Non-redundant Patterns Research Articles

Using Permutation Tests to Identify Statistically Sound and Nonredundant Sequential Patterns in Educational Event Sequences

Frequent sequential pattern mining is a valuable technique for capturing the relative arrangement of learning events, but current algorithms often return excessive learning event patterns, many of which may be noise or redundant. These issues exacerbate researchers’ burden when interpreting the patterns to derive actionable insights into learning processes. This study proposed permutation tests for identifying sequential patterns whose occurrences are statistically significantly greater than the chance value and different from their superpatterns. Simulations demonstrated that the test for detecting sound patterns had a low false discovery rate and high power, while the test for detecting nonredundant patterns also showed a high accuracy. Empirical data analyses found that the patterns detected in training data were generalizable to test data.

Crosstalk in image plane beam combination for optical interferometers

ABSTRACT Image plane beam combination in optical interferometers multiplexes the interference fringes from multiple baselines on to a single detector. The beams of starlight are arranged in a non-redundant pattern at the entrance of the combiner so that the signal from each baseline can be separated from one another in the frequency domain. If the signals from different baselines overlap in the frequency domain, this can give rise to a systematic error in the fringe measurements known as baseline crosstalk. In this paper, we quantify crosstalk arising from the combination of atmospheric seeing and beam propagation over distances of the order of hundreds of metres. We find that in idealized conditions atmospheric wavefront errors and beam propagation do not contribute to crosstalk. However, when aperture stops are included in the optical beam train we observe that wavefront errors can result in squared visibility errors arising from crosstalk as high as ΔV2 = 6.6 × 10−3 under realistic observing conditions.

Interpretable logic tree analysis: A data-driven fault tree methodology for causality analysis

This paper proposes an effective hybrid-based methodology, called interpretable logic tree analysis (ILTA), which characterizes and quantifies event causality occurring in engineering systems with the minimum involvement of human experts. It integrates two concepts: knowledge discovery in database (KDD) and fault tree analysis (FTA). The KDD extracts the root-causes in the form of a set of interpretable (meaningful) patterns and then is exploited to automatically construct a logic tree. Only the feasible solutions consisting of non-redundant patterns that cover the maximum number of observations in the dataset are selected using a burn-and-build algorithm. These solutions are employed first to visualize the discovered knowledge under the interpretable logic tree and second, to estimate the probability of an event given the occurrence of its root-causes. An actuator system dataset is used to illustrate and validate the proposed methodology. Moreover, the ILTA methodology allows the tuning of the system states based on Bayesian control rules that characterize the nature of the discovered root-causes.

A hash-based algorithm for mining non-redundant asynchronous periodic patterns

Periodic patterns occur in a wide variety of datasets like time series, temporal, spatiotemporal and biological datasets. Asynchronous periodic pattern mining discovers the patterns that appear significantly with strict periodicity in one or more subsequences called valid segments with tolerable disturbance between the valid segments. The state-of-the-art asynchronous periodic pattern mining algorithms proposed in the literature mine many redundant asynchronous periodic patterns. This paper proposes an algorithm to mine non-redundant asynchronous periodic patterns. It is a variation of a four-phase algorithm named SMCA. The proposed algorithm mines precise and concise set of non-redundant patterns whose number is in orders of magnitude smaller than that of previous methods. The proposed algorithm is explained using a hypothetical dataset and its performance is evaluated using three real data sets which are stock market data from Bombay Stock Exchange, Weather data from Cambridge University and Migratory Zebras' data from MoveBank.

A Hash-based Algorithm for Mining Non-redundant Asynchronous Periodic Patterns

Periodic patterns occur in a wide variety of datasets like time series, temporal, spatiotemporal and biological datasets. Asynchronous periodic pattern mining discovers the patterns that appear significantly with strict periodicity in one or more subsequences called valid segments with tolerable disturbance between the valid segments. The state-of-the-art asynchronous periodic pattern mining algorithms proposed in the literature mine many redundant asynchronous periodic patterns. This paper proposes an algorithm to mine non-redundant asynchronous periodic patterns. It is a variation of a four-phase algorithm named SMCA. The proposed algorithm mines precise and concise set of non-redundant patterns whose number is in orders of magnitude smaller than that of previous methods. The proposed algorithm is explained using a hypothetical dataset and its performance is evaluated using three real data sets which are stock market data from Bombay Stock Exchange, Weather data from Cambridge University and Migratory Zebras' data from MoveBank.

Discovery of Delta Closed Patterns and Noninduced Patterns from Sequences

Discovering patterns from sequence data has significant impact in many aspects of science and society, especially in genomics and proteomics. Here we consider multiple strings as input sequence data and substrings as patterns. In the real world, usually a large set of patterns could be discovered yet many of them are redundant, thus degrading the output quality. This paper improves the output quality by removing two types of redundant patterns. First, the notion of delta tolerance closed itemset is employed to remove redundant patterns that are not delta closed. Second, the concept of statistically induced patterns is proposed to capture redundant patterns which seem to be statistically significant yet their significance is induced by their strong significant subpatterns. It is computationally intense to mine these nonredundant patterns (delta closed patterns and noninduced patterns). To efficiently discover these patterns in very large sequence data, two efficient algorithms have been developed through innovative use of suffix tree. Three sets of experiments were conducted to evaluate their performance. They render excellent results when applying to genomics. The experiments confirm that the proposed algorithms are efficient and that they produce a relatively small set of patterns which reveal interesting information in the sequences.

