Meaningful Details Research Articles

Generalized multilevel B-spline approximation for scattered data interpolation in image processing

This paper proposes a Generalized Multilevel B-spline Approximation (GMBA) method, which addresses scattered data interpolation problems in image processing. Mathematically, the GMBA provides a better solution for the B-spline control lattice by superimposing identical level B-splines compared with traditional Multilevel B-spline Approximation (MBA). Specifically, the GMBA allows the spacing of next control lattice to be subdivided arbitrarily or remained unchanged, which is determined by a predefined spacing set or the current error level. These improvements bring higher approximation accuracy and more flexibility for algorithm design to avoid over-fitting. In this paper, basic GMBA algorithm and its refined algorithm are compiled for image processing. Finally, six relevant cases are involved to test the GMBA, including surface approximation, image enlargement, image completion, and Salt-and-Pepper (SAP) noise removal. The experimental results show that the GMBA has better performance than the MBA in surface approximation and image processing, performs comparatively fast with the best performance on more than half of the standard test images compared with traditional algorithms, and has partially better performance even than deep learning algorithms. The GMBA can effectively recover meaningful details in images contaminated with even extremely high SAP noise level (up to 99%).

Self-supervised representation learning of filtration barrier in kidney

While the advance of deep learning has allowed to automate many tasks in bioimage analysis, quantifying key visual features of biological objects in an image, such as cells, organs, or tissues, is still a multi-step and laborious task. It requires image segmentation and definition of features of interest, which often might be image- and problem-specific. This approach requires image labeling and training of the segmentation method as well as manual feature design and implementation of dedicated procedures for their quantification. Here we propose a self-supervised learning (SSL) approach to encoding in microscopy images morphological features of molecular structures that play role in disease phenotype and patient clinical diagnosis. We encode super-resolution images of slit diaphragm (SD)—a specialized membrane between podocyte cells in kidney—in a high-dimensional embedding space in an unsupervised manner, without the need of image segmentation and feature quantification. We inspect the embedding space and demonstrate its relationship to the morphometric parameters of the SD estimated with a previously published method. The SSL-derived image representations additionally reflect the level of albuminuria—a key marker of advancement of kidney disease in a cohort of chronic kidney disease patients. Finally, the embeddings allow for distinguishing mouse model of kidney disease from the healthy subjects with a comparable accuracy to classification based on SD morphometric features. In a one step and label-free manner the SSL approach offers possibility to encode meaningful details in biomedical images and allow for their exploratory, unsupervised analysis as well as further fine-tuning for specialized supervised tasks.

A Systematic Guide for Conducting Thematic Analysis in Qualitative Tourism Research

Thematic analysis is a widely used method for analyzing qualitative data in social sciences research. This research article provides a step-by-step guide on conducting thematic analysis manually, emphasizing the importance of understanding the process for researchers seeking a deeper grasp of the nuances involved. The article highlights the key steps, including familiarizing oneself with the data, generating initial codes, searching for themes, reviewing and refining them, defining and naming the themes, and producing a final report. Through iterative reading, note-taking, and coding techniques, researchers can delve deep into the data, capture meaningful details, and uncover rich insights. Thematic analysis offers a valuable avenue for developing theoretical understanding, generating nuanced interpretations, and contributing to knowledge advancement in tourism research. The article also discusses the relevance and adaptability of thematic analysis in various qualitative research designs and methodologies. By prioritizing transparency, rigor, and a manual coding approach, researchers can ensure the validity and reliability of their thematic analysis, leading to robust and insightful findings.

The Vittoria Alata from Brescia: a combined neutron techniques and SEM-EDS approach to the study of the alloy of a bronze Roman statue

The “Vittoria Alata” (Winged Victory) from Brescia, dated around the 1st Century BCE, is one of the most well-known Roman bronzes discovered in Italy. The statue has recently undergone a restoration coordinated by the Opificio delle Pietre Dure di Firenze (OPD) that offered a unique opportunity for a thorough investigation on its manufacturing methods and previous non-documented repair/restauration interventions. In this paper, we present the results of the study of the metal alloy, obtained by neutron techniques and Scanning Electron Microscope analysis (SEM-EDS), on 24 samples of mm-size from various parts of the statue and on a cm-size feather fragment, believed to have detached from the right wing. Time of Flight Neutron Diffraction (ToF-ND) and Neutron Imaging (NI) measurements were performed at the ISIS neutron and muon source, Rutherford Appleton Laboratory, UK; SEM-EDS analysis were carried out at the OPD Scientific Laboratory, Florence, IT. The representativeness of ToF-ND on millimetric samples was confirmed by comparing the results obtained on several large, irradiated areas (few cm2) of an entire bronze frame fragment from the same excavation context with the ones on a millimetric sample taken from the same frame. This investigation led to the characterization of the alloys used for the different parts of the statue and to identify meaningful details on the manufacturing method.

