39617215
Counting rooted near-triangulations on the sphere
This paper provides the results on the enumerations of rooted simple outerplanar maps, rooted outerplanar near-triangulations, rooted 2-connected near-triangulations, rooted strict 2-connected near-triangulations and rooted simple 2-connected near-triangulations. The answer to an open problem proposed by one of the authors is also provided.
43124802
On the Isomorphisms of Cayley Graphs of Abelian Groups
Let G be a finite group, S a subset of G\{1}, and let Cay (G, S) denote the Cayley digraph of G with respect to S. If, for any subset T of G\{1}, Cay(G, S) ≅ Cay(G, T) implies that Sα = T for some α ∈ Aut(G), then S is called a CI-subset. The group G is called a CIM-group if for any minimal generating subset S of G, S ∪ S-1 is a CI-subset. In this paper, CIM-abelian groups are characterized.
43519756
The Number of Loopless 4-Regular Maps on the Projective Plane
In this paper rooted loopless (near) 4-regular maps on surfaces such as the sphere and the projective plane are counted and exact formulae with up to three or four parameters for such maps are given. Several classical results on regular maps and one-faced maps are deduced.
389729
Predicting protein folds with structural repeats using a chain graph model
Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeats are prevalent in proteins involved in a wide spectrum of biological functions. However, extant approaches often perform inadequately due to their inability to capture long-range interactions between structural units and to handle low sequence similarities across proteins (under 25% identity). In this paper, we propose a chain graph model built on a causally connected series of segmentation conditional random fields (SCRFs) to address these issues. Specifically, the SCRF model captures long-range interactions within recurring structural units and the Bayesian network backbone decomposes cross-repeat interactions into locally computable modules consisting of repeat-specific SCRFs and a model for sequence motifs. We applied this model to predict β-helices and leucine-rich repeats, and found it significantly outperforms extant methods in predictive accuracy and/or computational efficiency.
12839905
Context sensitive vocabulary and its application in protein secondary structure prediction
Protein secondary structure prediction is an important step towards understanding the relation between protein sequence and structure. However, most current prediction methods use features difficult for biologists to interpret. In this paper, we present a new method that applies information retrieval techniques to solve the problem:we extract a context sensitive biological vocabulary for protein sequences and apply text classification methods to predict protein secondary structure. Experimental results show that our method performs comparably to the state-of-art methods. Furthermore, the context sensitive vocabularies can serve as a useful tool to discover meaningful regular expression patterns for protein structures.
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.
15475945
Performance prediction of component-based applications
One of the major problems in building large-scale enterprise systems is anticipating the performance of the eventual solution before it has been built. The fundamental software engineering problem becomes more difficult when the systems are built on component technology. This paper investigates the feasibility of providing a practical solution to this problem. An empirical approach is proposed to determine the performance characteristics of component-based applications by benchmarking and profiling. Based on observation, a model is constructed to act as a performance predictor for a class of applications based on the specific component technology. The performance model derived from empirical measures is necessary to make the problem tractable and the results relevant. A case study applies the performance model to an application prototype implemented by two component infrastructures: CORBA and J2EE.
39342031
An Experimental Investigation of Self-Adaptation in Evolutionary Programming
Evolutionary programming (EP) has been widely used in numerical optimization in recent years. One of EP's key features is its self-adaptation scheme. In EP, mutation is typically the only operator used to generate new offspring. The mutation is often implemented by adding a random number from a certain distribution (e.g., Gaussian in the case of classical EP) to the parent. An important parameter of the Gaussian distribution is its standard deviation (or equivalently the variance). In the widely used self-adaptation scheme of EP, this parameter is evolved, rather than manually fixed, along with the objective variables. This paper investigates empirically how well the self-adaptation scheme works on a set of benchmark functions. Some anomalies have been observed in the empirical studies, which demonstrate that the self-adaptation scheme may not work as well as hoped for some functions. An experimental evaluation of an existing simple fix to the problem is also carried out in this paper.