8840929
Optimized Live Heap Bound Analysis
This paper describes a general approach for optimized live heap space and live heap space-bound analyses for garbage-collected languages. The approach is based on program analysis and transformations and is fully automatic. In our experience, the space-bound analysis generally produces accurate (tight) upper bounds in the presence of partially known input structures. The optimization drastically improves the analysis efficiency. The analyses have been implemented and experimental results confirm their accuracy and efficiency.
2600635
A systematic incrementalization technique and its application to hardware design

Abstract.A systematic transformation method based on incrementalization and value caching generalizes a broad family of program optimizations. It yields significant performance improvements in many program classes, including iterative schemes that characterize hardware specifications. CACHET is an interactive incrementalization tool. Although incrementalization is highly structured and automatable, better results are obtained through interaction, where the main task is to guide term rewriting based on data-specific identities. Incrementalization specialized to iteration corresponds to strength reduction, a familiar program improvement technique. This correspondence is illustrated by the derivation of a hardware-efficient nonrestoring square-root algorithm, which has also served as an example of theorem prover-based implementation verification.
37778003
Automatic Accurate Cost-Bound Analysis for High-Level Languages
This paper describes a language-based approach for automatic and accurate cost-bound analysis. The approach consists of transformations for building cost-bound functions in the presence of partially known input structures, symbolic evaluation of the cost-bound function based on input size parameters, and optimizations to make the overall analysis efficient as well as accurate, all at the source-language level. The calculated cost bounds are expressed in terms of primitive cost parameters. These parameters can be obtained based on the language implementation or can be measured conservatively or approximately, yielding accurate, conservative, or approximate time or space bounds. We have implemented this approach and performed a number of experiments for analyzing Scheme programs. The results helped confirm the accuracy of the analysis.
15838270
Eliminating Dead Code on Recursive Data
This paper describes a general and powerful method for dead code analysis and elimination in the presence of recursive data constructions. We represent partially dead recursive data using liveness patterns based on general regular tree grammars extended with the notion of live and dead, and we formulate the analysis as computing liveness patterns at all program points based on program semantics. This analysis yields a most precise liveness pattern for the data at each program point, which is significantly more precise than results from previous methods. The analysis algorithm takes cubic time in terms of the size of the program in the worst case but is very efficient in practice, as shown by our prototype implementation. The analysis results are used to identify and eliminate dead code. The general framework for representing and analyzing properties of recursive data structures using general regular tree grammars applies to other analyses as well.
5822903
Automatic Accurate Live Memory Analysis for Garbage-Collected Languages
This paper describes a general approach for automatic and accurate live heap space and live heap space-bound analyses for high-level languages. The approach is based on program analysis and transformations and is fully automatic. The space-bound analysis produces accurate (tight) upper bounds in the presence of partially known input structures. The analyses have been implemented and experimental results confirm their accuracy.
9019278
Querying Complex Graphs
This paper presents a powerful language for querying complex graphs and a method for generating efficient implementations that can answer queries with complexity guarantees. The graphs may have edge labels that may have parameters, and easily and naturally capture complex interrelated objects in object-oriented systems and XML data. The language is built on extended regular path expressions with variables and scoping, and can express queries more easily and clearly than previous query languages.The method for implementation first transforms queries into Datalog with limited extensions. It then extends a previous method to generate specialized algorithms and complexity formulas from Datalog with these extensions.
62201345
Free form shape matching using deterministic annealing and softassign
Free form shape matching is a fundamental problem in both the machine vision and pattern recognition literatures. However, the automatic approach to free form shape matching still remains open. We improve an existing deterministic annealing and softassign method for automatic free form shape matching through incorporating rigid motion constraints. The experiments based on both synthetic data and real images show the effectiveness of the improved algorithm.
45330274
Time Series Prediction by Using Negatively Correlated Neural Networks
Negatively correlated neural networks (NCNNs) have been proposed to design neural network (NN) ensembles [1]. The idea of NCNNs is to encourage different individual NNs in the ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. The cooperation and specialisation among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialise. In this paper, NCNNs are applied to two time series prediction problems (i.e., the Mackey-Glass differential equation and the chlorophyll-a prediction in Lake Kasumigaura). The experimental results show that NCNNs can produce NN ensembles with good generalisation ability.
36759557
Geometrical analysis of two sets of 3D correspondence data patterns
Given two sets of image correspondence data, the analysis of the corresponding rigid body transformation that accurately describes the object's motion in 3D space is a fundamental problem in image understanding. While many methods have been put forward to analyse 3D transformations, such methods do not make use of the vector distance between feature points and angular information as constraints to analyse transformation parameters. Current methods and algorithms have also suffered from the problems of lack of efficiency, sensitivity to noise, and multiplicity of solutions. In this paper we present a novel geometrical analysis of rigid body transformations and a novel algorithm to calibrate transformation parameters based on image correspondence. The algorithm is validated through experimental calibration and a comparison is made with calibration implemented by the least squares method. We demonstrate that the proposed algorithm works well in the presence of noise and that its performance is in general superior to algorithms based on the least squares method.
7923791
Discontinuity-Based Simplification of Free Form Surface From a Range Image
This paper improves an existing method for more accurate simplification of free form surface represented as dense points. The main improvement lies in two aspects: one is the introduction of a novel transformation of curvatures, the other is to propose sampling points using both curvature information and curvature variation information. The experimental results based on real images show that the original object surface has been better approximated and the majority of the surface discontinuities have been better preserved with considerably few sampled (5%) data points
16112032
Fast Evolution Strategies
Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. These results, along with those obtained by fast evolutionary programming