Noisy Patterns Research Articles

On-line Learning With Reject Option

On-line learning is a training paradigm that allows the processing of constant data flows, so that learning adapts to new knowledge. However, due to the nature of the study problem, it is possible that in the clustering obtained there are data complexities (outliers, atypical patterns, noisy, etc.) that deteriorate the performance of the model in the classification stage. Due to the above, an alternative to cope data complexities is the use of algorithms that allow to detect reject options to filter noisy pattern. In this research the neighborhood-based reject option is implemented in an on-line learning process, with the intention of improving the clustering quality and thus increasing the precision indexes obtained with the nearest neighbor's rule in the classification stage. Likewise, to validate the quality of the clustering generated, internal and external analysis metrics are used. The experimental results show the viability of the proposal when analyzed on real data.

Discovering co-location patterns with aggregated spatial transactions and dependency rules

Co-location pattern mining focuses on finding associations among spatial features. Existing co-location pattern mining techniques mainly rely on frequency based thresholds which discard the rare patterns and find the noisy patterns. This could be avoided by evaluating co-location patterns based on their statistical significance. Recent studies focused on association rule mining have successfully adopted statistical tests to find significant rules. By transforming spatial data to transaction data, the co-location pattern mining problem can be reduced to an association rule mining problem and such methods can be used to find co-location patterns robustly. A transactionization mechanism has been recently proposed to achieve this. However, this method ignores the effect of general instances, with non-overlapping buffer regions, on the reference instances in their proximity. Addressing this, we propose a novel approach, AGT-Fisher, to robustly transform spatial data to transaction data and use statistically significant dependency rule searching methods to find co-location rules from them. Our work is motivated by an application in environmental health to investigate potential associations between air pollution and adverse birth outcomes in Canada. We used AGT-Fisher to find such associations from real datasets. The discovered co-location patterns were evaluated based on their statistical dependency and the empirical evidence, and results showed that our approach is more robust. Furthermore, we evaluated the resulting patterns to find spatial common and contrast sets, which are two special types of co-location patterns, to compare spatial regions and gain more insights.

A peak-finding algorithm based on robust statistical analysis in serial crystallography

The recent development of serial crystallography at synchrotron and X-ray free-electron laser (XFEL) sources is producing crystallographic datasets of ever increasing volume. The size of these datasets is such that fast and efficient analysis presents a range of challenges that have to be overcome to enable real-time data analysis, which is essential for the effective management of XFEL experiments. Among the blocks which constitute the analysis pipeline, one major bottleneck is `peak finding', whose goal is to identify the Bragg peaks within (often) noisy diffraction patterns. Development of faster and more reliable peak-finding algorithms will allow for efficient processing and storage of the incoming data, as well as the optimal use of diffraction data for structure determination. This paper addresses the problem of peak finding and, by extension, `hit finding' in crystallographic XFEL datasets, by exploiting recent developments in robust statistical analysis. The approach described here involves two basic steps: (1) the identification of pixels which contain potential peaks and (2) modeling of the local background in the vicinity of these potential peaks. The presented framework can be generalized to include both complex background models and alternative models for the Bragg peaks.

Patterns and coherence resonance in the stochastic Swift–Hohenberg equation with Pyragas control: The Turing bifurcation case

We provide a multiple time scales analysis for the Swift–Hohenberg equation with delayed feedback via Pyragas control, with and without additive noise. An analysis of the pattern formation near onset indicates both the possibility of either standing waves (rolls) or traveling waves via Turing or Turing–Hopf bifurcations, respectively, depending on the product of the strength of the feedback and the length of the delay. The remainder of the paper is focused on Turing bifurcations, where the delay can drive the appearance of an additional time scale, intermediate to the usual slow and fast time scales observed in the modulation of rolls without delay. In the deterministic case, a Ginzburg–Landau-type modulation equation is derived that inherits Pyragas control terms from the original equation. The Eckhaus stability criteria is obtained for the rolls, with the intermediate time scale observed in the transients. In the stochastic context, slow modulation equations are derived for the amplitudes of the primary modes that are coupled to a fast Ornstein–Uhlenbeck-type equation with delay for the zero mode driven by the additive noise. By deriving an averaging approximation for the amplitude of the primary mode, we show how the interaction of noise and delay influences the existence and stability range for the noisy roll-type patterns. Furthermore, approximations for the spectral densities of the primary and zero modes show that oscillations on the intermediate times scale are sustained through the phenomenon of coherence resonance. These dynamics on the intermediate time scale are sustained through the interaction of noise and delay, in contrast to the deterministic context where dynamics on the intermediate times scale are transient.

