33025668
The Permanent Rank of a Matrix
Define the perrank of a matrixAto be the size of the largest square submatrix ofAwith nonzero permanent. Motivated in part by the Alon?Jaeger?Tarsi Conjecture [3], we prove several results on perranks..
1241550
Spectrum of Variable-Random Trees
In this paper, we show that a continuous spectrum of randomisation exists, in which most existing tree randomisations are only operating around the two ends of the spectrum. That leaves a huge part of the spectrum largely unexplored. We propose a base learner VR-Tree which generates trees with variable-randomness. VR-Trees are able to span from the conventional deterministic trees to the complete-random trees using a probabilistic parameter. Using VR-Trees as the base models, we explore the entire spectrum of randomised ensembles, together with Bagging and Random Subspace. We discover that the two halves of the spectrum have their distinct characteristics; and the understanding of which allows us to propose a new approach in building better decision tree ensembles. We name this approach Coalescence, which coalesces a number of points in the random-half of the spectrum. Coalescence acts as a committee of "experts" to cater for unforeseeable conditions presented in training data. Coalescence is found to perform better than any single operating point in the spectrum, without the need to tune to a specific level of randomness. In our empirical study, Coalescence ranks top among the benchmarking ensemble methods including Random Forests, Random Subspace and C5 Boosting; and only Coalescence is significantly better than Bagging and Max-Diverse Ensemble among all the methods in the comparison. Although Coalescence is not significantly better than Random Forests, we have identified conditions under which one will perform better than the other.
15248817
TEFE: A Time-Efficient Approach to Feature Extraction
With the rapid evolution of Internet applications, people all over the world are sharing pictures, videos and audios online, and thus, content-based analysis is often demanded. Test efficiency is crucial to the success of online information processing. One obstacle to high-speed testing is the time cost of feature extraction for test objects, particularly for objects with complex representation such as images, videos and audios. In this paper, we study the problem of reducing test time cost by extracting cheap but sufficient features. We propose the TEFE (time-efficient feature extraction) approach, which balances between the test accuracy and test time cost by extracting a proper subset of features for each test object. In the implementation, TEFE trains a sequence of support vector machines and classifies each test object cascadingly. Empirical study shows that TEFE is time efficient while holding a classification accuracy close to that of using all features. It also shows that the test time is linearly adjustable in TEFE.
15348979
Cocktail Ensemble for Regression
This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formally analyze the optimal linear combination of two base ensembles, which is then extended to multiple individual ensembles via pairwise combinations. The Cocktail ensemble approach is proposed based on this analysis. Experiments over a broad range of data sets show that the proposed approach outperforms the individual ensembles, two other methods of ensemble combination, and two state-of-the-art regression approaches.
5420099
A new approach to estimating the expected first hitting time of evolutionary algorithms
The expected first hitting time is an important issue in theoretical analyses of evolutionary algorithms since it implies the average computational time complexity. In this paper, by exploiting the relationship between the convergence rate and the expected first hitting time, a new approach to estimating the expected first hitting time is proposed. This approach is then applied to four evolutionary algorithms which involve operators of mutation, mutation with population, mutation with recombination, and time-variant mutation, respectively. The results show that the proposed approach is helpful for analyzing a broad range of evolutionary algorithms.
14236359
Semi-naive Exploitation of One-Dependence Estimators
It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies, one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies whilst keeping the effectiveness of probability estimation, and therefore leading to excellent performance. In previous studies, however, ODEs were exploited in simple ways, such as by averaging, for classification. In this paper, we propose a semi-naive exploitation of ODEs that fits a function of ODEs to pursue higher-order attribute dependencies. Extensive experiments show that the proposed SNODE approach can achieve better performance than many state-of-the-art Bayesian classifiers.