Conveying indirect reservations through discursive redundancy

This paper proposes a definition of redundancy whereby if a discourse pattern P1 is used to achieve speaker’s meaning S, and discourse patterns P2 may equally achieve speaker’s meaning S, then the use of both P1 and P2 in a given textual environment will be conceived of as redundant. Focusing on discourse patterns implying reservations in the written press in French and in Hebrew, it postulates two types of redundancy: conventional redundancy, whereby the identification of co-textual cues relies on a conventional combination, and non-conventional redundancy, which includes various combinations of patterns, supplemented by context-dependent explicitations. The textual analysis distinguishes between redundant and non-redundant patterns of reservations, and indicates a tendency towards redundancy in both languages, with an inclination towards a higher degree of informativeness in Hebrew. Findings are interpreted in terms of Gricean Cooperative Principle as well as the postulate of Quantity Scales, previously argued for by the author.

Injury-induced mechanical hypersensitivity requires C-low threshold mechanoreceptors

Mechanical pain contributes to the morbidity associated with inflammation and trauma, but primary sensory neurons that convey the sensation of acute and persistent mechanical pain have not been identified. Dorsal root ganglion (DRG) neurons transmit sensory information to the spinal cord using the excitatory transmitter glutamate1, a process that depends on glutamate transport into synaptic vesicles for regulated exocytotic release. Here we report that a small subset of cells in the DRG expresses the low abundance vesicular glutamate transporter VGLUT3. In the dorsal horn of the spinal cord, these afferents project to lamina I and the innermost layer of lamina II, which has previously been implicated in persistent pain caused by injury2. Since the different VGLUT isoforms generally exhibit a nonredundant pattern of expression3, we used VGLUT3 knock-out (KO) mice to assess the role of VGLUT3+ primary afferents in the behavioral response to somatosensory input. The loss of VGLUT3 specifically impairs mechanical pain sensation and in particular the mechanical hypersensitivity to normally innocuous stimuli that accompanies inflammation, nerve injury and trauma. Direct recording from VGLUT3+ neurons in the DRG further identifies them as a poorly understood population of unmyelinated, low threshold mechanoreceptors (C-LTMRs)4,5. The analysis of VGLUT3 KO mice now indicates a critical role for C-LTMRs in the mechanical hypersensitivity caused by injury.

CyMATE: a new tool for methylation analysis of plant genomic DNA after bisulphite sequencing

Cytosine methylation is a hallmark of epigenetic information in the DNA of many fungi, vertebrates and plants. The technique of bisulphite genomic sequencing reveals the methylation state of every individual cytosine in a sequence, and thereby provides high-resolution data on epigenetic diversity; however, the manual evaluation and documentation of large amounts of data is laborious and error-prone. While some software is available for facilitating the analysis of mammalian DNA methylation, which is found nearly exclusively at CG sites, there is no software optimally suited for data from DNA with significant non-CG methylation. We describe CyMATE (Cytosine Methylation Analysis Tool for Everyone) for in silico analysis of DNA sequences after bisulphite conversion of plant DNA, in which methylation is more divergent with respect to sequence context and biological relevance. From aligned sequences, CyMATE includes and distinguishes methylation at CG, CHG and CHH (where H = A, C or T), and can extract both quantitative and qualitative data regarding general and pattern-specific methylation per sequence and per position, i.e. data for individual sites in a sequence and the epigenetic divergence within a sample. In addition, it can provide graphical output from alignments in either an overview or a 'zoom-in' view as pdf files. Detailed information, including a quality control of the sequencing data, is provided in text format. We applied CyMATE to the analysis of DNA methylation at transcriptionally silenced promoters in diploid and polyploid Arabidopsis and found significant hypermethylation, high stability of the methylated state independent of chromosome number, and non-redundant patterns of mC distribution. CyMATE is freely available for non-commercial use at http://www.gmi.oeaw.ac.at/CyMATE.

An Ecological Critique of the Sensory Conflict Theory of Motion Sickness

In this article we present a critique of the sensory conflict theory of motion sickness. We discuss three forms of sensory conflict that are believed to exist: input conflict, output conflict, and expectancy violation. We argue that any concept of sensory conflict necessarily entails the violation of internal expectations and, hence, that the three forms of conflict are logically identical. In the motion sickness literature, it is implicit that the expected pattern is one in which the inputs of the visual, vestibular, and somatosensory systems are redundant. We argue that an expectation of redundancy cannot plausibly result from interaction with the environment. We review data indicating that nonredundant patterns of intermodal stimulation lead to adaptive changes in the control of behavior; this implies that the calculation of nauseogenic conflict must be unrelated to the control of behavior. Moreover, we argue that no single pattern of sensory stimulation, redundant or otherwise, can serve as a reliable...

Information processing in the visual periphery

The information processing capacity of the visual periphery was investigated in two experiments using stimuli of known relative information content. The results of Experiment I showed redundant patterns to be easiest to identify at all peripheral angles tested (5, 10, 20, and 30 deg) as compared to intermediate and nonredundant patterns. Performance on all patterns decreased as peripheral angle increased. In Experiment II, simultaneous foveal and peripheral vision was required in a discrimination task. Performance was above chance only when the peripheral (7-deg) pattern was redundant. The foveal pattern had no effect on discrimination.