Mutual Similarity Research Articles

Image Reconstruction Enhancement Using Convolution Back Projection Method for Electrical Tomography Application

This paper presents an image enhancement technique using convolution back-projection (CBP) technique to overcome the blurry issue caused by linear back-projection (LBP) technique on tomographic images. The CBP technique is discussed and employed on four different types of stratified flow test profiles. The obtained image results are compared between LBP and CBP and further assessed using mutual structural similarity index (MSSIM). The enhancement technique has successfully scored MSSIM rating above 0.9 for all four test profiles whereby the rating is ranging from 0 (poor quality) to 1 (best quality).

Assessing the impact of choosing different deformable registration algorithms on cone-beam CT enhancement by histogram matching

BackgroundThe aim of this work is to assess the impact of using different deformable registration (DR) algorithms on the quality of cone-beam CT (CBCT) correction with histogram matching (HM).Methods and materialsData sets containing planning CT (pCT) and CBCT images for ten patients with prostate cancer were used. Each pCT image was registered to its corresponding CBCT image using one rigid registration algorithm with mutual information similarity metric (RR-MI) and three DR algorithms with normalized correlation coefficient, mutual information and normalized mutual information (DR-NCC, DR-MI and DR-NMI, respectively). Then, the HM was performed between deformed pCT and CBCT in order to correct the distribution of the Hounsfield Units (HU) in CBCT images.ResultsThe visual assessment showed that the absolute difference between corrected CBCT and deformed pCT was reduced after correction with HM except for soft tissue-air and soft-tissue-bone interfaces due to the improper registration. Furthermore, volumes comparison in terms of average HU error showed that using DR-NCC algorithm with HM yielded the lowest error values of about 55.95 ± 10.43 HU compared to DR-MI and DR-NMI for which the errors were 58.60 ± 10.35 and 56.58 ± 10.51 HU, respectively. Tissue class’s comparison by the mean absolute error (MAE) plots confirmed the performance of DR-NCC algorithm to produce corrected CBCT images with lowest values of MAE even in regions where the misalignment is more pronounced. It was also found that the used method had successfully improved the spatial uniformity in the CBCT images by reducing the root mean squared difference (RMSD) between the pCT and CBCT in fat and muscle from 57 and 25 HU to 8HU, respectively.ConclusionThe choice of an accurate DR algorithm before performing the HM leads to an accurate correction of CBCT images. The results suggest that applying DR process based on NCC similarity metric reduces significantly the uncertainties in CBCT images and generates images in good agreement with pCT.

CLUSTER ANALYSIS OF MEDICAL TEXT DOCUMENTS BY USING SEMI-CLUSTERING APPROACH BASED ON GRAPH REPRESENTATION

The development of Internet resulted in an increasing number of online text re-positories. In many cases, documents are assigned to more than one class and automatic multi-label classification needs to be used. When the number of labels exceeds the number of the documents, effective label space dimension reduction may signifi-cantly improve classification accuracy, what is a major priority in the medical field. In the paper, we propose document clustering for label selection. We use semi-clustering method, by considering graph representation, where documents are represented by vertices and edge weights are calculated according to their mutual similarity. Assigning documents to semi-clusters helps in reducing number of labels, further used in multilabel classification process. The performance of the method is examined by experiments conducted on real medical datasets.

Dobra śmierć – czyli jaka? Próba odpowiedzi na pytanie na podstawie nauczania K. Rahnera, H. U. von Balthasara i J. Ratzingera (Benedykta XVI)

On July 22, 1908, Pope Pius X established the Association of Our Lady of a Happy Death as universal for the whole Church. On May 1987, the Primate of Poland, Cardinal Joseph Glemp, approved the Polish Branch of the Association at the Shrine to the Virgin Mary in Górka Klasztorna. He also permitted the Missionaries of the Holy Family to carry out this work. The purpose of the Association is the propagation of prayer and preparation for a good death. The question is, however, can death be good? If so, what is a good death? The article titled „What makes a good death? An attempt to answer the question based on the teachings of K. Rahner, H.U. von Balthasar and J. Ratzinger – Benedict XVI“ is an analysis of the thoughts of the three great contemporary theologians. It does not, however, aim to analyze deeply the mutual similarities and differences between the German-speaking theologians. The study aims at explaining what we really ask God for when praying for a „happy death”. Even a cursory reading of selected books by Rahner, von Balthasar and Ratzinger – Benedict XVI reveals that thinking about death as something good is not unreasonable. Why? Because the positive aspect of dying does not come from the nature of death, but from God’s action in it. It was Him who in the Person of Jesus Christ became man and conquered the hell of death (Ratzinger) by transforming a “bad death” into a “good one” (von Balthasar), and thus gained eternal redemption for us (Rahner).