Implicit sequence learning despite multitasking: the role of across-task predictability.

One often replicated finding is that implicit sequence learning is hampered in dual-task situations. Thus, one crucial question has been whether implicit learning processes require attentional resources. Meanwhile, focusing exclusively on limited attentional resources might be considered as too unspecific. Overall, the focus lies now rather on the possibility that the impairment is due to interference coming along with (a) task integration (e.g., Schmidtke and Heuer in Psychol Res 60(1-2):53-71, 1997)-or with (b) parallel response selection (Schumacher and Schwarb in J Exp Psychol Gen 138(2):270-290, 2009). Yet, other explanations have also been put forward-and there is still no agreement. Our goal here is to contribute to this debate by testing several constraints that have been suggested in the literature within one single paradigm, originating by Schumacher and Schwarb (J Exp Psychol Gen 138(2):270-290, 2009). Therefore, we paired the same visual-manual serial reaction time task (SRTT; Nissen and Bullemer in Cogn Psychol 19(1):1-32, 1987) with different auditory-vocal tone-discrimination tasks across seven dual-task conditions. We manipulated (a) its relation to the SRTT and/or (b) the difficulty of response selection. The results suggest that task integration is indeed a crucial factor for implicit sequence learning: since the tone-task is a potential source of noisy patterns of covariation in a complex arrangement of task components, sequence learning is disrupted. In line with Rah, Reber, and Hsiao (Psychon Bull Rev 7(2):309-313, 2000), the usefulness (in terms of sequence learning) of task integration seems to depend on the predictive value of across-task stimulus and/or response events.

Simplified paraboloid phase model-based phase tracker for demodulation of a single complex fringe.

A regularized phase tracker (RPT) is an effective method for demodulation of single closed-fringe patterns. However, lengthy calculation time, specially designed scanning strategy, and sign-ambiguity problems caused by noise and saddle points reduce its effectiveness, especially for demodulating large and complex fringe patterns. In this paper, a simplified paraboloid phase model-based regularized phase tracker (SPRPT) is proposed. In SPRPT, first and second phase derivatives are pre-determined by the density-direction-combined method and discrete higher-order demodulation algorithm, respectively. Hence, cost function is effectively simplified to reduce the computation time significantly. Moreover, pre-determined phase derivatives improve the robustness of the demodulation of closed, complex fringe patterns. Thus, no specifically designed scanning strategy is needed; nevertheless, it is robust against the sign-ambiguity problem. The paraboloid phase model also assures better accuracy and robustness against noise. Both the simulated and experimental fringe patterns (obtained using electronic speckle pattern interferometry) are used to validate the proposed method, and a comparison of the proposed method with existing RPT methods is carried out. The simulation results show that the proposed method has achieved the highest accuracy with less computational time. The experimental result proves the robustness and the accuracy of the proposed method for demodulation of noisy fringe patterns and its feasibility for static and dynamic applications.

The maximum likelihood based intensity estimate for circular point processes

Identifying the geometrical nature of spatial point patterns plays an important role in many areas of scientific research. Common types of spatial point processes involve random, regular, and cluster patterns. However, some point patterns suggest identifiable geometrical shapes such as a circular or other conic patterns. These patterns may be recognized as either a specific clustered shape or an inhomogeneous point pattern. Less noisy conic shapes, including circular patterns, are heavily discussed in the pattern recognition literature, but the goodness-of-fit of conic-fitting algorithms is rarely discussed for very noisy data. This study addresses a parameter estimation technique for noisy circular point patterns using the maximum likelihood principle. Additionally, a spatial statistical tool known as the L-function is used to investigate whether the fitted location pattern is reasonably attributable to a circular shape. A novel quantity named ‘relative log-error’ ( $$\gamma $$ ) is introduced to quantify the goodness-of-fit for circular model fits. An iteratively re-weighted least squares procedure is introduced and robustness is evaluated under several error structures. Computational efficiency of the current and novel circle-fitting methods is also discussed. The findings are applied to two environmental science data sets.

