16271421
Weak Evolvability Equals Strong Evolvability
A process for the purification of caffeine which comprises carrying out the following steps in any order; (a) recrystallization of caffeine from an aqueous alkaline solution of caffeine containing a reducing agent and (b) extraction of the caffeine from an aqueous alkaline solution of caffeine with a substantially water-immiscible solvent which is liquid under the conditions of the process.
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.
37422743
A Framework for Modeling Positive Class Expansion with Single Snapshot
8259100
Adapt Bagging to Nearest Neighbor Classifiers
It is well-known that in order to build a strong ensemble, the component learners should be with high diversity as well as high accuracy. If perturbing the training set can cause significant changes in the component learners constructed, then Bagging can effectively improve accuracy. However, for stable learners such as nearest neighbor classifiers, perturbing the training set can hardly produce diverse component learners, therefore Bagging does not work well. This paper adapts Bagging to nearest neighbor classifiers through injecting randomness to distance metrics. In constructing the component learners, both the training set and the distance metric employed for identifying the neighbors are perturbed. A large scale empirical study reported in this paper shows that the proposed BagInRand algorithm can effectively improve the accuracy of nearest neighbor classifiers.
2550685
Ensembling local learners ThroughMultimodal perturbation
Ensemble learning algorithms train multiple component learners and then combine their predictions. In order to generate a strong ensemble, the component learners should be with high accuracy as well as high diversity. A popularly used scheme in generating accurate but diverse component learners is to perturb the training data with resampling methods, such as the bootstrap sampling used in bagging. However, such a scheme is not very effective on local learners such as nearest-neighbor classifiers because a slight change in training data can hardly result in local learners with big differences. In this paper, a new ensemble algorithm named Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR) is proposed for building ensembles of local learners, which utilizes multimodal perturbation to help generate accurate but diverse component learners. In detail, FASBIR employs the perturbation on the training data with bootstrap sampling, the perturbation on the input attributes with attribute filtering and attribute subspace selection, and the perturbation on the learning parameters with randomly configured distance metrics. A large empirical study shows that FASBIR is effective in building ensembles of nearest-neighbor classifiers, whose performance is better than that of many other ensemble algorithms.
14448098
Research on Dynamic Task Assignment in Grid Workflow System
Task assignment in grid workflow system should be designed to adapt well with the grid which allow people from different organizations to work together in the same workflow process. To ensure grid workflow systems could assign the task to the most suitable person, a novel solution is proposed firstly. Then two description languages GHRSL and HRRL are defined. GHRSL (grid human resource specification language) describes people as grid resources. HRRL (human resources requirement language) describes human resources requirement of workflow tasks. Finally, the evaluation method and the algorithm of dynamic task assignment are also proposed. Based on these, the grid workflow system could assign tasks dynamically to the correct person(s)
9176644
Automated and safe vulnerability assessment
As the number of system vulnerabilities multiplies in recent years, vulnerability assessment has emerged as a powerful system security administration tool that can identify vulnerabilities in existing systems before they are exploited. Although there are many commercial vulnerability assessment tools in the market, none of them can formally guarantee that the assessment process never compromises the computer systems being tested. This paper proposes a featherweight virtual machine (FVM) technology to address the safety issue associated with vulnerability testing. Compared with other virtual machine technologies, FVM is designed to facilitate sharing between virtual machines but still provides strong protection between them. The FVM technology allows a vulnerability assessment tool to test an exact replica of a production-mode network service, including both hardware and system software components, while guaranteeing that the production-mode network service is fully isolated from the testing process. In addition to safety, the vulnerability assessment support system described in this paper can also automate the entire process of vulnerability testing and thus for the first time makes it feasible to run vulnerability testing autonomously and frequently. Experiments on a Windows-based prototype show that Nessus assessment results against an FVM virtual machine are identical to those against a real machine. Furthermore, modifications to the file system and registry state made by vulnerability assessment runs are completely isolated from the host machine. Finally, the performance impact of vulnerability assessment runs on production network services is as low as 3%
14893914
Energy-balanced task allocation for collaborative processing in networked embedded systems
In this paper, we study the problem of allocating a real-time application onto a set of homogeneous processing elements connected by a single-hop wireless network. A periodic application consisting of a set of communicating tasks is considered. Each element is equipped with discrete dynamic voltage scaling for exploring the energy-latency tradeoffs. The time and energy costs of both computation and communication activities are considered. The goal is to balance the energy dissipation of the elements during each period of the application with respect to the remaining energy of elements, such that the system lifetime is maximized. An Integer Linear Programming (ILP) formulation is first developed, which can be solved to obtain the optimal solution. We then propose an efficient 3-phase heuristic. Experimental results show that for small scale problems, the performance of the heuristic achieves up to 85% of the system lifetime obtained by the ILP-based approach. For large scale problems, the performance of the heuristic shows an improvement of 120-250\% in the system lifetime compared with the case where no voltage scaling is used. Further, we present two extensions of our approaches that consider multiple communication channels and techniques for exploring the energy-latency tradeoffs of the communication activities.
6183487
Practical routing and torus assignment for RDT
The connection topology of interconnection networks and the routing algorithm often dominate system performance in MPCs. RDT has already been proved to be one of the best interconnection networks used in such systems. It supports a smaller diameter and degree than most at the size of one thousand to tens of thousands nodes. The floating vector routing algorithm is very effective for transferring messages, which also shows good performance. In this paper, a descending vector routing algorithm is proposed. We also proposed a practical torus assignment named RDT(2,2,1)/a and the deadlock-free routing algorithm. RDT(2,2,1)/a mainly focuses on the MPC that has about one thousand nodes. With a simplified simulation and comparison, the results show that RDT(2,2,1)/a has a better performance at the size of one thousand nodes.
6083994
On communication models for algorithm design in networked sensor systems: A case study,
Towards building a systematic methodology of algorithm design for applications of networked sensor systems, we formally define two link-wise communication models, the Collision Free Model (CFM) and the Collision Aware Model (CAM). While CFM provides ease of programming and analysis for high level application functionality, CAM enables more accurate performance analysis and hence more efficient algorithms through cross-layer optimization, at the expense of increased programming and analysis complexity. These communication models are part of an abstract network model, above which algorithm design and performance optimization is performed. We use the example of optimizing a probability based broadcasting scheme under CAM to illustrate algorithm optimization based on the defined models. Specifically, we present an analytical framework that facilitates an accurate modeling and analysis for the probability based broadcasting in CAM (PB_CAM). Our analytical results indicate that (1) the optimal broadcast probability for either maximizing the reachability within a given latency constraint or minimizing the latency for a given reachability constraint decreases rapidly with node density, and (2) the optimal probability for either maximizing the reachability with a given energy constraint or minimizing the energy cost for a given reachability constraint varies slowly between 0 and 0.1 over the entire range of the variations in node density. Our analysis is also confirmed by extensive simulation results.