Plausibility in models and fiction: What integrated assessment modellers can learn from an interaction with climate fiction

Integrated assessment models (IAMs) are critical tools to explore possible pathways to a low-carbon future. By simulating complex interactions between social and climatic processes, they help policymakers to systematically compare mitigation policies. However, their authoritative projections of cost-effective and technically feasible pathways restrict more transformative low-carbon imaginaries, especially because IAM pathways are often understood in terms of probability rather than plausibility. We suggest an interaction with climate fiction could be helpful to address this situation. Despite fundamental differences, we argue that both IAMs and climate fiction can be seen as practices of storytelling about plausible future worlds. For this exploratory article, we staged conversations between modellers and climate fiction writers to compare their respective processes of storytelling and the content of both their stories and story-worlds, focusing specifically on how they build plausibility. Whereas modellers rely on historical observations, expert judgment, transparency and rationality to build plausibility, fiction writers build plausibility by engaging with readers’ life worlds and experience, concreteness and emotionally meaningful details. Key similarities were that both modellers and fiction writers work with what-if questions, a causally connected story and build their stories through an iterative process. Based on this comparison, we suggest that an interaction between IAMs and climate fiction could be useful for improving the democratic and epistemic qualities of the IAM practice by 1) enabling a more equal dialogue between modellers and societal actors on plausible futures and 2) critically reflecting upon and broadening the spectrum of plausible futures provided by IAMs.

Limited attention shifts judgments in socioindexical perception

This project investigated how limited attention influences the perception of socially meaningful details in speech with a focus on American English ING. American English ING has two variants, a canonically standard velar variant -ing, as in cooking, and a non-standard apical variant -in’, as in cookin’. Talkers who more frequently use the apical variant are judged to be less educated, less articulate, less formal, and more likely to be Southern. In a matched guise task talkers produced apical variants, velar variants, or a mix of the two. Participants rated the talkers on intelligence, clarity, dynamism, friendliness, and gender. In a distracted condition, participants completed visual search tasks while listening to limit their ability to attend to the talkers’ speech. In a focused condition, participants listened with no concurrent task. The results revealed that apical talkers were rated as less cold and less articulate than velar talkers. Ratings for coldness, but not articulateness, were less affected by ING guise for distracted participants than focused participants. Additionally, ratings for articulateness and gender shifted between distracted and focused conditions independent of ING guise. These findings suggest that limited attention alters how listeners form judgments from indexical information in speech.

Breaking Your Boundaries: How TikTok Use Impacts Privacy Concerns Among Influencers

ABSTRACT Considering the unique nature of TikTok, where users are encouraged to share meaningful details of their lives, the present study is interested in the interplay between privacy concerns, TikTok use, and the creation of online identities amongst influencers based in the United States. The present study extends the framework of boundary regulation to understand how privacy concerns underlie information disclosures on TikTok. To accomplish this, interviews were conducted with 25 content creators with a minimum of 10,000 followers to understand the relationship between an individual’s desire to maintain a private life and a competing interest in developing a public persona. Results indicate competing desires to share private information in an effort to cultivate an online following coupled with concerns for protecting one’s loved ones who may not have consented to living life in the public eye.

Image Restoration using Nonlocal Regularized Variational Model with Spatially Adapted Regularization Parameter

Restoration of blurred images corrupted with impulse noise has attracted increasing attention due to its great potential toward practical applications. To guarantee the noisy image deblurring performance, total variation (TV), one of the most popular regularizers, has been extensively exploited in the literature. This local regularizer is known for its capability of preserving edges and penalizing oscillations but not sharp discontinuities. However, TV regularized variational models commonly suffer from undesirable staircase-like artifacts in homogeneous regions. The reason behind this phenomenon is that TV regularizer favors solutions that are piecewise constant in numerical experiments. To overcome the model-dependent deficiency, we proposed to replace the commonly used local regularizer by its nonlocal extension, i.e., nonlocal total variation (NLTV) regularizer. By taking advantage of the high degree of self-similarity within images, the proposed nonlocal variational model is able to remove both blurring and impulse noise while preserving meaningful image details. To guarantee solution stability and efficiency, the resulting nonsmooth image restoration problem was effectively dealt with using an alternating optimization algorithm. In addition, to further enhance the image quality, a local variance estimator-based automatic method was introduced to calculate the spatially adapted regularization parameters during image restoration. Extensive experiments have demonstrated that the proposed method could achieve superior imaging performance compared with several state-of-the-art image restoration methods.

