Comcast Cable Communications, LLC

Other

Joseph Yongxiang Chen

Marc Fernandez

Yuan Yan Chen

A computing device may provide content items to user devices requesting the content items. The computing device may receive the content item, in portions, from a source, and may send the portions to the user device. The computing device may send a first portion before the computing device receives all of the content item from the source. The request may also be associated with an expiration period, and the expiration period may affect the sending of the content item to the user device.

Streamlined delivery of video content

A content delivery server may provide content to a requesting client device using a streamlined HTTP enhancement proxy delivery technique. For example, an HTTP proxy server may receive a request for video content or a fragment of video content from a client device. The request may be associated with a timeout scheduled to occur if no content has been received after a specified amount of time. The server may then transmit a request for the content to a remote server, such as an upstream cache server in the proxy server's CDN. When the proxy server receives a portion of the requested content from the remote server, the proxy server begins transmitting the portion to the client device before the requested content has been completely received and buffered. The client device may then begin receiving data from the proxy server before timeout has occurred.

Forearm for a rifle

Keymod forearm for AR-15 and AR-10 rifles

Keymod forearm for an AR-15 rifle

Railed forearm for AR-15 style rifles

A plausible neural network (PLANN) is an artificial neural network with weight connection given by mutual information, which has the capability of inference and learning, and yet retains many characteristics of a biological neural network. The learning algorithm is based on statistical estimation, which is faster than the gradient decent approach currently used. The network after training becomes a fuzzy/belief network; the inference and weight are exchangeable, and as a result, knowledge extraction becomes simple. PLANN performs associative memory, supervised, semi-supervised, unsupervised learning and function/relation approximation in a single network architecture. This network architecture can easily be implemented by analog VLSI circuit design.

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Englewood, CO, United States of America

Joseph Yongxiang Chen