Identification of Indian monsoon predictors using climate network and density-based spatial clustering

The Indian summer monsoon is a complex climatic phenomenon with a large variability over the years. The climatic predictors affecting the phenomenon evolve with time, and consequently new predictors have gained importance. Several statistical approaches are being explored in the literature to identify the potential predictors influencing the Indian summer monsoon. A complex network paradigm involving climatic variables at the grids over the globe has been proposed for predictor identification and monsoon prediction. The approach initiates with the identification of communities in the climate network considering mutual similarity and the influence of climate variables of grids on the Indian summer monsoon. Spatial clustering is performed over the communities to identify the geographical regions of significance. The climatic predictors extracted from variables of these regions are evaluated in terms of their correlation with the monsoon as well as their forecasting skills in predicting the summer monsoon of the country. The newly identified predictors forecast monsoon with an error of 4.2%, which is significant for the prediction of the complex phenomenon of monsoon.

Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering.

To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme, where a concept of virtual parallel-projection matching the measurement condition is introduced to aid the “compressive sensing” in the reconstruction procedure, and meanwhile, the non-local spatially adaptive filtering exploring the a priori information of the mutual similarities in natural images is adopted to recover the unknowns in the transformed sparse domain. Consequently, the reconstructed images with the proposed sparse-view scheme can be evidently improved in comparison to those with the universal back-projection method, for the cases of same sparse views. The proposed approach has been validated by the simulations and ex vivo experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.

Estimating sequence similarity from read sets for clustering next-generation sequencing data

Computing mutual similarity of biological sequences such as DNA molecules is essential for significant biological tasks such as hierarchical clustering of genomes. Current sequencing technologies do not provide the content of entire biological sequences; rather they identify a large number of small substrings called reads, sampled at random places of the target sequence. To estimate similarity of two sequences from their read-set representations, one may try to reconstruct each one first from its read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences. Due to the nature of data, sequence assembly often cannot provide a single putative sequence that matches the true DNA. Therefore, we propose instead to estimate the similarities directly from the read sets. Our approach is based on an adaptation of the Monge-Elkan similarity known from the field of databases, avoiding the sequence assembly step. For low-coverage (i.e. small) read set samples, it yields a better approximation of the true sequence similarities. This in turn results in better clustering in comparison to the first-assemble-then-cluster approach. Put differently, for a fixed estimation accuracy, our approach requires smaller read sets and thus entails reduced wet-lab costs.

Asymptotic Similarity of Time Correlation Functions and Shape of the 13C and 29Si NMR Spectra in Diamond and Silicon

Based on the proposed theory, we have investigated the shape of the NMR absorption spectra for 13C and 29Si nuclei in diamond and silicon crystals attributable to the internuclear dipole–dipole interaction. In accordance with the available experimental data, we have considered both crystals with a 100% content of magnetoactive isotopes and crystals with a comparatively low dilution by nonmagnetic nuclei. The time correlation functions (the first of which is the Fourier transform of the NMR spectrum) arising in an infinite chain of coupled differential equations are shown to be mutually similar with a slight time delay. The proposed theory allows the spectrum to be calculated analytically. The results obtained agree satisfactorily with the experimental ones. It is noted that the mutual similarity of the time correlation functions is probably a corollary of the development of dynamical chaos in the system

Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF) exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, it uses standard pre-trained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.

