Mutual Similarity Research Articles

Bioclimatic gradients and soil property trends from northernmost mainland Norway to the Svalbard archipelago. Does the arctic biome extend into mainland Norway?

The boundary between the boreal and arctic biomes in northwest Europe has been a matter of debate for many years. Some authors consider that the boundary is marked by the northern limit of tree growth in the northernmost Norwegian mainland. In this study we have collected air and soil temperature data from 37 heath stands from northern Finnmark (71°N), the northernmost part of the Norwegian mainland, through Bear Island (74°N) in the Barents sea, to Adventsdalen (78)°N (in Spitsbergen) in Svalbard archipelago. In Finnmark, plots both south and north of the treeline were investigated. Vegetation and soil chemistry analyses were performed on the plots in Finnmark and Svalbard. Significant decreasing south-north trends in air and soil temperatures were observed from Finnmark to Spitsbergen. Soils in Finnmark were acidic and rich in organic matter, while those on Adventsdalen were basic and poor in organic matter. Vegetational analysis identified five communities: three in Finnmark and two on Adventsdalen. The communities in Finnmark had marked mutual similarities but were very different from those on Adventsdalen. No significant ecological differences between heaths south and north of the treeline in Finnmark were observed. Air and soil temperature variables in Finnmark were outside the recognized range for the arctic biome and inconsistent with the presence of permafrost both south and north of the treeline. A major difference between Finnmark and Spitsbergen was amount of soil frost and length of the growing season. Our results suggest that the boreal biome extends all the way to the north coast of mainland Norway; and previously used division of heaths in Finnmark into boreal, alpine and arctic biomes is not justified.

Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

Quality assessment of the 3D printed surfaces is one of the crucial issues related to fast prototyping and manufacturing of individual parts and objects using the fused deposition modeling, especially in small series production. As some corrections of minor defects may be conducted during the printing process or just after the manufacturing, an automatic quality assessment of object’s surfaces is highly demanded, preferably well correlated with subjective quality perception, considering aesthetic aspects. On the other hand, the presence of some greater and more dense distortions may indicate a reduced mechanical strength. In such cases, the manufacturing process should be interrupted to save time, energy, and the filament. This paper focuses on the possibility of using some general-purpose full-reference image quality assessment methods for the quality assessment of the 3D printed surfaces. As the direct application of an individual (elementary) metric does not provide high correlation with the subjective perception of surface quality, some modifications of similarity-based methods have been proposed utilizing the calculation of the average mutual similarity, making it possible to use full-reference metrics without the perfect quality reference images, as well as the combination of individual metrics, leading to a significant increase of correlation with subjective scores calculated for a specially prepared dataset.

Characterization and pre-industrial validation of Streptococcus thermophilus strains to be used as starter cultures for Crescenza, an Italian soft cheese

The aim of this work was to search for new candidate strains to be included in a culture for Crescenza, a rindless soft cheese, today produced mainly at industrial level using selected starter cultures composed of S. thermophilus. Performance testing was applied to 29 pre-selected strains and a scoring approach was developed to identify the most suitable candidates to be employed in Crescenza cheesemaking. Eight S. thermophilus strains fulfilling most of the desired properties (e.g., high phage resistance, fast acidification rate, no growth below 20 °C, NaCl sensibility, no post acidification at 4 °C) were selected. These strains were grouped in pairs to design different starter culture formulations, which were preliminary tested for the production of Crescenza cheeses at laboratory scale. Two couples of binary cultures (designed Phage rotation 1 and Phage rotation 2) were finally designed and used as starters in pilot scale cheesemaking. The combinations, especially those designed in Phage rotation 1, appeared to be suitable for Crescenza production and showed mutual similarity in terms of strain characteristics, technological performance, and cheese quality. The selection method and ranking approach presented in this work may be adapted to other species of LAB showing traits of industrial interest.

