 11 episode of Raw, Edge's match with Kane was interrupted when Matt Hardy made a surprise appearance. Hardy referred to Edge by his real name and issued a threat to Lita as well.[93] When Hardy was officially brought back to Raw, he and Edge continued their feud, including a match at SummerSlam on August 21 where Edge defeated Hardy, causing Hardy to have excessive blood loss.[94] A week later on Raw, after a match Hardy had with Rob Conway, Edge then appeared and overpowered Hardy. He then wedged Hardy's head between the steel steps and the ringpost, and then kicked the steel steps into Hardy's head.[95] On the August 29 episode of Raw, Edge and Hardy fought in a Street Fight, which resulted in a no contest when Hardy performed a side effect on Edge off the entrance ramp into the sound speakers and other electrical equipment.[96] They also fought in a Steel cage match at Unforgiven on September 18 in which Hardy defeated Edge.[97] The feud culminated in a Loser Leaves Raw ladder match on Raw Homecoming on October 3, which Edge won.[98] After the match, Hardy left the Raw brand to go to the SmackDown! brand, while Edge suffered a legit torn pectoralis major muscle that kept him shelved for a couple of months.[99] During his time off, he starred in his own talk show segment on Raw entitled The Cutting Edge,[100] dubbing himself the "Rated-R Superstar". Edge used his talk show to start a feud with Ric Flair following Flair's well-publicized arrest in connection with a road rage incident.[100][101] Edge eventually began using The Cutting Edge as a soapbox to run down Flair until, after weeks of public mockery, Flair eventually showed up and attacked Edge.[102] Edge and Flair formally met at the New Year's Revolution event on January 8, 2006 in a match for Flair's Intercontinental Championship, which resulted in Flair retaining after Edge was disqualified.[103]

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Copeland lives in Asheville, North Carolina.[316] He began a relationship with Alannah Morley, the sister of wrestler Val Venis, in 1998; they married on November 8, 2001,[317] and divorced on March 10, 2004.[318] He married Lisa Ortiz on October 21, 2004. Soon after marrying her, he began an affair with wrestler Lita, who was dating his friend Matt Hardy. The affair became public knowledge in February 2005,[319] resulting in Copeland and Ortiz divorcing on November 17, 2005.[320] Copeland and wrestler Beth Phoenix had a daughter in 2013 and a second daughter in 2016.[321][322] They were married on October 30, 2016 (his 43rd birthday).[323]

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But this virtual flood of data is also changing the way businesses handle computing. The traditional computing paradigm built on a centralized data center and everyday internet isn't well suited to moving endlessly growing rivers of real-world data. Bandwidth limitations, latency issues and unpredictable network disruptions can all conspire to impair such efforts. Businesses are responding to these data challenges through the use of edge computing architecture.

In simplest terms, edge computing moves some portion of storage and compute resources out of the central data center and closer to the source of the data itself. Rather than transmitting raw data to a central data center for processing and analysis, that work is instead performed where the data is actually generated -- whether that's a retail store, a factory floor, a sprawling utility or across a smart city. Only the result of that computing work at the edge, such as real-time business insights, equipment maintenance predictions or other actionable answers, is sent back to the main data center for review and other human interactions.

Thus, edge computing is reshaping IT and business computing. Take a comprehensive look at what edge computing is, how it works, the influence of the cloud, edge use cases, tradeoffs and implementation considerations.

So IT architects have shifted focus from the central data center to the logical edge of the infrastructure -- taking storage and computing resources from the data center and moving those resources to the point where the data is generated. The principle is straightforward: If you can't get the data closer to the data center, get the data center closer to the data. The concept of edge computing isn't new, and it is rooted in decades-old ideas of remote computing -- such as remote offices and branch offices -- where it was more reliable and efficient to place computing resources at the desired location rather than rely on a single central location.

One of the easiest ways to understand the differences between edge, cloud and fog computing is to highlight their common theme: All three concepts relate to distributed computing and focus on the physical deployment of compute and storage resources in relation to the data that is being produced. The difference is a matter of where those resources are located.

Edge. Edge computing is the deployment of computing and storage resources at the location where data is produced. This ideally puts compute and storage at the same point as the data source at the network edge. For example, a small enclosure with several servers and some storage might be installed atop a wind turbine to collect and process data produced by sensors within the turbine itself. As another example, a railway station might place a modest amount of compute and storage within the station to collect and process myriad track and rail traffic sensor data. The results of any such processing can then be sent back to another data center for human review, archiving and to be merged with other data results for broader analytics.

Cloud. Cloud computing is a huge, highly scalable deployment of compute and storage resources at one of several distributed global locations (regions). Cloud providers also incorporate an assortment of pre-packaged services for IoT operations, making the cloud a preferred centralized platform for IoT deployments. But even though cloud computing offers far more than enough resources and services to tackle complex analytics, the closest regional cloud facility can still be hundreds of miles from the point where data is collected, and connections rely on the same temperamental internet connectivity that supports traditional data centers. In practice, cloud computing is an alternative -- or sometimes a complement -- to traditional data centers. The cloud can get centralized computing much closer to a data source, but not at the network edge.

Fog. But the choice of compute and storage deployment isn't limited to the cloud or the edge. A cloud data center might be too far away, but the edge deployment might simply be too resource-limited, or physically scattered or distributed, to make strict edge computing practical. In this case, the notion of fog computing can help. Fog computing typically takes a step back and puts compute and storage resources "within" the data, but not necessarily "at" the data.

Fog computing environments can produce bewildering amounts of sensor or IoT data generated across expansive physical areas that are just too large to define an edge. Examples include smart buildings, smart cities or even smart utility grids. Consider a smart city where data can be used to track, analyze and optimize the public transit system, municipal utilities, city services and guide long-term urban planning. A single edge deployment simply isn't enough to handle such a load, so fog computing can operate a series of fog node deployments within the scope of the environment to collect, process and analyze data.

Note: It's important to repeat that fog computing and edge computing share an almost identical definition and architecture, and the terms are sometimes used interchangeably even among technology experts.

By deploying servers and storage where the data is generated, edge computing can operate many devices over a much smaller and more efficient LAN where ample bandwidth is used exclusively by local data-generating devices, making latency and congestion virtually nonexistent. Local storage collects and protects the raw data, while local servers can perform essential edge analytics -- or at least pre-process and reduce the data -- to make decisions in real time before sending results, or just essential data, to the cloud or central data center.

In principal, edge computing techniques are used to collect, filter, process and analyze data "in-place" at or near the network edge. It's a powerful means of using data that can't be first moved to a centralized location -- usually because the sheer volume of data makes such moves cost-prohibitive, technologically impractical or might otherwise violate compliance obligations, such as data sovereignty. This definition has spawned myriad real-world examples and use cases: e24fc04721