Dense Sequence Research Articles

Total ordering for intuitionistic fuzzy numbers

Intuitionistic fuzzy set plays a vital role in decision making, data analysis, and artificial intelligence. Many decision‐making problems consist of different types of datum, where fuzzy set theoretical approaches may fail to obtain the optimal decision. Numerous approaches for intuitionistic fuzzy decision‐making problem have been introduced in the literature to overcome these short comings. But there is no single approach that can be used to solve all kinds of problems because of the partial ordering defined on the collection of intuitionistic fuzzy numbers (IFNs). Even though ranking of fuzzy numbers have been studied from early seventies in the last century, a total order on the entire class of fuzzy numbers has been introduced by Wang and Wang (Fuzzy Sets Syst 2014, 243, 131–141) only on 2014. A total order on the collection of all IFN is an open problem till today. In this article, a total order on the entire class of IFN using upper lower dense sequence in the interval [0, 1] is proposed and compared with existing techniques using illustrative examples, further an algorithm (which is problem independent) for solving any intuitionistic fuzzy multicriteria decision‐making problem (Intuitionistic fuzzy MCDM) is introduced. This new total ordering on IFNs generalizes the total ordering defined in Wang and Wang () for fuzzy numbers. © 2016 Wiley Periodicals, Inc. Complexity 21: 54–66, 2016

Modulation of the substorm current wedge by bursty bulk flows: 8 September 2002—Revisited

AbstractThe ultimate formation mechanism of the substorm current wedge (SCW) remains to date unclear. In this study, we investigate its relationship to plasma flows at substorm onset and throughout the following expansion phase. We revisit the case of 8 September 2002, which has been defined as “one of the best textbook examples of a substorm” because of its excellent coverage by both spacecraft in the magnetotail and ground‐based observatories. We found that a dense sequence of arrival of nightside flux transfer events (NFTEs; which can be understood as the lobe magnetic signature due to a bursty bulk flow travelling earthward in the central plasma sheet) in the near‐Earth tail leads to a modulation (and further step‐like builtup) of the SCW intensity during the substorm expansion phase. In addition, we found that small SCWs are created also during the growth phase of the event in association with another less intense sequence of NFTEs. The differences between the sequence of NFTEs in the growth and expansion phase are discussed. We conclude that the envelope of the magnetic disturbances which we typically refer to as an intense magnetic substorm is the result of a group or sequence of more intense and more frequent NFTEs.

Multispectral imaging using a single bucket detector.

Existing multispectral imagers mostly use available array sensors to separately measure 2D data slices in a 3D spatial-spectral data cube. Thus they suffer from low photon efficiency, limited spectrum range and high cost. To address these issues, we propose to conduct multispectral imaging using a single bucket detector, to take full advantage of its high sensitivity, wide spectrum range, low cost, small size and light weight. Technically, utilizing the detector’s fast response, a scene’s 3D spatial-spectral information is multiplexed into a dense 1D measurement sequence and then demultiplexed computationally under the single pixel imaging scheme. A proof-of-concept setup is built to capture multispectral data of 64 pixels × 64 pixels × 10 wavelength bands ranging from 450 nm to 650 nm, with the acquisition time being 1 minute. The imaging scheme holds great potentials for various low light and airborne applications, and can be easily manufactured as production-volume portable multispectral imagers.

Total ordering defined on the set of all intuitionistic fuzzy numbers

L.A.Zadeh introduced the concept of fuzzy set theory as the generalization of classical set theory in 1965 and further it has been generalized to intuitionistic fuzzy sets (IFSs) by Atanassov in 1983 to model information by the membership, nonmembership and hesitancy degree more accurately than the theory of fuzzy logic. The notions of intuitionistic fuzzy numbers in different contexts were studied in literature and applied in real life applications. Problems in different fields involving qualitative, quantitative and uncertain information can be modeled better using intutionistic fuzzy numbers introduced in [17] which generalizes intuitionistic fuzzy values [1,7,17], interval valued intuitionistic fuzzy number (IVIFN) [10] than with usual IFNs [5,11,21]. Ranking of fuzzy numbers have started in early seventies in the last century and a complete ranking on the class of fuzzy numbers have achieved by W.Wang and Z.Wang only on 2014. A complete ranking on the class of IVIFNs, using axiomatic set of membership, non membership, vague and precise score functions has been introduced and studied by Geetha et al.[10]. In this paper, a total ordering on the class of IFNs [17] using double upper dense sequence in the interval [0; 1] which generalizes the total ordering on fuzzy numbers (FNs) is proposed and illustrated with examples. Examples are given to show the proposed method on this type of IFN is better than existing methods and this paper will give the better understanding over this new type of IFNs.

