6081408
Universal Cupping Degrees
Cupping nonzero computably enumerable (c.e. for short) degrees to 0′ in various structures has been one of the most important topics in the development of classical computability theory. An incomplete c.e. degree a is cuppable if there is an incomplete c.e. degree b such that a∪b=0′, and noncuppable if there is no such degree b. Sacks splitting theorem shows the existence of cuppable degrees. However, Yates(unpublished) and Cooper [3] proved that there are noncomputable noncuppable degrees. After that, Harrington and Shelah were able to employ the cupping/noncupping properties to show that the theory of the c.e. degrees under relation ≤ is undecidable. Cuppable and noncuppable degrees were further studied later. See Harrington [7], Miller [10], Fejer and Soare [6], Ambos-Spies, Lachlan and Soare [1], etc..
1961392
Degrees of Weakly Computable Reals
This paper studies the degrees of weakly computable reals. It is shown that certain types of limit-recursive reals are Turing incomparable to all weakly computable reals except the recursive and complete ones. Furthermore, it is shown that an r.e. Turing degree is array-recursive iff every real in it is weakly computable.
1638130
Bounding computably enumerable degrees in the Ershov hierarchy
Nanyang Technol Univ, Sch Math & Phys Sci, Div Math Sci, Singapore 639798, Singapore. Chinese Acad Sci, Inst Software, Beijing 100080, Peoples R China. Natl Univ Singapore, Dept Math, Singapore 119260, Singapore.
9638835
Isolation and Lattice Embeddings
18609322
On the Quotient Structure of Computably Enumerable Degrees Modulo the Noncuppable Ideal
We show that minimal pairs exist in the quotient structure of $\mathcal{R}$ modulo the ideal of noncuppable degrees.
7723209
1-Generic splittings of computably enumerable degrees
Say a set G⊆ω is 1-generic if for any e∈ω, there is a string σ⊂G such that {e}σ(e)↓ or ∀τ⊇σ({e}τ(e)↑). It is known that 0′ can be split into two 1-generic degrees. In this paper, we generalize this and prove that any nonzero computably enumerable degree can be split into two 1-generic degrees. As a corollary, no two computably enumerable degrees bound the same class of 1-generic degrees.
7109499
Statistical learning for effective visual information retrieval
For effective retrieval of visual information, statistical learning plays a pivotal role. Statistical learning in such a context faces at least two major mathematical challenges: scarcity of training data, and imbalance of training classes. We present these challenges and outline our methods for addressing them: active learning, recursive subspace co-training, adaptive dimensionality reduction, class-boundary alignment, and quasi-bagging.
1677219
Application of Smodels in Quartet Based Phylogeny Construction
Evolution is an important sub-area of study in biological science, where given a set of taxa, the goal is to reconstruct their evolutionary history, or phylogeny. One very recent approach is to predict a local phylogeny for every subset of 4 taxa, called a quartet topology, and then to assemble a phylogeny for the whole set of taxa satisfying these predicted quartet topologies. In general, the predicted quartet topologies might not always agree with each other, and thus the objective function becomes to satisfy a maximum number of them. This is the well known Maximum Quartet Consistency (MQC) problem. In the past, the MQC problem has been solved by dynamic programming and the so-called fixed-parameter method. Recently, we have proposed to solve the MQC in answer set programming. In this note, we summarize the theoretical results of this approach and report new experimental results for the purpose of comparison, which show that our approach in answer set programming is favored over the existing approaches based on dynamic programming and fixed-parameter method. In particular, some of the hard instances (where the error ratio is high) that were not reported to be solved in other approaches can now be solved in our approach.
2439726
Concept boundary detection for speeding up SVMs
Support Vector Machines (SVMs) suffer from an O(n2) training cost, where n denotes the number of training instances. In this paper, we propose an algorithm to select boundary instances as training data to substantially reduce n. Our proposed algorithm is motivated by the result of (Burges, 1999) that, removing non-support vectors from the training set does not change SVM training results. Our algorithm eliminates instances that are likely to be non-support vectors. In the concept-independent preprocessing step of our algorithm, we prepare nearest-neighbor lists for training instances. In the concept-specific sampling step, we can then effectively select useful training data for each target concept. Empirical studies show our algorithm to be effective in reducing n, outperforming other competing downsampling algorithms without significantly compromising testing accuracy.
18105952
Adapting Prime Number Labeling Scheme for Directed Acyclic Graphs
Directed Acyclic Graph(DAG) could be used for modeling subsumption hierarchies. Several labeling schemes have been proposed or tailored for indexing DAG in order to efficiently explore relationships in such hierarchy. However few of them can satisfy all the requirements in response time, space, and effect of updates simultaneously. In this paper, the prime number labeling scheme is extended for DAG. The scheme invests intrinsic mapping between integer divisibility and subsumption hierarchy, which simplifies the transitive closure computations and diminishes storage redundancy, as well as inherits the dynamic labeling ability from original scheme. Performance is further improved by introducing some optimization techniques. Our extensive experimental results show that prime number labeling scheme for DAG outperforms interval-based and prefix-based labeling schemes in most cases.
16585637
Learning with non-metric proximity matrices
Many emerging applications formulate non-metric proximity matrices (non-positive semidefinite), and hence cannot fit into the framework of kernel machines. A popular approach to this problem is to transform the spectrum of the similarity matrix so as to generate a positive semidefinite kernel matrix. In this paper, we explore four representative transformation methods: denoise, flip, diffusion, and shift. Theoretically, we discuss a generalization problem where the test data are not available during transformation, and thus propose an efficient algorithm to address the problem of updating the cross-similarity matrix between test and training data. Extensive experiments have been conducted to evaluate the performance of these methods on several real-world (dis)similarity matrices with semantic meanings.