Adaptive rate control for H.264
This paper presents a rate control scheme for H.264 by introducing the concept of basic unit and a linear prediction model. The basic unit can be a macroblock (MB), a slice, or a frame. It can be used to obtain a trade-off between the overall coding efficiency and the bits fluctuation. The linear model is used to solve the chicken and egg dilemma existing in the rate control of H.264. Both constant bit rate (CBR) and variable bit rate (VBR) cases are studied. Our scheme has been adopted by H.264.

title: Basic Topological Models for Spatial Entities in 3-Dimensional Space
abstract: In recent years, models of spatial relations, especially topological relations, have attracted much attention from the GIS community. In this paper, some basic topologic models for spatial entities in both vector and raster spaces are discussed.It has been suggested that, in vector space, an open set in 1-D space may not be an open set any more in 2-D and 3-D spaces. Similarly, an open set in 2-D vector space may also not be an open set any more in 3-D vector spaces. As a result, fundamental topological concepts such as boundary and interior are not valid any more when a lower dimensional spatial entity is embedded in higher dimensional space. For example, in 2-D, a line has no interior and the line itself (not its two end-points) forms a boundary. Failure to recognize this fundamental topological property will lead to topological paradox. It has also been stated that the topological models for raster entities are different in Z2 and R2. There are different types of possible boundaries depending on the definition of adjacency or connectedness. If connectedness is not carefully defined, topological paradox may also occur. In raster space, the basic topological concept in vector space—connectedness—is implicitly inherited. This is why the topological properties of spatial entities can also be studied in raster space. Study of entities in raster (discrete) space could be a more efficient method than in vector space, as the expression of spatial entities in discrete space is more explicit than that in connected space.

title: A Compiler-Assisted Scheme for Adaptive Cache Coherence Enforcement

title: Efficient and distributed computation of maximum multicast rates
abstract: The transmission of information within a data network is constrained by network topology and link capacities. In this paper, we study the fundamental upper bound of information multicast rates with these constraints, given the unique replicable and encodable property of information flows. Based on recent information theory advances in coded multicast rates, we are able to formulate the maximum multicast rate problem as a linear network optimization problem, assuming the general undirected network model. We then proceed to apply Lagrangian relaxation techniques to obtain (1) a necessary and sufficient condition for multicast rate feasibility, and (2) a subgradient solution for computing the maximum rate and the optimal routing strategy to achieve it. The condition we give is a generalization of the well-known conditions for the unicast and broadcast cases. Our subgradient solution takes advantage of the underlying network flow structure of the problem, and therefore outperforms general linear programming solving techniques. It also admits a natural intuitive interpretation, and is amenable to fully distributed implementations.

title: Position Variance, Recurrence and Perceptual Learning
abstract: Stimulus arrays are inevitably presented at different positions on the retina in visual tasks, even those that nominally require fixation. In particular, this applies to many perceptual learning tasks. We show that perceptual inference or discrimination in the face of positional variance has a structurally different quality from inference about fixed position stimuli, involving a particular, quadratic, non-linearity rather than a purely linear discrimination. We show the advantage taking this non-linearity into account has for discrimination, and suggest it as a role for recurrent connections in area VI, by demonstrating the superior discrimination performance of a recurrent network. We propose that learning the feedforward and recurrent neural connections for these tasks corresponds to the fast and slow components of learning observed in perceptual learning tasks.

title: QoS-aware multicasting in DiffServ domains
abstract: QoS-aware multicasting is becoming more and more desirable with the expanding usage of group-based applications, especially those involving multimedia objects. Until now, most of the proposed QoS-aware multicasting routing protocols adopt per-flow based resource reservation. Although these schemes can be adopted in integrated services (IntServ) Internet, they are not suitable for more scalable Differentiated Services (DiffServ) Internet. A new QoS-aware multicast routing protocol called QMD is proposed for DiffServ environments in this paper. Based on the design philosophy of DiffServ, the complex multicasting control plane functionalities are removed from the core routers. In addition, for each multicast group, only a limited set of on-tree routers (termed as key nodes) maintain multicast routing states and forward multicast data traffic. The key nodes of a multicast group uniquely identify a QoS-satisfied multicast tree connecting the group members. Although the other on-tree routers between any two key nodes do not maintain any multicast routing states and QoS reservation information, the group members' QoS requirements can still be satisfied. Through simulation experiments based on both random and real intra-domain topologies, we have also demonstrated that QMD can provide higher QoS-satisfaction rate while maintaining the simplicity of core routers.

title: Applying array contraction to a sequence of DOALL loops
abstract: Efficient program execution on multiprocessor computers requires both sufficient parallelism and good data locality. Recent research found that, using a combination of loop shifting, loop fusion, and array contraction, one can reduce the memory required to execute a sequence of serial loops, thereby to improve the cache locality. This paper studies how to extend such a memory-reduction scheme to a sequence of DOALL loops, which are executed in parallel on multiprocessors. Two methods are proposed to overcome difficulties caused by loop-carried dependences. Data copy-in is performed to remove anti-dependences between different parallel threads, and computation duplication is performed to remove flow dependences. Experiments performed on a number of benchmark programs show that the proposed technique improves both cache locality and parallel execution speed for the DOALL loops. The scheme achieves an average speedup of 1.41 for 17 programs on a 4-processor SUN machine.