Cloud-Based Geospatial 3D Image Spaces—A Powerful Urban Model for the Smart City

In this paper, we introduce the concept and an implementation of geospatial 3D image spaces as new type of native urban models. 3D image spaces are based on collections of georeferenced RGB-D imagery. This imagery is typically acquired using multi-view stereo mobile mapping systems capturing dense sequences of street level imagery. Ideally, image depth information is derived using dense image matching. This delivers a very dense depth representation and ensures the spatial and temporal coherence of radiometric and depth data. This results in a high-definition WYSIWYG (“what you see is what you get”) urban model, which is intuitive to interpret and easy to interact with, and which provides powerful augmentation and 3D measuring capabilities. Furthermore, we present a scalable cloud-based framework for generating 3D image spaces of entire cities or states and a client architecture for their web-based exploitation. The model and the framework strongly support the smart city notion of efficiently connecting the urban environment and its processes with experts and citizens alike. In the paper we particularly investigate quality aspects of the urban model, namely the obtainable georeferencing accuracy and the quality of the depth map extraction. We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps.

A novel multitarget tracking algorithm for Myosin VI protein molecules on actin filaments in TIRFM sequences.

We propose a novel multitarget tracking framework for Myosin VI protein molecules in total internal reflection fluorescence microscopy sequences which integrates an extended Hungarian algorithm with an interacting multiple model filter. The extended Hungarian algorithm, which is a linear assignment problem based method, helps to solve measurement assignment and spot association problems commonly encountered when dealing with multiple targets, although a two-motion model interacting multiple model filter increases the tracking accuracy by modelling the nonlinear dynamics of Myosin VI protein molecules on actin filaments. The evaluation of our tracking framework is conducted on both real and synthetic total internal reflection fluorescence microscopy sequences. The results show that the framework achieves higher tracking accuracies compared to the state-of-the-art tracking methods, especially for sequences with high spot density.

Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction

The application of Bayesian methods to the problem of fatigue crack growth prediction has been growing in recent years. In particular, sequential Monte-Carlo sampling is often presented as an efficient model-based technique to filter the sequential measures of the damage evolution provided as an input to the algorithm. However, a lot of measures are required to reliably identify the system state condition and the underlying model parameters. Many studies rely on the availability of a relatively dense sequence of crack length measures during damage evolution, made in most cases impractical by the consequent maintenance costs. Thus, real-time damage diagnosis is a requirement to enable prognostic health management.This work focuses on the application of sequential Monte-Carlo sampling to estimate the probabilistic residual life of a structural component subjected to fatigue crack propagation, while real-time estimation of crack length is provided through a committee of artificial neural networks, trained with finite element simulated strain patterns. Multiple crack length observations are available at each discrete time and are provided as the input to the prognostic system, based on a sequential importance resampling algorithm. Each time a new set of measures is available, the algorithm evaluates the posterior distribution of the augmented state vector, including the crack length and a material parameter governing damage evolution. This filtered information is used to numerically update the probability density functions of the residual life of the component. The methodology is applied first to a simulated crack and then to a metallic stiffened panel specimen subject to fatigue crack growth.

Analytically defined uniformly dense sequences

We introduce a notion of uniform density. This notion is an analogue of uniform distribution modulo 1, but is of interest mostly for non-compact spaces. Our main results show that various specific (families of) sequences or real numbers, defined by means of analytic formulas, are uniformly dense.

Detecting human falls with 3-axis accelerometer and depth sensor.