Approach of spectral information-based image registration similarity

For local intensity-based image registration methods, a template of a predefined size is adopted. Some measure of similarity between the images is used to determine when the optimal alignment has occurred. Most intensity-based similarity utilizes the statistical and spatial information of an image. However, there are significant nonlinear radiometric differences between infrared and visible data. Images of natural scenes usually do not have enough spatial features within the template. Hence, many similarities will be useless when dealing with infrared and visible data. Minor eigenvalues (ME) image registration similarity is presented with the exploitation the spectral properties of remote sensing images. ME similarity is based on the linear spectral mixture model and detects control points through searching the minimum of ME of the covariance matrix. Experiments on Landsat-7 satellite Enhanced Thematic Mapper Plus data are performed to verify and evaluate the effectiveness. Transformation performance curves, correct match ratio (CMR), and registration accuracy are also discussed. According to the data, root-mean-square error of phase correlations is 0.0722 pixels and the CMR of ME similarity is nearly 100%. The results on the basis of TM1, TM2, TM3, and TM4 band images indicate that the proposed similarity holds promise for infrared and color image registration in natural scenes, with advantages over previous normal mutual information and gradient mutual information similarities.

Neoliberalism and sociocultural specificities: a discourse analysis of early childhood curriculum policies in Australia, China, New Zealand, and Singapore

ABSTRACT Many governments have launched the top-down early childhood curriculum (ECC) reforms to enhance the accountability of early childhood education and care (ECEC) services. The present discourse analysis of latest ECC policies across four diverse but representative countries – Australia, China, New Zealand, and Singapore – aims to examine the effects of neoliberalism and contextualization through within- and cross-case analyses. Our findings revealed that despite the mutual interactions and similarities, neoliberal ECC policies had been developed in context-specific ways across countries. Children's agency had been commonly valued to recognize its essential role in effective learning, which was constructed in play, social interaction, and community participation. Moreover, Australia and New Zealand emphasized the development of the culturally competent child on top of children's holistic development, without segregating children's learning into domains. These findings demonstrate the confounding effect produced by the diverse shaping forces in terms of defining the ideal ECC across countries – ‘curriculum hybridization’.

Expressionism in European and Chinese painting (late XIX – early XXI centuries)

This article examines the impact of Chinese traditional painting upon the formation of Western expressionism, as well as interprets the influence of Western expressionism upon Chinese painting in expressionist manner of different periods. The author reveals the mutual influence, similarities and differences between Western expressionism and Chinese painting in expressionist manner, Chinese imagery oil painting, Chinese colored ink painting, and Chinese imagery painting in Western style. Based on correlation between the spiritual ideology of painting and artistic form, the author carries out a comparative analysis of spiritual and formal factors of Chinese and Western painting systems for the purpose of determination of their mutual influence, and how it affects the emergence of new concepts in painting. The structure of brush stroke of the artist defines his aesthetic spirit. The article determines the “cyclic” nature of interinfluence processes between Chinese and Western painting systems that stimulate the development of human civilization. The analysis of corresponding cultural factors allows assessing the individual artistic characteristics of painting. Both, Chinese and Western painting systems entered the period when spiritual ideology of painting interacts with the artistic forms, opening the era of “globalization” of the language of painting.

Some Possibilities of Computer Linguistics on an Example of Analysis of Novels

This paper shows some aspects of statistical analysis of well-known novels: Death and the Dervish by Meša Selimović (1966), Autobiography by Branislav Nušić (1924) and in the Registrar's Office by Ante Kovačić (1888). The goal of the analysis is to point to mutual similarities and differences of statistical data in those texts and to compare them with the up to date findings in that field. A part of the analysis relates to comparison of languages of these writers with today's language, used by column authors in electronic media. These kinds of researches belong to linguistics, as a science on language, but the results may be used in the contemporary development of artificial intelligence.