18554601
Fault-tolerant routing algorithm for RDT structure
RDT (recursive diagonal torus) is a class of interconnection networks for massively parallel computers (MPCs). It consists of recursively structured mesh/torus connections. It has been proved that RDT supports better performances than most direct networks. SINOMP designed for the structure simulation and performance analysis between RDT and those networks is proposed. The simulation results also prove the good performances of RDT. In this paper, the fault-tolerant routing algorithms on the RDT structure are proposed and discussed. They select one of the shortest routes computed with the routing vector on the non-faulty network by using the different rank order. The additional hops are used to send packet only when all the shortest routes have faulty links/nodes. The proposed algorithms need no additional virtual channels for fault tolerant and support a reasonably large number of faults without significantly degrading the system performance.
13388104
MetaData Pro: Ontology-Based Metadata Processing for Web Resources
Metadata is the foundation of Semantic Web. The MetaData Pro project seeks to build a metadata processing platform for web resource. The system architecture has three key components– Metadata Extraction, Ontology Management, and Metadata Retrieval. The system can automatically extract metadata about web resource: if the web resource itself contains metadata, extracts them; otherwise, automatically generates the metadata for the resource according to Dublin Core by applying automatic keyword extraction and text summarization techniques. To manage the metadata, MetaData Pro integrates Protege to create domain ontology, makes use of HowNet to help ontology construction, and provides an ontology-based metadata retrieval.
14114624
A metric and mixed-integer-programming-based approach for resource allocation in dynamic real-time systems
Dynamic real-time systems such as embedded systems operate in environments in which several parameters vary at run time. These systems must satisfy several performance requirements. Resource allocation on these systems becomes challenging because variations of run-time parameters may cause violations of the performance requirements. Performance violations result in the need for dynamic re-allocation, which is a costly operation. A method for allocating resources such that the allocation can sustain the system in the light of a continuously changing environment is developed. We introduce a novel performance metric called MAIL (maximum allowable increase in load) to capture the effectiveness of a resource allocation. Given a resource allocation, MAIL quantifies the amount of additional load that can be sustained by the system without any performance violations. A mixed-integer-programming-based approach (MIP) is developed to determine a resource allocation that has the highest MAIL value. Using simulations, several sets of experiments are conducted to evaluate our heuristics in various scenarios of machine and task heterogeneities. The performance of MIP is compared with three other heuristics: integer-programming based, greedy, and classic min-min. Our results show that MIP performs significantly better when compared with the other heuristics.
20364817
Log-Concave Functions And Poset Probabilities
elements of some (finite) poset , write for the probability that precedes in a random (uniform) linear extension of . For define where the infimum is over all choices of and distinct .Addressing an issue raised by Fishburn [6], we give the first nontrivial lower bounds on the function . This is part of a more general geometric result, the exact determination of the function where the infimum is over chosen uniformly from some compact convex subset of a Euclidean space.These results are mainly based on the Brunn–Minkowski Theorem and a theorem of Keith Ball [1], which allow us to reduce to a 2-dimensional version of the problem.
5882124
A feather-weight virtual machine for windows applications
Many fault-tolerant and intrusion-tolerant systems require the ability to execute unsafe programs in a realistic environment without leaving permanent damages. Virtual machine technology meets this requirement perfectly because it provides an execution environment that is both realistic and isolated. In this paper, we introduce an OS level virtual machine architecture for Windows applications called Feather-weight Virtual Machine (FVM), under which virtual machines share as many resources of the host machine as possible while still isolated from one another and from the host machine. The key technique behind FVM is namespace virtualization, which isolates virtual machines by renaming resources at the OS system call interface. Through a copy-on-write scheme, FVM allows multiple virtual machines to physically share resources but logically isolate their resources from each other. A main technical challenge in FVM is how to achieve strong isolation among different virtual machines and the host machine, due to numerous namespaces and interprocess communication mechanisms on Windows. Experimental results demonstrate that FVM is more flexible and scalable, requires less system resource, incurs lower start-up and run-time performance overhead than existing hardware-level virtual machine technologies, and thus makes a compelling building block for security and fault-tolerant applications.