AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

LiveRAC: interactive visual exploration of system management time-series data

Abstract: We present LiveRAC, a visualization system that supports the analysis of large collections of system management time-series data consisting of hundreds of parameters across thousands of network devices. LiveRAC provides high information density using a reorderable matrix of charts, with semantic zooming adapting each chart's visual representation to the available space. LiveRAC allows side-by-side visual comparison of arbitrary groupings of devices and parameters at multiple levels of detail. A staged design and development process culminated in the deployment of LiveRAC in a production environment. We conducted an informal longitudinal evaluation of LiveRAC to better understand which proposed visualization techniques were most useful in the target environment.

System and method for synthetically generated speech describing media content

Abstract: Disclosed herein are systems, methods, and computer readable-media for providing an automatic synthetically generated voice describing media content, the method comprising receiving one or more pieces of metadata for a primary media content, selecting at least one piece of metadata for output, and outputting the at least one piece of metadata as synthetically generated speech with the primary media content. Other aspects of the invention involve alternative output, output speech simultaneously with the primary media content, output speech during gaps in the primary media content, translate metadata in foreign language, tailor voice, accent, and language to match the metadata and/or primary media content. A user may control output via a user interface or output may be customized based on preferences in a user profile.

Tabulation based 4-universal hashing with applications to second moment estimation

Abstract: We show that 4-universal hashing can be implemented efficiently using tabulated 4-universal hashing for characters, gaining a factor of 5 in speed over the fastest existing methods. We also consider generalization to k-universal hashing, and as a prime application, we consider the approximation of the second moment of a data stream.

Approximation of functions over redundant dictionaries using coherence

Abstract: One of the central problems of modern mathematical approximation theory is to approximate functions, or signals, concisely, with elements from a large candidate set called a dictionary. Formally, we are given a signal A ∈ RN and a dictionary D = {φi}i∈I of unit vectors that span RN. A representation R of B terms for input A ∈ RN is a linear combination of dictionary elements, R = σi∈A αiφi, for φi ∈ D and some A, vAv ≥ B. Typically, B ⪡ N, so that R is a concise approximation to signal A. The error of the representation indicates by how well it approximates A, and is given by ∥A - R∥2 = √Σt|A[t - R[t]|2. The problem is to find the best B-term representation, i.e., find a R that minimizes ∥A - R∥2. A dictionary may be redundant in the sense that there is more than one possible exact representation for A, i.e., vDv > N = dim(RN). Redundant dictionaries are used because, both theoretically and in practice, for important classes of signals, as the size of a dictionary increases, the error and the conciseness of the approximations improve.We present the first known efficient algorithm for finding a provably approximate representation for an input signal over redundant dictionaries. We identify and focus on redundant dictionaries with small coherence (ie., vectors are nearly orthogonal). We present an algorithm that preprocesses any such dictionary in time and space polynomial in vDv, and obtains an 1 + e approximate representation of the given signal in time nearly linear in signal size N and polylogarithmic in vDv; by contrast, most algorithms in the literature require Ω(vDv)time, and, yet, provide no provable bounds. The technical crux of our result is our proof that two commonly used local search techniques, when combined appropriately, gives a provably near-optimal signal representation over redundant dictionaries with small coherence. Our result immediately applies to several specific redundant dictionaries considered by the domain experts thus far. In addition, we present new redundant dictionaries which have small coherence (and therefore are amenable to our algorithms) and yet have significantly large sizes, thereby adding to the redundant dictionary construction literature.Work with redundant dictionaries forms the emerging field of highly nonlinear approximation theory. We have presented algorithmic results for some of the most basic problems in this area, but other mathematical and algorithmic questions remain to be explored.

Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices

Abstract: Flare is a practical system for streaming 360-degree videos on commodity mobile devices. It takes a viewport-adaptive approach, which fetches only portions of a panoramic scene that cover what a viewer is about to perceive. We conduct an IRB-approved user study where we collect head movement traces from 130 diverse users to gain insights on how to design the viewport prediction mechanism for Flare. We then develop novel online algorithms that determine which spatial portions to fetch and their corresponding qualities. We also innovate other components in the streaming pipeline such as decoding and server-side transmission. Through extensive evaluations (~400 hours' playback on WiFi and ~100 hours over LTE), we show that Flare significantly improves the QoE in real-world settings. Compared to non-viewport-adaptive approaches, Flare yields up to 18x quality level improvement on WiFi, and achieves high bandwidth reduction (up to 35%) and video quality enhancement (up to 4.9x) on LTE.