BioEssays 7∕2017

BioEssaysVolume 39, Issue 7 1770075 Back CoverFree Access BioEssays 7∕2017 First published: 26 June 2017 https://doi.org/10.1002/bies.201770075AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Graphical Abstract Interindividual epigenetic variability: Sound or noise? This cover image tries to reflect the potential consequences of epigenetic “sound” (purposeful epigenetic changes) and epigenetic “noise” (stochastic epigenetic changes) in the establishment of interindividual epigenetic variability. The different DNA molecules make allusion to different individuals, where musical notes represent methylathed (minim) and unmethylated (crotchet) CpGs, introducing the concept of phenotypic variation across the population of interest. To highlight the idea of “purposeful” and “stochastic” epigenetic variation, DNA molecules are split into a clean, easy to interpret signal (left side), or a blurry, noisy pattern (right side) respectively, reflecting some of the current biological scenarios that are discussed in article 1700055 by Juan Ramón Tejedor and Mario F. Fraga. Volume39, Issue7July 20171770075 RelatedInformation

Noise detection on ECG based on agglomerative clustering of morphological features

Biosignals are usually contaminated with artifacts from limb movements, muscular contraction or electrical interference. Many algorithms of the literature, such as threshold methods and adaptive filters, focus on detecting these noisy patterns. This study introduces a novel method for noise and artifact detection in electrocardiogram based on time series clustering.The algorithm starts with the extraction of features that best characterize the shape and behaviour of the signal over time and groups its samples in separated clusters by means of an agglomerative clustering approach. The method has been tested in numerous datasets to reveal that it is independent on specific records and globally, the algorithm was able to successfully detect noisy patterns and artifacts with a sensitivity of 88%, a specificity of 92% and an accuracy of 91%, demonstrating a good performance in pattern detection based on morphological clustering.This algorithm can be applied to the detection and sectioning of multiple types of noise for more accurate denoising and adapted for signal classification.

When do correlations increase with firing rates in recurrent networks?

A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix.

Noisy-free Length Discriminant Analysis with cosine hyperbolic framework for dimensionality reduction

Dimensionality Reduction (DR) is very useful and popular in many application areas of expert and intelligent systems, such as machine learning, finance, data and text mining, multimedia mining, image processing, anomaly detection, defense applications, bioinformatics and natural language processing. DR is widely applied for better data visualization and improving learning in all the above fields. In this manuscript, we propose a novel DR approach namely, Noisy-free Length Discriminant Analysis (NLDA) by developing Noisy-free Relevant Pattern Selection (NRPS). Traditional pattern selection methods discriminate boundary and non-boundary patterns with the help of class information and nearest neighbors. And these methods completely ignore noisy patterns which may degrade the performance of subsequent subspace learning. To overcome this, we develop Noisy-free Relevant Pattern Selection (NRPS), in which data instances are partitioned into boundary, non-boundary and noisy patterns. With the help of noisy-free boundary and non-boundary patterns, Noisy-free Length Discriminant Analysis (NLDA) has been proposed by developing new within and between-class scatters. These scatters model discriminations between lengths (L2-norms) of different class instances by considering only boundary and non-boundary patterns, while ignoring noisy patterns. A cosine hyperbolic frame work has been developed to formulate the objective of NLDA. Moreover, NLDA can also model the discrimination of multimodal data as different class data may consist of different lengths. Experimental study conducted on the synthesized data, UCI, and leeds butterfly databases. Moreover, an experimental study over human and computer interaction, i.e., face recognition (one of the application areas of expert and intelligent systems), has been performed. And, these studies prove that the proposed method can produce better discriminated subspace compare to the state-of-the-art methods.