Supervised discrete hashing through similarity learning

Supervised hashing has achieved better accuracy than unsupervised hashing in many practical applications owing to its use of semantic label information. However, the mutual relationship between semantic labels is always ignored when leveraging label information. In addition, the major challenge in learning hash is of handling the discrete constraints imposed on the hash codes, which typically transform the hash optimization into NP-hard problems. To address these issues, a form of supervised discrete hashing through learning mutual similarities is proposed. Different from the existing supervised hashing methods that learn hash codes from least-squares classification by regressing the hash codes to their corresponding labels, we leverage the mutual relation between different semantic labels to learn more stable hash codes. In addition, the proposed method can simultaneously learn the discrete hash codes for training samples and the projections between the original features and their corresponding hash codes for the out-of-sample cases. Experiments have beeen performed on two public datasets. The experimental results demonstrate the superiority of the proposed method.

Development of T-Cell Therapy Targeting Hematopoietic Minor Histocompatibility Antigen HA-1

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is currently the only curative therapy for hematological malignancies yet in nearly one-third of patients, it is followed by a relapse of the disease contributing to high mortality. In fully HLA-matched allo-HSCT graft versus leukemia reaction is driven by the recognition of the minor histocompatibility antigens (MiHAs) - endogenous polymorphic peptides presented by MHC. Particularly, HA-1 MiHA is a promising target for immunotherapy. HA-1 is presented by frequent among Caucasians HLA allele - A*02:01. The single nucleotide variation in ARGHAP45 gene which generates the MiHA has the optimal allelic distribution, thus immunogenic mismatch occurs in 30% of allo-HSCT. Also, ARGHAP45 is overexpressed in certain types of leukemia. Here we aim to develop HA-1-specific T-cells for post-transplant relapse therapy. To obtain the sequences of HA-1-specific T-cell receptors (TCRs), naive CD8+ T-cells from 3 HLA-A*02:01 positive and HA-1 negative donors were expanded in vitro using autologous dendritic cells pulsed with HA-1 peptide. Antigen-specific cells were enriched by CD137 marker expression or by HLA-tetramer staining, RNA from positive and negative fractions was isolated for cDNA library preparation. The α and β TCR-repertoires were sequenced using the Illumina MiSeq system. The representative enrichment plot is shown in Figure 1 (A - α chains, B - β chains). Each circle represents a unique TCR. The vertical axis shows the normalized frequency in enriched fraction, the horizontal axis shows the normalized frequency in tetramer or CD137 negative flowthrough. HA-1-specific TCRs are denoted by green filled circles.TCRs were considered to be HA-1-specific if they were significantly enriched in CD137+ or tetramer+ fraction (exact Fisher test, p=0.05). In total, 49 α and 80 β chains were described. To determine the degree of similarity between HA-1-specific TCRs Levenstein distance was calculated between amino acid sequences of complementarity-determining region 3 for both chains. Sequences of previously published HA-1-specific TCRs were also included in the analysis (Verdijk et.al., Haematologica, 2002; Bleakley et.al., 2017, WO2018058002A1). α chains demonstrated low degree of mutual similarity, the majority of sequences did not belong to any cluster (Figure 2A, sequences with the Levenstein distance <3 are connected). In contrast, a significant proportion of β chains were organized in a few clusters containing sequences from all 3 donors and previously published data (Figure 2B). We selected 14 α and 12 β HA-1-specific TCR chains (marked by the black dots in Figure 2). Clones were picked to represent separate clusters of similarity to Levenstein metrics, and unique sequences. Selected α and β-chains were cloned for subsequent functional screening in different combinations. Besides, we developed the modular lentiviral backbone for manufacturing HA-1 specific transgenic CD8+ T-cells. Our approach utilizes Golden Gate Cloning, which allows rapid assembly of lentiviral backbone carrying any combination of TCR α and β chains fused with the selective marker for sorting via p2A peptides. We used truncated CD34 as a transduced cell surface marker for the rapid separation of transduced cells by clinical-grade antibodies and subsequent expansion. In order to prevent the mispairing of transgenic TCR with endogenous one, CRISPR/Cas9 knockout strategy of endogenous TCR chains was developed. We used guide RNAs specific to TRAC,TRBC1 and TRBC2 genes and recombinant Cas9. The efficiency was demonstrated on Jurkat E6-1 cell line, the knockout was confirmed both by flow cytometry and genotyping of the modified cells using fragment analysis. Constant regions of the transgenic TCRs were modified to prevent cleavage by Cas9, the resistance was confirmed by in vitro Cas9 digestion assay. Moreover additional cysteines were introduced in the constant regions of transgenic TCRs for increased transgenic TCR stability. Cytotoxic activity of modified cells will be confirmed on lymphoblastoid cell lines and patient leukemia samples, cytokine secretion of modified cells will be detected using ELISPOT. The work was supported by the Russian Foundation for Basic Research grant 19-29-04156. Disclosures No relevant conflicts of interest to declare.

