Path (computing)

Abstract: Briefly, in accordance with one embodiment of the invention, a music player may receive a requested music file through a wireless communication. For example, the device may establish a peer-to-peer wireless communication path(s) with another device to initiate a request for a music file without a priori knowledge of whether the file is available to the another device. The device may then receive the music file from the remote device through a peer-to-peer wireless communication path(s).

Abstract: A telecommunications switching system employing multi-protocol routing optimization which utilizes predetermined and measured parameters in accordance with a set of user priorities in determining the selection of a telecommunications path to be utilized for transmitting a data file to a remote destination. The switching system has a first memory for storing the data file to be transferred, a second memory for storing predetermined parameters such as cost data associated with each of the telecommunications paths, a third memory for storing a set of user priorities regarding the transmission of data files, and means for measuring the value of variable parameters such as file transfer speed associated with each of the telecommunications paths. Processor means are operatively associated with the second and third memories and the variable parameter measuring means for determining which of the plurality of telecommunications paths should be utilized for transferring the data file in accordance with the set of user priorities, the predetermined telecommunications path parameters, and the measured variable parameters. The switching system further comprises input means for allowing a user to change the user priorities in the third memory prior to transmitting a file.

Abstract: We examine three sets of established behavioral hypotheses about consumers’ in-store behavior using field data on grocery store shopping paths and purchases. Our results provide field evidence for the following empirical regularities. First, as consumers spend more time in the store, they become more purposeful—they are less likely to spend time on exploration and more likely to shop/buy. Second, consistent with “licensing” behavior, after purchasing virtue categories, consumers are more likely to shop at locations that carry vice categories. Third, the presence of other shoppers attracts consumers toward a store zone but reduces consumers’ tendency to shop there.

Abstract: Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right - similar to why we study the human brain - and will enable researchers to further improve DNNs. One path to understanding how a neural network functions internally is to study what each of its neurons has learned to detect. One such method is called activation maximization (AM), which synthesizes an input (e.g. an image) that highly activates a neuron. Here we dramatically improve the qualitative state of the art of activation maximization by harnessing a powerful, learned prior: a deep generator network (DGN). The algorithm (1) generates qualitatively state-of-the-art synthetic images that look almost real, (2) reveals the features learned by each neuron in an interpretable way, (3) generalizes well to new datasets and somewhat well to different network architectures without requiring the prior to be relearned, and (4) can be considered as a high-quality generative method (in this case, by generating novel, creative, interesting, recognizable images).