Caricatures of Scientists: Problems of Source Analysis

This article analyzes different methodological approaches in the studies of caricatures of scientists. Satirical drawings are discussed as important sources for the history of science: they reveal little-explored episodes from the scientists’ life; shed some light on the scientists’ psychological, social, and professional characteristics; and influence public opinion even though more indirectly than written documents do. It is shown that their completeness and fairness vary significantly but all of these caricatures have a number of common characteristics such as hidden meaning, emphasis on various features of their subjects’ appearance or character traits, and abundance of meaningful details. Caricatures not only evoke smiles but also help understand whether scientific discoveries were accepted or opposed by society. The analysis of artistic devices used by the caricaturists allows to determine their ideological focus, emotional impact, and power of conviction. The article offers examples of how the caricatures in printed media played an important role in the emergence of myths about scientists and their discoveries. Therefore the historians of science need to decipher the images in the caricatures and elucidate the motives for creating them. Visual sources, presented and analyzed in this paper, were found to be unique in their message, artistic devices, theme, and emotional impact, and therefore were reviewed as complementary sources of information that demand additional resources for their deciphering.

Лингвокултурологична интерпретация на турцизмите в християнски религиозни текстове

One of the most important tendencies in the lexis of the Bulgarian language during the Ottoman period is the interference of foreign lexis and in particular the so-called turcisms or osmanisms. The article examines the connotative function of selected Turkish borrowings in the Synaxary Life of St. George the New of Sofia. One of its copies from the town of Elena, of the early 18th century, is a deliberate adaptation of an unknown writer who introduced Turkish terms for denoting specific realities in order to achieve a consciously sought authenticity and plausibility of the story, and to restore the emotional perception of George’s martyrdom two centuries after the event. Although moving away from the original, the Elena copy contributes to the deepening of the message of the literary work thus making it more accessible to the common reader through the use of meaningful details.

Measure of unevenness in human genomes, described as a self-affine phase transition in a 'spin-chain’ model

Background: Non-Gaussian distribution of polymorphic positions across a genome can substantially influence the results of any approach to molecular evolution based on a 'classical' probability model. The infinite dispersion of non-Gaussian perturbations is a challenge in an attempt to accept it in a probability-based model of evolution. Methods: Here a model is proposed where non-Gaussian distribution is introduced to an exact solution of the 'Ising model'; it describes a behavior of one-dimensional chain of spins in an approaching to a phase transition. The distribution of fragments which are identical between two genomes is similar to distribution of islands of spins with the same orientation, in the model where non-integer dimension is introduced. Results: Application of this model allows us to compare the relative contributions of non-Gaussian perturbations for pairs of human genomes from different ethnic groups. An evolution of the three human races in a most compact presentation is considered, rates of development on the separated stages of the evolution are assumed to be proportional to a value of relative unevenness between the appropriate groups of genomes. In the resolved model, the meaningful details of the separation between Asian and European races are clarified, in a period around ten thousand years ago; a particular viewpoint to the separation of the African race is also presented. Conclusion: The proposed approximation of non-Gaussian perturbations in human genomes allows to support the statements which are otherwise missed in the scientific investigations of the early history of modern humans.

Clarifying and Improving the Role of the Psychiatric Consultant

Clarifying and Improving the Role of the Psychiatric Consultant

Object‐Level Remote Sensing Image Augmentation Using U‐Net‐Based Generative Adversarial Networks

With the continuous development of deep learning in computer vision, semantic segmentation technology is constantly employed for processing remote sensing images. For instance, it is a key technology to automatically mark important objects such as ships or port land from port area remote sensing images. However, the existing supervised semantic segmentation model based on deep learning requires a large number of training samples. Otherwise, it will not be able to correctly learn the characteristics of the target objects, which results in the poor performance or even failure of semantic segmentation task. Since the target objects such as ships may move from time to time, it is nontrivial to collect enough samples to achieve satisfactory segmentation performance. And this severely hinders the performance improvement of most of existing augmentation methods. To tackle this problem, in this paper, we propose an object‐level remote sensing image augmentation approach based on leveraging the U‐Net‐based generative adversarial networks. Specifically, our proposed approach consists two components including the semantic tag image generator and the U‐Net GAN‐based translator. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. State‐of‐the‐art generative models, DCGAN, WGAN, and CycleGAN, are selected as baselines. According to the experimental results, our proposed approach significantly outperforms the baselines in terms of not only drawing the outlines of target objects but also capturing their meaningful details.

Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher's Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.

Perceptual-Aware Sketch Simplification Based on Integrated VGG Layers.

Deep learning has been recently demonstrated as an effective tool for raster-based sketch simplification. Nevertheless, it remains challenging to simplify extremely rough sketches. We found that a simplification network trained with a simple loss, such as pixel loss or discriminator loss, may fail to retain the semantically meaningful details when simplifying a very sketchy and complicated drawing. In this paper, we show that, with a well-designed multi-layer perceptual loss, we are able to obtain aesthetic and neat simplification results preserving semantically important global structures as well as fine details without blurriness and excessive emphasis on local structures. To do so, we design a multi-layer discriminator by fusing all VGG feature layers to differentiate sketches and clean lines. The weights used in layer fusing are automatically learned via an intelligent adjustment mechanism. Furthermore, to evaluate our method, we compare our method to state-of-the-art methods through multiple experiments, including visual comparison and intensive user study.