Cascade feature selection and coarse-to-fine mechanism for nighttime multiclass vehicle detection

Nighttime vehicle detection is part of an intelligent transportation system for road safety, driving navigation, and surveillance at night. However, previous nighttime vehicle detection methods only deal with a single class or two classes of vehicles. This paper presents an effective system based on cascade feature selection and a coarse-to-fine mechanism for detecting preceding multiclass vehicles at night. First, we train a coarse-level classifier using contrast features. Second, we combine a cascade selection framework with feature mutual correlation, similarity and regions’ overlap to select a set of image regions, where we can extract effective features. Three features, including local binary pattern, histogram of oriented gradients, and four direction features, are extracted from the selected regions for training a fine-level multiclass vehicle classifier. During the detection stage, we utilize a coarse-to-fine mechanism. In the coarse level, a multiscale sliding window is classified by the coarse-level classifier to find regions of interest (ROIs) that are likely to be vehicles. In the fine level, these ROIs are identified by the trained multiclass vehicle classifier. Evaluations on a Hong Kong nighttime multiclass vehicle dataset show that our proposed system successfully detects a car, taxi, bus, and minibus within nighttime images under different scenes. Quantitatively, our proposed system obtains 95.48% detection rate at 0.055 false positives per image, outperforming some state-of-the-art detection approaches including two current nighttime vehicle detection methods, and two deep learning-based methods.

Exploiting User Posts for Web Document Summarization

Relevant user posts such as comments or tweets of a Web document provide additional valuable information to enrich the content of this document. When creating user posts, readers tend to borrow salient words or phrases in sentences. This can be considered as word variation. This article proposes a framework that models the word variation aspect to enhance the quality of Web document summarization. Technically, the framework consists of two steps: scoring and selection. In the first step, the social information of a Web document such as user posts is exploited to model intra-relations and inter-relations in lexical and semantic levels. These relations are denoted by a mutual reinforcement similarity graph used to score each sentence and user post. After scoring, summaries are extracted by using a ranking approach or concept-based method formulated in the form of Integer Linear Programming. To confirm the efficiency of our framework, sentence and story highlight extraction tasks were taken as a case study on three datasets in two languages, English and Vietnamese. Experimental results show that: (i) the framework can improve ROUGE-scores compared to state-of-the-art baselines of social context summarization and (ii) the combination of the two relations benefits the sentence extraction of single Web documents.

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching.

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

A New Evaluation Method of Contact Area at Interface Between Pulsed Surface Discharge and Water

The authors propose a new method for evaluating the contact area of a surface discharge on water by analyzing the resistance of water solution. This method supplements optical observations of light emissions and the Schlieren method. The contact area was modeled as a disk electrode, and the theoretical resistance of water was calculated under the assumption of a mutual similarity between the current and electrostatic fields. A simulated electrostatic field was calculated using a common charge-simulation method. The experimental values of the resistance of water were obtained by dividing the measured voltage drop at water by the measured current. The contact area evaluation was performed by comparing the experimental and theoretical resistance values. The contact area increases with increasing the applied voltage and is practically independent of water depth.

Complex long-term activity of the post-nova X Serpentis

Aims. X Ser is a cataclysmic variable (CV) which erupted as a classical nova in 1903. In this work we use over 100 years of photometry to characterize the long-term light curve of X Ser, with the aim of interpreting the post-nova activity in X Ser in the context of behaviors in other old novae. Methods. This analysis of its long-term optical activity uses the data from the Digital Access to a Sky Century @ Harvard (DASCH), AAVSO, and Catalina Real-time Transient Survey databases, supplemented by the data of other authors. Results. We show that X Ser displays a strong complex activity with the characteristics of various CV types after the return to quiescence from its classical nova outburst. Both nova-like and dwarf nova (DN) features are present. The decaying branches of the individual post-nova outbursts display large mutual similarities and obey the Bailey law for outbursts of DNe. These outbursts of X Ser constitute a uniform group (inside-out outbursts), and their decaying branches can be explained by propagation of cooling front through the accretion disk. In the interpretation, X Ser rapidly transitioned to a thermal-viscous instability regime of the disk, initially only intermittently. The occurrence of the DN outbursts shortly after the end of the nova outburst suggests that the mass transfer rate into the disk was usually not sufficiently high to prevent a thermal-viscous instability of this post-nova. The very long orbital period, and hence large accretion disk of X Ser can contribute to this.