Previous work demonstrated that Kinect sensor can be very useful for fall detection. In this work we present a novel approach to fall detection that allows us to achieve reliable fall detection in larger areas through person detection and tracking in dense depth map sequences acquired by an active pan-tilt 3D camera. We demonstrate that both high sensitivity and specificity can be obtained using dense depth images acquired by a ceiling mounted Kinect and executing the proposed algorithms for lying pose detection and motion analysis. The person is extracted using depth region growing and person detection.

Efficient haplotype block recognition of very long and dense genetic sequences

BackgroundThe new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least Θ(n2) time and memory complexity.ResultsWe derived three incremental optimizations of the widely used haplotype block recognition algorithm proposed by Gabriel et al. in 2002. Our most efficient solution, called MIG ++, has only Θ(n) memory complexity and, on a genome-wide scale, it omits >80% of the calculations, which makes it an order of magnitude faster than the original algorithm. Differently from the existing software, the MIG ++ analyzes the LD between SNPs at any distance, avoiding restrictions on the maximal block length. The haplotype block partition of the entire HapMap II CEPH dataset was obtained in 457 hours. By replacing the standard likelihood-based D′ variance estimator with an approximated estimator, the runtime was further improved. While producing a coarser partition, the approximate method allowed to obtain the full-genome haplotype block partition of the entire 1000 Genomes Project CEPH dataset in 44 hours, with no restrictions on allele frequency or long-range correlations. These experiments showed that LD-based haplotype blocks can span more than one million base-pairs in both HapMap II and 1000 Genomes datasets. An application to the North American Rheumatoid Arthritis Consortium (NARAC) dataset shows how the MIG ++ can support genome-wide haplotype association studies.ConclusionsThe MIG ++ enables to perform LD-based haplotype block recognition on genetic sequences of any length and density. In the new generation sequencing era, this can help identify haplotypes that carry rare variants of interest. The low computational requirements open the possibility to include the haplotype block structure into genome-wide association scans, downstream analyses, and visual interfaces for online genome browsers.

Efficient Method for Mining Patterns from Highly Similar and Dense Database based on Prefix-Frequent-Items

In recent years, there are a great deal of efforts on sequential pattern mining, but some challenges have not been resolved, such as large search spaces and the ineffectiveness in handling highly similar, dense and long sequences. This paper mainly focuses on how to design some effective search space pruning methods to accelerate the mining process. We present a novel structure, Prefix-Frequent-Items Graph (PFI-Graph), which presents the prefix frequent items of other items in sequential patterns. An efficient algorithm PFI-PrefixSpan (Prefix-Frequent- Items PrefixSpan) based on PFI-Graph is proposed in this paper. It avoids redundant data scanning, and thus can effectively speed up the discovery process of new patterns. Extensive experimental results on some synthetic and real sequence datasets show that the proposed novel structure is substantially more efficient than PrefixSpan with physical-projection and pseudo-projection, especially for dense and highly similar sequence databases.

Unconditionality of orthogonal spline systems in Lp

Given any natural number $k$ and any dense point sequence $(t_n)$, we prove that the corresponding orthonormal spline system of order $k$ is an unconditional basis in reflexive $L^p$.

Sequence-based prediction of linear autoepitopes involved in pathogenesis of IPAH and the corresponding organism sources of molecular mimicry.

We proposed here a sequence-based approach predicting some microorganisms as possible sources of autoantigen-related molecular mimicry concerning Idiopathic Pulmonary Arterial Hypertension (IPAH) and related hypertension mostly accompanying autoimmune diseases and AIDS (APAH). This approach (SPECIES_VALENCE) processes the database occurrences of linear autoepitope-related short Dense Quasi-Pattern Sequences (DQPA) generated based on identities of important autoantigenic sequences. The corresponding enumeration comprises two types of statistical evaluations performed in each of eight proposed models. Based on this enumeration, we selected nine microorganisms, whereas revaluation of the obtained scoring values restricted Pseudomonas aeruginosa, Aspergillus fumigatus and the two co-infecting herpes viruses (Epstein Barr virus and cytomegalovirus) as most favourable. The results are discussed in terms of (a) the validity of increased DQPA occurrence in functionally correlated sequences, (b) the possible mechanisms leading to autoantibody response, (c) selected additional pathogenic effects of predicted microorganisms and (d) possible effects of cross-reactivities and immune tolerance.

