Matthias Ruhl

Finding nearest neighbors in growth-restricted metrics

Most research on nearest neighbor algorithms in the literature has been focused on the Euclidean case. In many practical search problems however, the underlying metric is non-Euclidean. Nearest neighbor algorithms for general metric spaces are quite weak, which motivates a search for other classes of metric spaces that can be tractably searched.In this paper, we develop an efficient dynamic data structure for nearest neighbor queries in growth-constrained metrics. These metrics satisfy the property that for any point q and number r the ratio between numbers of points in balls of radius 2r and r is bounded by a constant. Spaces of this kind may occur in networking applications, such as the Internet or Peer-to-peer networks, and vector quantization applications, where feature vectors fall into low-dimensional manifolds within high-dimensional vector spaces.

Simple efficient load balancing algorithms for peer-to-peer systems

Load balancing is a critical issue for the efficient operation of peer-to-peer networks. We give two new load-balancing protocols whose provable performance guarantees are within a constant factor of optimal. Our protocols refine the consistent hashing data structure that underlies the Chord (and Koorde) P2P network. Both preserve Chord’s logarithmic query time and near-optimal data migration cost. Our first protocol balances the distribution of the key address space to nodes, which yields a load-balanced system when the DHT maps items “randomly” into the address space. To our knowledge, this yields the first P2P scheme simultaneously achieving O(log n) degree, O(log n) look-up cost, and constant-factor load balance (previous schemes settled for any two of the three). Our second protocol aims to directly balance the distribution of items among the nodes. This is useful when the distribution of items in the address space cannot be randomized – for example, if we wish to support range-searches on “ordered” keys. We give a simple protocol that balances load by moving nodes to arbitrary locations “where they are needed.” As an application, we use the last protocol to give an optimal implementation of a distributed data structure for range searches on ordered data.

Diminished chord: a protocol for heterogeneous subgroup formation in peer-to-peer networks

In most of the P2P systems developed so far, all nodes play essentially the same role. In some applications, however, different machine capabilities or owner preferences may mean that only a subset of nodes in the system should participate in offering a particular service. Arranging for each service to be supported by a different peer to peer network is, we argue here, a wasteful solution. Instead, we propose a version of the Chord peer-to-peer protocol that allows any subset of nodes in the network to jointly offer a service without forming their own Chord ring. Our variant supports the same efficient join/leave/insert/delete operations that the subgroup would get if they did form their own separate peer to peer network, but requires significantly less resources than the separate network would. For each subgroup of k machines, our protocol uses O(k) additional storage in the primal Chord ring. The insertion or deletion of a node in the subgroup and the lookup of the next node of a subgroup all require O(log n) hops.

Evolutionary fractal image compression

This paper introduces evolutionary computing to fractal image compression. In fractal image compression a partitioning of the image into ranges is required. We propose to use evolutionary computing to find good partitionings. Here ranges are connected sets of small square image blocks. Populations consist of N/sub p/ configurations, each of which is a partitioning with a fractal code. In the evolution each configuration produces /spl sigma/ children who inherit their parent partitionings except for two random neighboring ranges which are merged. From the offspring the best ones are selected for the next generation population based on a fitness criterion (collage error). We show that a far better rate-distortion curve can be obtained with this approach as compared to traditional quad-tree partitionings.

Optimal fractal coding is NP-hard

In fractal compression a signal is encoded by the parameters of a contractive transformation whose fixed point (attractor) is an approximation of the original data. Thus fractal coding can be viewed as the optimization problem of finding in a set of admissible contractive transformations the transformation whose attractor is closest to a given signal. The standard fractal coding scheme based on the collage theorem produces only a suboptimal solution. We demonstrate by a reduction from MAXCUT that the problem of determining the optimal fractal code is NP-hard. To our knowledge, this is the first analysis of the intrinsic complexity of fractal coding. Additionally, we show that standard fractal coding is not an approximating algorithm for this problem.

Region-based fractal image compression

A fractal coder partitions an image into blocks that are coded via self-references to other parts of the image itself. We present a fractal coder that derives highly image-adaptive partitions and corresponding fractal codes in a time-efficient manner using a region-merging approach. The proposed merging strategy leads to improved rate-distortion performance compared to previously reported pure fractal coders, and it is faster than other state-of-the-art fractal coding methods.

Adaptive partitionings for fractal image compression

In fractal image compression a partitioning of the image into ranges is required. Saupe and Ruhl (1996) proposed to find good partitionings by means of a split-and-merge process guided by evolutionary computing. In this approach ranges are connected sets of small square image blocks. Far better rate-distortion curves can be obtained as compared to traditional quadtree partitionings, however, at the expense of an increase of computing time. In this paper we show how conventional acceleration techniques and a deterministic version of the evolution reduce the time-complexity of the method without degrading the encoding quality. Furthermore, we report on techniques to improve the rate-distortion performance and evaluate the results visually.

Interactive Visualization of Implicit Surfaces with Singularities

This paper presents work on two methods for interactive visualization of implicit surfaces: physically‐based sampling using particle systems and polygonization followed by physically‐based mesh improvement which explicitly makes use of the surface‐defining equation. While most previous work applied to bounded manifolds without singularities and without boundary (topological spheres) we broaden the scope of the methods to include surfaces with such features, in particular cusp points and surface self‐intersections. These aspects are not (yet) essential for computer graphics modelling with implicit surfaces but they naturally occur in simulations of interest in mathematical visualization. In this paper we use the Kummer family of algebraic surfaces as an example.

The Directed Steiner Network problem is tractable for a constant number of terminals

We consider the Directed Steiner Network (DSN) problem, also called the Point-to-Point Connection problem, where given a directed graph G and p pairs {(s/sub 1/,t/sub 1/), ..., (s/sub p/,t/sub p/)} of nodes in the graph, one has to find the smallest subgraph H of G that contains paths from s/sub i/ to t/sub i/ for all i. The problem is NP-hard for general p, since the Directed Steiner Tree problem is a special case. Until now, the complexity was unknown for constant p/spl ges/3. We prove that the problem is polynomially solvable if p is any constant number, even if nodes and edges in G are weighted and the goal is to minimize the total weight of the subgraph H. In addition, we give an efficient algorithm for the Strongly Connected Steiner Subgraph problem for any constant p, where given a directed graph and p nodes in the graph, one has to compute the smallest strongly connected subgraph containing the p nodes.

Optimal hierarchical partitions for fractal image compression

In fractal image compression a partitioning of the image is required. In this paper we discuss the construction of rate-distortion optimal partitions. We begin with a fine scale partition which gives a fractal encoding with a high bit rate and a low distortion. The partition is hierarchical, thus, corresponds to a tree. We employ a pruning strategy based on the generalized BFOS algorithm. It extracts subtrees corresponding to partitions and fractal encodings which are optimal in the rate-distortion sense. First results are included for the case of fractal encodings based on rectangular (HV) partitions. We also provide a comparison with greedy partitions based on the traditional collage error criterion or just using block variance.