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.

Unsupervised language model adaptation

Abstract: This paper investigates unsupervised language model adaptation, from ASR transcripts. N-gram counts from these transcripts can be used either to adapt an existing n-gram model or to build an n-gram model from scratch. Various experimental results are reported on a particular domain adaptation task, namely building a customer care application starting from a general voicemail transcription system. The experiments investigate the effectiveness of various adaptation strategies, including iterative adaptation and self-adaptation on the test data. They show an error rate reduction of 3.9% over the unadapted baseline performance, from 28% to 24.1%, using 17 hours of unsupervised adaptation material. This is 51% of the 7.7% adaptation gain obtained by supervised adaptation. Self-adaptation on the test data resulted in a 1.3% improvement over the baseline.

Algorithms for distributed functional monitoring

Abstract: We study what we call functional monitoring problems. We have k players each tracking their inputs, say player i tracking a multiset Ai(t) up until time t, and communicating with a central coordinator. The coordinator's task is to monitor a given function f computed over the union of the inputs ∪iAi(t), continuously at all times t. The goal is to minimize the number of bits communicated between the players and the coordinator. A simple example is when f is the sum, and the coordinator is required to alert when the sum of a distributed set of values exceeds a given threshold τ. Of interest is the approximate version where the coordinator outputs 1 if f ≥ τ and 0 if f ≤ (1 - e)τ. This defines the (k, f, τ, e) distributed, functional monitoring problem. Functional monitoring problems are fundamental in distributed systems, in particular sensor networks, where we must minimize communication; they also connect to problems in communication complexity, communication theory, and signal processing. Yet few formal bounds are known for functional monitoring. We give upper and lower bounds for the (k, f, τ, e) problem for some of the basic f's. In particular, we study frequency moments (F0, F1, F2). For F0 and F1, we obtain continuously monitoring algorithms with costs almost the same as their one-shot computation algorithms. However, for F2 the monitoring problem seems much harder. We give a carefully constructed multi-round algorithm that uses "sketch summaries" at multiple levels of detail and solves the (k, F2, τ, e) problem with communication O(k2/e+ (√k/e)3). Since frequency moment estimation is central to other problems, our results have immediate applications to histograms, wavelet computations, and others. Our algorithmic techniques are likely to be useful for other functional monitoring problems as well.

The (in)completeness of the observed internet AS-level structure

Abstract: Despite significant efforts to obtain an accurate picture of the Internet's connectivity structure at the level of individual autonomous systems (ASes), much has remained unknown in terms of the quality of the inferred AS maps that have been widely used by the research community. In this paper, we assess the quality of the inferred Internet maps through case studies of a sample set of ASes. These case studies allow us to establish the ground truth of connectivity between this set of ASes and their directly connected neighbors. A direct comparison between the ground truth and inferred topology maps yield insights into questions such as which parts of the actual topology are adequately captured by the inferred maps, which parts are missing and why, and what is the percentage of missing links in these parts. This information is critical in assessing, for each class of real-world networking problems, whether the use of currently inferred AS maps or proposed AS topology models is, or is not, appropriate. More importantly, our newly gained insights also point to new directions towards building realistic and economically viable Internet topology maps.

Rigorous treatment of distillable entanglement

Abstract: The notion of distillable entanglement is one of the fundamental concepts of quantum information theory. Unfortunately, there is an apparent mismatch between the intuitive and rigorous definitions of distillable entanglement. To be precise, the existing rigorous definitions impose the constraint that the distilation protocol produce an output of constant dimension. It is therefore conceivable that this unnecessary constraint might have led to underestimation of the true distillable entanglement. We give a new definition of distillable entanglement which removes this constraint, but could conceivably overestimate the true value. Since the definitions turn out to be equivalent, neither underestimation nor overestimation is possible, and both definitions are arguably correct

Methods for finding frequent items in data streams

Abstract: The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Many applications rely directly or indirectly on finding the frequent items, and implementations are in use in large scale industrial systems. However, there has not been much comparison of the different methods under uniform experimental conditions. It is common to find papers touching on this topic in which important related work is mischaracterized, overlooked, or reinvented. In this paper, we aim to present the most important algorithms for this problem in a common framework. We have created baseline implementations of the algorithms and used these to perform a thorough experimental study of their properties. We give empirical evidence that there is considerable variation in the performance of frequent items algorithms. The best methods can be implemented to find frequent items with high accuracy using only tens of kilobytes of memory, at rates of millions of items per second on cheap modern hardware.