Total orderings defined on the set of all fuzzy numbers

In this work, a new concept of upper dense sequence in interval (0,1] is introduced. There are infinitely many upper dense sequences in interval (0,1]. Using any upper dense sequence, a new decomposition theorem for fuzzy sets is established and proved. Then, using a chosen upper dense sequence as one of the necessary reference systems, infinitely many total orderings on the set of all fuzzy numbers can be well defined. Among them, a common upper dense sequence based on the binary numbers is suggested as a natural default option. Another upper dense sequence based on the rational numbers is also suggested. Regarding real numbers as special fuzzy numbers, all of these total orderings defined by using the suggested upper dense sequences are consistent with the natural ordering of real numbers. Building total ordering on the set of all fuzzy numbers in such a way is significant for fuzzy data analysis and, therefore, may be used in decision making with fuzzy information.

Quantitative investigation of cardiac motion effects on in vivo diffusion tensor parameters: a simulation study

Background Cardiac motion is a crucial problem in in vivo diffusion tensor imaging (DTI) of the human heart. Despite its importance, the effects of cardiac motion on diffusion tensor parameters of the human heart in vivo have not been well established, mainly because of large signal loss. Recently, an efficient method was proposed that acquires cardiac diffusion weighted (DW) images at different time points of the cardiac cycle and reduces motion-induced signal loss using PCA filtering and temporal MIP techniques (PCATMIP) (Rapacchi, Invest Radiol 2011). Meanwhile, polarized light imaging (PLI) provides us the ground-truth of the heart fiber architecture, and DENSE sequence offers us higher spatial resolution displacement fields of the human heart in vivo. These different imaging possibilities have led us to investigate a multimodal approach to quantitatively investigate the effects of cardiac motion on diffusion tensor parameters such as

Projection Position-Based Sequential Pattern Mining Algorithm

PrefixSpan algorithm will construct huge amount of projected databases in the process of mining sequence patterns, especially mining dense dataset and long sequence pattern, which will cause decline of the performance of the algorithm. The resource problem can be solved by Projection position-based Sequential Pattern Mining Algorithm so as to reduce time and storage space. In order to avoid producing huge amount of projected databases and reduce unnecessary storage space and scanning time, compared with the others improved algorithm, the PSPM utilizes projected position to locate projected sequence position for mining local frequency items and deletes the non-frequent items. Experiment results demonstrate that PSPM outperforms the PrefixSpan(with pseudo-pro) algorithm in the aspect of Runtime performance.

DISCOVERY AND EARLY MULTI-WAVELENGTH MEASUREMENTS OF THE ENERGETIC TYPE IC SUPERNOVA PTF12GZK: A MASSIVE-STAR EXPLOSION IN A DWARF HOST GALAXY

We present the discovery and extensive early-time observations of the Type Ic supernova (SN) PTF12gzk. Our finely sampled light curves show a rise of 0.8mag within 2.5hr. Power-law fits [f(t)\sim(t-t_0)^n] to these data constrain the explosion date to within one day. We cannot rule out the expected quadratic fireball model, but higher values of n are possible as well for larger areas in the fit parameter space. Our bolometric light curve and a dense spectral sequence are used to estimate the physical parameters of the exploding star and of the explosion. We show that the photometric evolution of PTF12gzk is slower than that of most SNe Ic, and its high ejecta velocities (~30,000km/s four days after explosion) are closer to the observed velocities of broad-lined SNe Ic associated with gamma-ray bursts (GRBs) than to the observed velocities in normal Type Ic SNe. The high velocities are sustained through the SN early evolution, and are similar to those of GRB-SNe when the SN reach peak magnitude. By comparison with the spectroscopically similar SN 2004aw, we suggest that the observed properties of PTF12gzk indicate an initial progenitor mass of 25-35 solar mass and a large (5-10E51 erg) kinetic energy, close to the regime of GRB-SN properties. The host-galaxy characteristics are consistent with GRB-SN hosts, and not with normal SN Ic hosts as well, yet this SN does not show the broad lines over extended periods of time that are typical of broad-line Type Ic SNe.

Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows

Whole-genome and exome sequencing have already proven to be essential and powerful methods to identify genes responsible for simple Mendelian inherited disorders. These methods can be applied to complex disorders as well, and have been adopted as one of the current mainstream approaches in population genetics. These achievements have been made possible by next generation sequencing (NGS) technologies, which require substantial bioinformatics resources to analyze the dense and complex sequence data. The huge analytical burden of data from genome sequencing might be seen as a bottleneck slowing the publication of NGS papers at this time, especially in psychiatric genetics. We review the existing methods for processing NGS data, to place into context the rationale for the design of a computational resource. We describe our method, the Graphical Pipeline for Computational Genomics (GPCG), to perform the computational steps required to analyze NGS data. The GPCG implements flexible workflows for basic sequence alignment, sequence data quality control, single nucleotide polymorphism analysis, copy number variant identification, annotation, and visualization of results. These workflows cover all the analytical steps required for NGS data, from processing the raw reads to variant calling and annotation. The current version of the pipeline is freely available at http://pipeline.loni.ucla.edu. These applications of NGS analysis may gain clinical utility in the near future (e.g., identifying miRNA signatures in diseases) when the bioinformatics approach is made feasible. Taken together, the annotation tools and strategies that have been developed to retrieve information and test hypotheses about the functional role of variants present in the human genome will help to pinpoint the genetic risk factors for psychiatric disorders.

Genome‐wide patterns of divergence and gene flow across a butterfly radiation

The Heliconius butterflies are a diverse recent radiation comprising multiple levels of divergence with ongoing gene flow between species. The recently sequenced genome of Heliconius melpomene allowed us to investigate the genomic evolution of this group using dense RAD marker sequencing. Phylogenetic analysis of 54 individuals robustly supported reciprocal monophyly of H. melpomene and Heliconius cydno and refuted previous phylogenetic hypotheses that H. melpomene may be paraphylectic with respect to H. cydno. Heliconius timareta also formed a monophyletic clade closely related but distinct from H. cydno with Heliconius heurippa falling within this clade. We find evidence for genetic admixture between sympatric populations of the sister clades H. melpomene and H. cydno/timareta, particularly between H. cydno and H. melpomene from Central America and between H. timareta and H. melpomene from the eastern slopes of the Andes. Between races, divergence is primarily explained by isolation by distance and there is no detectable genetic population structure between parapatric races, suggesting that hybrid zones between races are not zones of secondary contact. Our results also support previous findings that colour pattern loci are shared between populations and species with similar colour pattern elements. Furthermore, this pattern is almost unique to these genomic regions, with only a very small number of other loci showing significant similarity between populations and species with similar colour patterns.

An empirical comparison of real-time dense stereo approaches for use in the automotive environment

In this work we evaluate the use of several real-time dense stereo algorithms as a passive 3D sensing technology for potential use as part of a driver assistance system or autonomous vehicle guidance. A key limitation in prior work in this area is that although significant comparative work has been done on dense stereo algorithms using de facto laboratory test sets only limited work has been done on evaluation in real world environments such as that found in potential automotive usage. This comparative study aims to provide an empirical comparison using automotive environment video imagery and compare this against dense stereo results drawn on standard test sequences in addition to considering the computational requirement against performance in real-time. We evaluate five chosen algorithms: Block Matching, Semi-Global Matching, No-Maximal Disparity, Cross-Based Local Approach, Adaptive Aggregation with Dynamic Programming. Our comparison shows a contrast between the results obtained on standard test sequences and those for automotive application imagery where a Semi-Global Matching approach gave the best empirical performance. From our study we can conclude that the noise present in automotive applications, can impact the quality of the depth information output from more complex algorithms (No-Maximal Disparity, Cross-Based Local Approach, Adaptive Aggregation with Dynamic Programming) resulting that in practice the disparity maps produced are comparable with those of simpler approaches such as Block Matching and Semi-Global Matching which empirically perform better in the automotive environment test sequences. This empirical result on automotive environment data contradicts the comparative result found on standard dense stereo test sequences using a statistical comparison methodology leading to interesting observations regarding current relative evaulation approaches.