10706283
Evolutionary feature synthesis for facial expression recognition
Feature extraction is one of the key steps in object recognition. In this paper we propose a novel genetically inspired learning method for facial expression recognition (FER). Unlike current research on facial expression recognition that generally selects visually meaningful feature by hands, our learning method can discover the features automatically in a genetic programming-based approach that uses Gabor wavelet representation for primitive features and linear/nonlinear operators to synthesize new features. These new features are used to train a support vector machine classifier that is used for recognizing the facial expressions. The learned operator and classifier are used on unseen testing images. To make use of random nature of a genetic program, we design a multi-agent scheme to boost the performance. We compare the performance of our approach with several approaches in the literature and show that our approach can perform the task of facial expression recognition effectively.
13892368
Super-resolution Restoration of Facial Images in Video
Reconstruction-based super-resolution has been widely treated in computer vision. However, super-resolution of facial images has received very little attention. Since different parts of a face may have different motions in normal videos, this paper proposes a new method for enhancing the resolution of low-resolution facial image by handling the facial image non-uniformly. We divide low-resolution face image into different regions based on facial features and estimate motions of each of these regions using different motion models. Our experimental results show we can achieve better results than applying super-resolution on the whole face image uniformly
552016
Feature Synthesis Using Genetic Programming for Face Expression Recognition
In this paper a novel genetically-inspired learning method is proposed for face expression recognition (FER) in visible images. Unlike current research for FER that generally uses visually meaningful feature, we proposed a Genetic Programming based technique, which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. In this approach, the output of the learned composite operator is a feature vector that is used for FER. The experimental results show that our approach can find good composite operators to effectively extract useful features.
27420151
Recent Advances in PC-Linux Systems for Electronic Structure Computations by Optimized Compilers and Numerical Libraries
One of the most frequently used packages for electronic structure research, GAUSSIAN 98, is compiled on Linux systems with various hardware configurations, including AMD Athlon (with the "Thunderbird" core), AthlonMP, and AthlonXP (with the "Palomino" core) systems as well as the Intel Pentium 4 (with the "Willamette" core) machines. The default PGI FORTRAN compiler (pgf77) and the Intel FORTRAN compiler (ifc) are respectively employed with different architectural optimization options to compile GAUSSIAN 98 and test the performance improvement. In addition to the BLAS library included in revision A.11 of this package, the Automatically Tuned Linear Algebra Software (ATLAS) library is linked against the binary executables to improve the performance. Various Hartree-Fock, density-functional theories, and the MP2 calculations are done for benchmarking purposes. It is found that the combination of ifc with ATLAS library gives the best performance for GAUSSIAN 98 on all of these PC-Linux computers, including AMD and Intel CPUs. Even on AMD systems, the Intel FORTRAN compiler invariably produces binaries with better performance than pgf77. The enhancement provided by the ATLAS library is more significant for post-Hartree-Fock calculations. The performance on one single CPU is potentially as good as that on an Alpha 21264A workstation or an SGI supercomputer. The floating-point marks by SpecFP2000 have similar trends to the results of GAUSSIAN 98 package.
15858355
Asymptotics of Efficiency Loss in Competitive Market Mechanisms
We consider the loss of efficiency in competitive market mechanisms used to allocate network resources. We model large heterogeneous populations of users assuming that each user has a random utility function. We show that if the utility functions are bounded, the competitive equilibrium will be nearly as efficient as the social optimum with high probability as the number of users increases. This is the case for inelastic capacity as well as elastic capacity, under some standard assumptions. This result extends to a network setup where sources and destinations are picked at random. If, however, the utility functions are not bounded, then the loss of efficiency does not converge to zero. Collaborating simulations are also presented.
10613404
Enhancing Network Processor Simulation Speed with Statistical Input Sampling
While cycle-accurate simulation tools have been widely used in modeling high-performance processors, such an approach can be hindered by the increasing complexity of the simulation, especially in modeling chip multi-proce- ssors with multi-threading such as the network processors (NP). We have observed that for NP cycle level simulation, several days of simulation time covers only about one second of the real-world network traffic. Existing approaches to accelerating simulation are through either code analysis or execution sampling. Unfortunately, they are not applicable in speeding up NP simulations due to the small code size and the iterative nature of NP applications. We propose to sample the traffic input to the NP so that a long packet trace is represented by a much shorter one with simulation error bounded within ±3% and 95% confidence. Our method resulted one order of magnitude improvement in the NP simulation speed.
1926620
Program Mapping onto Network Processors by Recursive Bipartitioning and Refining
Mapping packet processing applications onto embedded network processors (NP) is a challenging task due to the unique constraints of NP systems and the characteristics of network application domains. A remarkable difference with general multiprocessor task scheduling is that NPs are often programmed into a hybrid parallel and pipeline topology. In this paper, we introduce a multilevel balancing and refining algorithm for NP program mapping. We use a divide- and-conquer approach to recursively bipartition the task graph into disjoint subdomains. At each level of bipartition, the processing resources will be co-allocated so that an estimation of throughput can be derived. The bipartition continues until the code of the tasks can be fit into the instruction memory of processing elements. Then the algorithm iteratively refines the solution by migrating tasks from the bottleneck stage to other stages. The performance of our scheme is evaluated with a suite of NP benchmarks using SUIF/Machine SUIF compiler and Intel IXA Architecture Tool. The throughput improvement is significant: average throughput is increased by 20%, and the maximum is 108%.
1840510
Evolution of a Controller with a Free Variable Using Genetic Programming
A mathematical formula containing one or more free variables is “general” in the sense that it provides a solution to an entire category of problems. For example, the familiar formula for solving a quadratic equation contains free variables representing the equation’s coefficients. Previous work has demonstrated that genetic programming can automatically synthesize the design for a controller consisting of a topological arrangement of signal processing blocks (such as integrators, differentiators, leads, lags, gains, adders, inverters, and multipliers), where each block is further specified (“tuned”) by a numerical component value, and where the evolved controller satisfies user-specified requirements. The question arises as to whether it is possible to use genetic programming to automatically create a “generalized” controller for an entire category of such controller design problems — instead of a single instance of the problem. This paper shows, for an illustrative problem, how genetic programming can be used to create the design for ‘both the topology and tuning of controller, where the controller contains a free variable.