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Wicked Delights Of A Bridal Bed

RRP $15.99

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To her surprise, Lady Mallory Byron finds herself walking down the aisle with the last man she ever expected to ask for her hand . . . .

Everyone knows the Byron brothers are "mad, bad and dangerous." Now their sister shockingly discovers she's the newest talk of the Ton when she marries the scandalous Earl of Gresham. Faced with a tragic loss, she'd sought comfort from him as a family friend. But soon consolation turned to passion, scandal--and a wedding In the bridal bed, she finds pleasure beyond her wildest dreams. But can nights of wicked delight change friendship into true love?

Charming rakehell Adam, Earl of Gresham, has secretly loved Mallory for years. He lost her once to another man, but now he has a second chance to win her love--and plans to do so by any means necessary. Will Mallory's heart give him what he so dearly desires? Or is the past too much to overcome?


Neural Network Perception For Mobile Robot Guidance

RRP $37.95

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Dean Pomerleau's trainable road tracker, ALVINN, is arguably the world's most famous neural net application. It currently holds the world's record for distance traveled by an autonomous robot without interruption: 21.2 miles along a highway, in traffic, at speedsofup to 55 miles per hour. Pomerleau's work has received worldwide attention, including articles in Business Week (March 2, 1992), Discover (July, 1992), and German and Japanese science magazines. It has been featured in two PBS series, "The Machine That Changed the World" and "By the Year 2000," and appeared in news segments on CNN, the Canadian news and entertainment program "Live It Up", and the Danish science program "Chaos". What makes ALVINN especially appealing is that it does not merely drive - it learns to drive, by watching a human driver for roughly five minutes. The training inputstothe neural networkare a video imageoftheroad ahead and thecurrentposition of the steering wheel. ALVINN has learned to drive on single lane, multi-lane, and unpaved roads. It rapidly adapts to other sensors: it learned to drive at night using laser reflectance imaging, and by using a laser rangefinder it learned to swerve to avoid obstacles and maintain a fixed distance from a row of parked cars. It has even learned to drive backwards.



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