M-Denclue for Effective Data Clustering in High Dimensional Non-Linear Data

Clustering is a data mining task devoted to the automatic grouping of data based on mutual similarity. Clustering in high-dimensional spaces is a recurrent problem in many domains. It affects time complexity, space complexity, scalability and accuracy of clustering methods. Highdimensional non-linear datausually live in different low dimensional subspaces hidden in the original space. As high‐dimensional objects appear almost alike, new approaches for clustering are required. This research has focused on developing Mathematical models, techniques and clustering algorithms specifically for high‐dimensional data. The innocent growth in the fields of communication and technology, there is tremendous growth in high dimensional data spaces. As the variant of dimensions on high dimensional non-linear data increases, many clustering techniques begin to suffer from the curse of dimensionality, de-grading the quality of the results. In high dimensional non-linear data, the data becomes very sparse and distance measures become increasingly meaningless. The principal challenge for clustering high dimensional data is to overcome the “curse of dimensionality”. This research work concentrates on devising an enhanced algorithm for clustering high dimensional non-linear data.

Verification of the Thin-walled Beam Theory with Application of FEM and Shell Modelling

This article presents the results of a systematic comparison of predictions of the thin-walled beam theory and shell theory in the context of bending of elastic thin-walled beams with an open or closed cross-section. The results for shell theory were obtained using FEM and the ABAQUS/Standard program. The bending task of the thin-walled beam was decomposed into the task with a symmetrical and anti-symmetrical loading, so that the bending and torsion of the thin-walled element would be better visible. In order to objectify the comparison, two coefficients characterizing the mutual similarity of functions were introduced: root mean square deviation coefficient determined for the selected cross-section (in relation to the entire contour) and a coefficient determining the maximum relative difference of a given quantity. The comparison was made for both the stress and the displacement state.

Tangled Worldview Model of Opinion Dynamics

We study the joint evolution of worldviews by proposing a model of opinion dynamics, which is inspired in notions from evolutionary ecology. Agents update their opinion on a specific issue based on their propensity to change -- asserted by the social neighbours -- weighted by their mutual similarity on other issues. Agents are, therefore, more influenced by neighbours with similar worldviews (set of opinions on various issues), resulting in a complex co-evolution of each opinion. Simulations show that the worldview evolution exhibits events of intermittent polarization when the social network is scale-free. This, in turn, trigger extreme crashes and surges in the popularity of various opinions. Using the proposed model, we highlight the role of network structure, bounded rationality of agents, and the role of key influential agents in causing polarization and intermittent reformation of worldviews on scale-free networks.

Enhanced Itch Intensity Is Associated with Less Efficient Descending Inhibition Processing for Itch But Not Pain Attenuation in Chronic Dermatology Patients.