Analysis of chaotic and noise processes in a fluctuating blood flow using the Allan variance technique

The aim of this work was to develop a novel technique for digital processing of Doppler ultrasound blood flow sensor data from noisy blood flow velocity waveforms. To evaluate the fluctuating blood flow parameters, various nonlinear dynamics methods and algorithms are often being used. Here, for identification of chaotic and noise components in a fluctuating coronary blood flow, for the first time the Allan variance technique was used. Analysis of different types of noises (White, Brownian, Flicker) was carried out and their strong correlation with fractality of time series (the Hurst exponent) was revealed. Based on a specialized software realizing the developed technique, numerical experiments with real clinical data were carried out. Recommendations for identification of noisy patterns of coronary blood flow in normal and pathological states were developed. The methodology gives us the possibility for the more detailed quantitative and qualitative analysis of a noisy fluctuating blood flow data.

Group sparse autoencoder

Unsupervised feature extraction is gaining a lot of research attention following its success to represent any kind of noisy data. Owing to the presence of a lot of training parameters, these feature learning models are prone to overfitting. Different regularization methods have been explored in the literature to avoid overfitting in deep learning models. In this research, we consider autoencoder as the feature learning architecture and propose ℓ2,1-norm based regularization to improve its learning capacity, called as Group Sparse AutoEncoder (GSAE). ℓ2,1-norm is based on the postulate that the features from the same class will have a common sparsity pattern in the feature space. We present the learning algorithm for group sparse encoding using majorization–minimization approach. The performance of the proposed algorithm is also studied on three baseline image datasets: MNIST, CIFAR-10, and SVHN. Further, using GSAE, we propose a novel deep learning based image representation for minutia detection from latent fingerprints. Latent fingerprints contain only a partial finger region, very noisy ridge patterns, and depending on the surface it is deposited, contain significant background noise. We formulate the problem of minutia extraction as a two-class classification problem and learn the descriptor using the novel formulation of GSAE. Experimental results on two publicly available latent fingerprint datasets show that the proposed algorithm yields state-of-the-art results for automated minutia extraction.

A study of noisy signal pattern recognition using method of emphasizing wavelet coefficients for reference clean signal data

This paper improves a pattern recognition method for the signals under the noisy environment using method of emphasizing wavelet coefficients for reference clean signal data. If the data is polluted with noise, these signal pattern recognitions are extremely difficult problem. To analyze noise prob lem, people in general have used the fourier analysis. But the fourier analysis reveals only the frequency information. And the general noise filters reduce specific frequency band contained both noise and signal. It is difficult to eliminate only noise component from a signal containing noise signal. To overcome this difficulty, we applied the wavelet analysis. In this paper, we improve the noisy signal pattern recognition by bringing it close to reference data and noisy input data by modifying the spectrum by using the wavelet transform for reference clean signal data. As a result, noisy signal pattern recognition rate is improved by this method.

Adaptive edited natural neighbor algorithm

Reduction techniques can reduce prohibitive computational costs and the storage requirements for classifying patterns while maintaining classification accuracy. The edited nearest neighbor rule is one of the most popular reduction technique, which removes noisy patterns that are not correctly classified by their k-nearest neighbors. However, selection of neighborhood parameters is an unsolved problem for the traditional neighborhood construction algorithms such as k-nearest neighbor and ε-neighborhood. To solve the problem, we present a novel editing algorithm called adaptive Edited Natural Neighbor algorithm (ENaN). ENaN aims to eliminate the noisy patterns based on the concept of natural neighbor which are obtained adaptively by the search algorithm of natural neighbor. The main advantages are that ENaN does not need any parameters and can degrade the effect of noisy patterns. The adaptive ENaN algorithm can be easily applied into other reduction algorithms as a noisy filter. Experiments show that the proposed approach effectively removes the noisy patterns while keeping more reasonable class boundaries and improves the performance of two condensation methods in terms of both accuracy and reduction rate greatly.