Forms as Simple and Individual Grounds of Things’ Natures

To understand Aristotle’s conception of form, we have to see clearly the relationship between his account and Plato’s Theory of Forms. I offer a novel interpretation of Aristotle’s Moderate Realism, in which forms are simple particulars that ground the character and mutual similarity of the entities they inform. Such an account has advantages in three areas: explaining (1) the similarity of particulars, (2) the synchronic unity of composite particulars, and (3) the diachronic unity or persistence of intrinsically changing particulars.

Robust MST-Based Clustering Algorithm.

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

Partially Local Multi-way Alignments

Multiple sequence alignments are an essential tool in bioinformatics and computational biology, where they are used to represent the mutual evolutionary relationships and similarities between a set of DNA, RNA, or protein sequences. More recently they have also received considerable interest in other application domains, in particular in comparative linguistics. Multiple sequence alignments can be seen as a generalization of the string-to-string edit problem to more than two strings. With the increase in the power of computational equipment, exact, dynamic programming solutions have become feasible in practice also for 3- and 4-way alignments. For the pairwise (2-way) case, there is a clear distinction between local and global alignments. As more sequences are considered, this distinction, which can in fact be made independently for both ends of each sequence, gives rise to a rich set of partially local alignment problems. So far these have remained largely unexplored. Here we introduce a general formal framework that gives raise to a classification of partially local alignment problems. This leads to a generic scheme that guides the principled design of exact dynamic programming solutions for particular partially local alignment problems.

Dynamic mutual information similarity based transient process identification and fault detection

Industrial process status can be modified according to continuous operations, which are required by different production specifications. Commonly, in a multimode process, attention was always paid to stable modes, while transitions were neglected. In a transition process, the process may be externally time varying or nonstationary so that the identification and the modelling are intractable to implement using traditional statistical methods. In this article, a transition identification and process monitoring method is proposed to handle the above problems based on a novel dynamic mutual information similarity (DMIS) analysis. Firstly, a multimode process is represented by a series of overlapping moving windows to consider the local information. In order to extract the corresponding dynamic information, each of these windows is modelled using the dynamic partial least squares (DPLS) method. Then, the mutual information algorithm is introduced to calculate the similarity between different latent variables. The hierarchical clustering method is employed to transfer the similarity information into a visualized dendrogram where the whole process, including a transition, is divided into several segments. The statistical characteristics in each segment are relatively stable and can be characterized by conventional multivariate statistical process control methods. Online identification and fault detection are then carried out for the multimode process through a series of DPLS models, established in the offline steps. The feasibility and effectiveness of the proposed method are validated by the Tennessee Eastman (TE) benchmark and a real process. The results of the proposed methods have shown superior performance compared to previous works.

Detecting image differences based on reference EMD-filters

In our previous works dealing with detecting differences in images in the case of substantial variations in brightness and geometry of an object, we proposed a morphological scheme for image analysis based on diffusion and reference mosaic filters. The filters were defined through the heat kernels of the similarity of image fragments. In this paper, we propose an implementation of this morphological scheme using original reference Earth mover's distance (EMD) filters, in which the optimal matrices of the mutual similarity of mosaic forms are calculated through a linear programming method.

Some properties of Lin–Wong divergence on the past lifetime data

ABSTRACTMeasures of statistical divergence are used to assess mutual similarities between distributions of multiple variables through a variety of methodologies including Shannon entropy and Csiszar divergence. Modified measures of statistical divergence are introduced throughout the present article. Those modified measures are related to the Lin–Wong (LW) divergence applied on the past lifetime data. Accordingly, the relationship between Fisher information and the LW divergence measure was explored when applied on the past lifetime data. Throughout this study, a number of relations are proposed between various assessment methods which implement the Jensen–Shannon, Jeffreys, and Hellinger divergence measures. Also, relations between the LW measure and the Kullback–Leibler (KL) measures for past lifetime data were examined. Furthermore, the present study discusses the relationship between the proposed ordering scheme and the distance interval between LW and KL measures under certain conditions.