The study aims were 1) to investigate the direction of mutual inhibitory pathways on itch intensity by utilizing conditioned pain modulation paradigms for pain and itch attenuation and 2) to explore whether itch severity is affected by the individual pain sensitivity profile, as well as pain scores reported during the tests and the past week. Cross-sectional. Testing was conducted at the Department of Dermatology, Rambam Health Care Campus. Forty patients suffering from chronic skin disorders associated with itch and treated in the Dermatology Clinic at Rambam Health Care Campus participated in the study. Efficacy of descending inhibition was evaluated by two conditioned pain modulation (CPM) paradigms: by pruriception (CPMItch) induced by cold and heat as counterstimuli to inhibit itch intensity and by nociception (CPMPain). Severity and interference of clinical pain were assessed using the Brief Pain Inventory (BPI). Robust CPMItch responses were obtained following the various noxious stimulations. No associations were observed between CPMPain and CPMItch, itch severity, skin disease severity, and clinical pain symptoms. According to the linear regression model, itch severity was independently associated with less efficient CPMItch (B = -0.750, P < 0.001) and more efficient CPMPain (B = 0.031, P = 0.016), which affects itch in opposing manners. Findings indicate that the intrinsic capacity to inhibit pain and itch by exposure to exogenous noxious stimuli autonomously affects itch intensity in an opposing manner. These findings may shed new light on the mutual mechanistic similarity and dissimilarity between pain and itch and their hierarchy.

Effective construction of classifiers with the k-NN method supported by a concept ontology

In analysing sensor data, it usually proves beneficial to use domain knowledge in the classification process in order to narrow down the search space of relevant features. However, it is often not effective when decision trees or the k-NN method is used. Therefore, the authors herein propose to build an appropriate concept ontology based on expert knowledge. The use of an ontology-based metric enables mutual similarity to be determined between objects covered by respective concept ontology, taking into consideration interrelations of features at various levels of abstraction. Using a set of medical data collected with the Holter method, it is shown that predicting coronary disease with the use of the approach proposed is much more accurate than in the case of not only the k-NN method using classical metrics, but also most other known classifiers. It is also proved in this paper that the expert determination of appropriate structure of ontology is of key importance, while subsequent selection of appropriate weights can be automated.

Network inference from the timing of events in coupled dynamical systems.

Spreading phenomena like opinion formation or disease propagation often follow the links of some underlying network structure. While the effects of network topology on spreading efficiency have already been vastly studied, we here address the inverse problem of whether we can infer an unknown network structure from the timing of events observed at different nodes. For this purpose, we numerically investigate two types of event-based stochastic processes. On the one hand, a generic model of event propagation on networks is considered where the nodes exhibit two types of eventlike activity: spontaneous events reflecting mutually independent Poisson processes and triggered events that occur with a certain probability whenever one of the neighboring nodes exhibits any of these two kinds of events. On the other hand, we study a variant of the well-known SIRS model from epidemiology and record only the timings of state switching events of individual nodes, irrespective of the specific states involved. Based on simulations of both models on different prototypical network architectures, we study the pairwise statistical similarity between the sequences of event timings at all nodes by means of event synchronization and event coincidence analysis (ECA). By taking strong mutual similarities of event sequences (functional connectivity) as proxies for actual physical links (structural connectivity), we demonstrate that both approaches can lead to reasonable prediction accuracy. In general, sparser networks can be reconstructed more accurately than denser ones, especially in the case of larger networks. In such cases, ECA is shown to commonly exhibit the better reconstruction accuracy.

Using patch-based image synthesis to measure perceptual texture similarity

The perceptual similarity of textures has gained considerable attention in the computer vision and graphics communities. Here, we focus on the challenging task of estimating the mutual perceptual similarity between two textures from materials on a consistent scale. Unlike previous studies that more or less directly queried pairwise similarity from human subjects, we propose an indirect approach that is inspired by the notion of just-noticeable differences (JND). Similar metrics are common in imaging and color science, but so far have not been directly transferred to textures, since they require the generation of intermediate stimuli. Using patch-based statistical texture synthesis, we produce continuous transitions between pairs of textures. In a user experiment, participants are then asked to locate an interpolated specimen in the linear continuum. Our intuition is that the JND, defined as the uncertainty with which participants perform this task, is closely related with the perceived pairwise texture similarity. Using a dataset of fabric textures, we show that this metric is particularly suitable to address fine-grained similarities, produces approximately interval-scale measurements and is additionally convenient for crowdsourcing. We further validate our approach by comparing it with a well-established data collection technique using the same dataset.