Natural Neighbor Reduction Algorithm for Instance-based Learning

Instance reduction is aimed at reducing prohibitive computational costs and the storage space for instance-based learning. The most frequently used methods include the condensation and edition approaches. Condensation method removes the patterns far from the decision boundary and do not contribute to better classification accuracy, while edition method removes noisy patterns to improve the classification accuracy. In this paper, a new hybrid algorithm called instance reduction algorithm based on natural neighbor and nearest enemy is presented. At first, an edition algorithm is proposed to filter noisy patterns and smooth the class boundaries by using natural neighbor. The main advantage of the algorithm is that it does not require any user-defined parameters. Then, using a new condensation method based on nearest enemy to reduce instances far from decision line. Through this algorithm, interior instances are discarded. Experiments show that the hybrid approach effectively reduces the number of instances while achieves higher classification accuracy along with competitive algorithms.

A wavelet- based transform method for quality improvement in noisy speech patterns of Arabic language

This paper addresses the problem of single-channel speech enhancement of low (negative) SNR of Arabic noisy speech signals. For this aim, a binary mask thresholding function based coiflet5 mother wavelet transform is proposed for Arabic speech enhancement. The effectiveness of binary mask thresholding function based coiflet5 mother wavelet transform is compared with Wiener method, spectral subtraction, log-MMSE, test-PSC and p-mmse in presence of babble, pink, white, f-16 and Volvo car interior noise. The noisy input speech signals are processed at various levels of input SNR range from ź5 to ź25 dB. Performance of the proposed method is evaluated with the help of PESQ, SNR and cepstral distance measure. The results obtained by proposed binary mask thresholding function based coiflet5 wavelet transform method are very encouraging and shows that the proposed method is much helpful in Arabic speech enhancement than other existing methods.

Dragonfly: an implementation of the expand-maximize-compress algorithm for single-particle imaging.

Single-particle imaging (SPI) with X-ray free-electron lasers has the potential to change fundamentally how biomacromolecules are imaged. The structure would be derived from millions of diffraction patterns, each from a different copy of the macromolecule before it is torn apart by radiation damage. The challenges posed by the resultant data stream are staggering: millions of incomplete, noisy and un-oriented patterns have to be computationally assembled into a three-dimensional intensity map and then phase reconstructed. In this paper, the Dragonfly software package is described, based on a parallel implementation of the expand-maximize-compress reconstruction algorithm that is well suited for this task. Auxiliary modules to simulate SPI data streams are also included to assess the feasibility of proposed SPI experiments at the Linac Coherent Light Source, Stanford, California, USA.

SENSITIVITY KERNELS FOR FLOWS IN TIME–DISTANCE HELIOSEISMOLOGY: EXTENSION TO SPHERICAL GEOMETRY

ABSTRACT We extend an existing Born approximation method for calculating the linear sensitivity of helioseismic travel times to flows from Cartesian to spherical geometry. This development is necessary for using the Born approximation for inferring large-scale flows in the deep solar interior. As first sanity check, we compare two f-mode kernels from our spherical method and from an existing Cartesian method. The horizontal and total integrals agree to within 0.3%. As a second consistency test, we consider a uniformly rotating Sun and a travel distance of 42°. The analytical travel-time difference agrees with the forward-modeled travel-time difference to within 2%. In addition, we evaluate the impact of different choices of filter functions on the kernels for a meridional travel distance of 42°. For all filters, the sensitivity is found to be distributed over a large fraction of the convection zone. We show that the kernels depend on the filter function employed in the data analysis process. If modes of higher harmonic degree (90 ≲ l ≲ 170) are permitted, a noisy pattern of a spatial scale corresponding to l ≈ 260 appears near the surface. When mainly low-degree modes are used (l ≲ 70), the sensitivity is concentrated in the deepest regions and it visually resembles a ray-path-like structure. Among the different low-degree filters used, we find the kernel for phase-speed-filtered measurements to be best localized in depth.

Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions

A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using SPICE simulations with a Verilog-A device model based on the measured characteristics of the CBRAM device. The applicability of the neuron is demonstrated by constructing a neural network system and applying it to pattern reconstructions that can recall the original patterns from noisy patterns. With these CBRAM-based neurons, a reduction in the area and power of neuromorphic chips is expected in comparison with CMOS-only neuron implementations.