6443463
Prescient R-D optimized packet dependency management for low-latency video streaming
An improved, prescient, scheme to deliver preencoded video streams at very low latency is presented. We dynamically manage the prediction dependency for a group of packets using a rate-distortion (R-D) optimization framework that adapts to the channel, so that the expected end-to-end distortion is minimized under a given rate constraint. In our system, the R-D characteristics of each prediction mode are precomputed at compression time and stored in an R-D preamble along with the multiple compressed versions of the stream. The distortion in the preamble is estimated using an accurate loss-distortion model, and the optimal prediction modes are computed with an iterative descent algorithm. Complexity is reduced at streaming time by using the preamble to compute the optimal modes instead of actually compressing the video. Experiments demonstrate that the proposed prescient scheme provides significant performance gains over simple intra-insertion, and consistent bitrate savings compared to the greedy scheme.
17534707
Upfront intra-refresh decision for low-complexity wireless video telephony
An adaptive intra-refresh (IR) technique is proposed for low-complexity video encoding on resource-constrained wireless platforms. The IR decision is made upfront without requiring any pre-encoding, which significantly reduces the complexity and power consumption in real-time communication. To allow upfront mode decision, a novel closed-form solution is provided requiring no algorithmic iteration or exhaustive searches. The IR scheme adapts to multiple factors including the content texture information, the frame-to-frame pixel-value variation, and the estimated channel loss probability. The macroblock-based refresh can be performed using various patterns (i.e., cyclic, random etc.). Experimental results demonstrate superior performance of the adaptive scheme over fixed schemes at all channel loss rates
7289490
Low-latency video transmission over lossy packet networks using rate-distortion optimized reference picture selection
Today's Internet video streaming systems employ buffering and multiple retransmissions to guarantee the correct reception of each packet, which leads to high latency in media delivery. In this work, a novel scheme of dynamic management of the dependency across packets is proposed using optimal reference picture selection at the frame level that adapts to the channel. The optimal selection of the reference picture is achieved with a rate-distortion framework, which minimizes the expected end-to-end distortion given a rate constraint. The expected distortion is calculated based on an accurate binary tree modeling with the effects of channel loss and error concealment taken into account. Experiments demonstrate that the proposed :scheme provides significant performance gains over a simple INTRA-insertion scheme. The increased error-resilience eliminates the need of retransmission, which makes it possible to reduce the latency from 10-15 seconds to a few hundred milliseconds, with similar video quality maintained.
38500613
On the Feedback Vertex Set Problem in Permutation Graphs
Abstract Brandstadt and Kratsch (1987) presented an O( n 6 ) time algorithm for the minimum weight feedback vertex set problem in a permutation graph of n vertices and m edges. This result was recently improved to O( n.m.m ) by Brandstat (1993), where m is the number of edges in the complement of G . In this paper, we employ a different dynamic programming scheme to reduce the time complexity to O( nm ) for this problem in permutation graphs.
41985683
Constraint Satisfaction In "Conscious" Software Agents--A Practical Application
ABSTRACT The U.S. Navy has been trying for many years to automate its personnel assignment process. Periodic assignment of personnel to new jobs is mandatory according to Navy policy. Though various software systems are used regularly, the process is mainly done manually and sequentially by Navy personnel called detailers. This paper is a case study in applying cognitive theories implemented via new AI techniques to produce flexible adaptive human-like software. In it we present a sketch of an intelligent distribution agent that aims to largely automate the detailer's tasks. In particular, we look inside its constraint satisfaction, which is mainly responsible for satisfying the requirements of the Navy's policies, the command's needs, and the sailor's preferences in order to achieve “satisfactory” job assignments, according to ever-evolving Navy standards.
18261713
Differential and trajectory methods for time course gene expression data
MOTIVATION
The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differentiation approaches can reduce the dimensions of data, help to remove redundant genes and noises, and highlight the most relevant genes that are major players in the development of certain diseases or the effect of drug treatment. The purpose of this study is to investigate the efficiency of parametric (including Bayesian and non-Bayesian, linear and non-linear), non-parametric and semi-parametric gene filtering methods through the application of time course microarray data from multiple sclerosis patients being treated with interferon-beta-1a. The analysis of variance with bootstrapping (parametric), class dispersion (semi-parametric) and Pareto (non-parametric) with permutation methods are presented and compared for filtering and finding differentially expressed genes. The Bayesian linear correlated model, the Bayesian non-linear model the and non-Bayesian mixed effects model with bootstrap were also developed to characterize the differential expression patterns. Furthermore, trajectory-clustering approaches were developed in order to investigate the dynamic patterns and inter-dependency of drug treatment effects on gene expression.


RESULTS
Results show that the presented methods performed significant differently but all were adequate in capturing a small number of the potentially relevant genes to the disease. The parametric method, such as the mixed model and two Bayesian approaches proved to be more conservative. This may because these methods are based on overall variation in expression across all time points. The semi-parametric (class dispersion) and non-parametric (Pareto) methods were appropriate in capturing variation in expression from time point to time point, thereby making them more suitable for investigating significant monotonic changes and trajectories of changes in gene expressions in time course microarray data. Also, the non-linear Bayesian model proved to be less conservative than linear Bayesian correlated growth models to filter out the redundant genes, although the linear model showed better fit than non-linear model (smaller DIC). We also report the trajectories of significant genes-since we have been able to isolate trajectories of genes whose regulations appear to be inter-dependent.
10881008
Steiner Set and Connected Domination in Trapezoid Graphs
Abstract Trapezoid graphs are extensions of interval graphs and permutation graphs. This paper presents an O (¦V¦) time algorithm for finding a minimum cardinality Steiner set and an O (¦E¦ + ¦V¦) time algorithm for finding a minimum cardinality connected dominating set in a trapezoid graph G = ( V , E ), given the trapezoid diagram.
23159718
Efficient Algorithms for Finding Depth-First and Breadth-First Search Trees in Permutation Graphs
This paper presents an O(n log log n) time algorithm for finding a depth-first search tree and an O(n) time algorithm for finding a breadth-first search tree in a permutation graph, respectively.