A Framework for Optimal Attribute Evaluation and Selection in Hesitant Fuzzy Environment Based on Enhanced Ordered Weighted Entropy Approach for Medical Dataset.

Background:In this paper, a generic hesitant fuzzy set (HFS) model for clustering various ECG beats according to weights of attributes is proposed. A comprehensive review of the electrocardiogram signal classification and segmentation methodologies indicates that algorithms which are able to effectively handle the nonstationary and uncertainty of the signals should be used for ECG analysis. Extensive research that focuses on incorporating vagueness in the form of fuzzy sets, fuzzy rough sets and hesitant fuzzy sets (HFS) has been in past decades. Objective: The paper aims to develop an enhanced entropy based on the clustering technique for calculating the weights of the attributes to finally generate appropriately clustered attributes. Material and Methods: Finding optimal attributes to make a decision has always been a matter of concern for the researchers. Metrics used for optimal attribute generation can be broadly classified into mutual dependency, similarity, correlation and entropy based metrics in fuzzy domain .The experimentation has been carried out on ECG dataset in a hesitant fuzzy framework with four attributes. Results: We propose a novel correlation based on an algorithm that takes entropy based weighted attributes as input which effectively generates a relevant and non-redundant set of attributes. We have also derived correlation coefficient formulas for HFSs and applied them to clustering analysis under framework of hesitant fuzzy sets. The results show the comparison of the proposed mathematical model with the existing similarity based on algorithms. Conclusion: The selection of optimal relevant attributes certainly highlights the robustness and efficacy of the proposed approach. The entire experimentation and comparative results help us conclude that selection of optimal attributes in hesitant fuzzy domain certainly prove to be a powerful tool in order to express uncertainty in the process of data acquisition and classification.

Self-conflict in Seid Kamily Emamy’s The City of My Heart and T. S. Eliot’s The Love Song of J Alfred Prufrock

Man’s internal self-conflict is something of great interest among philosophers, playwrights, and poets. It is an everlasting and universal issue of human psychological discordances which continuously draws the poets’ attention. For this reason, the present research tends to compare self-conflict in two selected poems; “The City of My Heart” by the Kurdish poet Seid Kamily Emamy (1903-1989) and “The Love Song of J Alfred Prufrock” by T. S. Eliot (1888-1965). In the above-mentioned poems, the poets shed light on man’s indecision from two different perspectives, but still, there are some mutual similarities between them. The central conflicts of these poems are encapsulated in the figures of their apparent heroes. The study consists of three sections. The first section is an introduction. Then the second section attempts to give an account of self-conflict in “The City of My Heart” by Seid Kamily Emamy. Following that, the third section explores self-conflict in “The Love Song of J Alfred Prufrock” by T. S. Eliot. Finally, there is a conclusion to the study.

Underpotential and Electroless Pb Monolayer Deposition on Ru(0001)

The work reporting a detailed and comparative study of Pb UPD and Pb e-less ML deposition on Ru(0001) substrate is presented. The electrochemical results are analyzed through the scope of the adsorption isotherm formalism where parameters describing the thermodynamics of deposited Pb monolayer obtained by each process are compared. In addition to these results, the in situ STM and electrochemical quartz micro balance data are presented for each process identifying the mutual mechanistic similarities and differences between electroless Pb and underpotential Pb monolayer. Considerable applications of Ru metal in microelectronics and catalysis rise significance of these results for variety of future developments in these areas.

Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information †.

Applying people detectors to unseen data is challenging since patterns distributions, such as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt frame by frame people detectors during runtime classification, without requiring any additional manually labeled ground truth apart from the offline training of the detection model. Such adaptation make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation discriminates between relevant instants in a video sequence, i.e., identifies the representative frames for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration (i.e., detection threshold) of each detector under analysis, maximizing the mutual information to obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not require training the detectors for each new scenario and uses standard people detector outputs, i.e., bounding boxes. The experimental results demonstrate that the proposed approach outperforms state-of-the-art detectors whose optimal threshold configurations are previously determined and fixed from offline training data.