Geologically Meaningful 40Ar/39Ar Ages of Altered Biotite from a Polyphase Deformed Shear Zone Obtained by in Vacuo Step-Heating Method: A Case Study of the Waziyü Detachment Fault, Northeast China

Discordant biotite 40Ar/39Ar age spectra are commonly reported in the literature. These can be caused by a number of processes related to in vacuo heating, homogenization of the argon distribution, and production of misleadingly flat age spectra. Problematic samples are typically derived from metamorphic belts; thermal overprinting and chloritization are two of the main known causes of disturbed age spectra. Biotite and muscovite of the Waziyü detachment fault, Yiwulüshan metamorphic core complex, Jinzhou, China, yield highly variable 40Ar/39Ar data that hinder reconstruction of their deformation history. We combined mineralogical studies with detailed 40Ar/39Ar dating of biotite, phengitic white mica, and K-feldspar augen from this fault. We infer that argon within the biotite was modified by hydrothermal fluids during fault activity and associated epidotization, chloritization, and muscovitization such that bulk sample step-heating, single grain total fusion, and in situ laser ablation of biotite produced mixed 40Ar/39Ar ages. However, detailed step-heating of biotite shows that this mineral records the ages of cooling and later alteration based on data from a coexisting rigid feldspar porphyroblast and neo-crystallized phengite that record two periods of fault activity at ~120–113 and 18–12 Ma. Our data reveal that the discordant biotite 40Ar/39Ar age spectra might represent a mixed age and that only detailed step-heating methods can extract meaningful geological details of the deformation history of a fault. Therefore, the mineral and the method must be carefully considered if metamorphic or deformed samples are dated.

Facing Botticelli: three studies on the significant detail

These texts are the results of a seminar organised and coordinated by Marzia Faietti at Gallerie degli Uffizi (a.a. 2016-2017). During these lectures, particular attention was payed to meaningful details in some famous Botticelli’s paintings. Valentina Saetti’s text analyses Madonna of the Magnificat, Amalda Ciani Cuka’s St.Augustin in his study and Tiziana Borea’s the Calumny of Apelles.

Deep image reconstruction from human brain activity.

The mental contents of perception and imagery are thought to be encoded in hierarchical representations in the brain, but previous attempts to visualize perceptual contents have failed to capitalize on multiple levels of the hierarchy, leaving it challenging to reconstruct internal imagery. Recent work showed that visual cortical activity measured by functional magnetic resonance imaging (fMRI) can be decoded (translated) into the hierarchical features of a pre-trained deep neural network (DNN) for the same input image, providing a way to make use of the information from hierarchical visual features. Here, we present a novel image reconstruction method, in which the pixel values of an image are optimized to make its DNN features similar to those decoded from human brain activity at multiple layers. We found that our method was able to reliably produce reconstructions that resembled the viewed natural images. A natural image prior introduced by a deep generator neural network effectively rendered semantically meaningful details to the reconstructions. Human judgment of the reconstructions supported the effectiveness of combining multiple DNN layers to enhance the visual quality of generated images. While our model was solely trained with natural images, it successfully generalized to artificial shapes, indicating that our model was not simply matching to exemplars. The same analysis applied to mental imagery demonstrated rudimentary reconstructions of the subjective content. Our results suggest that our method can effectively combine hierarchical neural representations to reconstruct perceptual and subjective images, providing a new window into the internal contents of the brain.

Aplicación de la fotogrametría automatizada y de técnicas de iluminación con herramientas SIG para la visualización y el análisis de una piedra con relieves antropomorfos

<p><strong>Extended Abstract:</strong></p><p>We present a methodological approach for the representation, visualisation and analysis of three-dimensional (3D) models of meaningful details in stone reliefs provided by digital documentation tools and subsequent processing. For this aim, anthropomorphous shapes engraved on a flat stone slab found in Sierra de Fontcalent (Alicante) are studied. The object under consideration was located near two archaeological sites, Cova del Fum–a cave with presence of the Chalcolithic material (López, 2010)–and the archaeological site of Fontcalent, with remains from different phases of occupation spanning from 7th-6thBC to the 20thcentury (Ximénez, 2012).</p><p>In the last few years, the use of digital tools provided by new technologies and software development has left traditional work methodology behind (De Reu et al., 2014)while enabling the development of new approaches to both minimise heritage alteration and provide objective and accurate information (Lopez-Menchero, Marchante, Vincent, Cárdenas, & Onrubia, 2017). 3D documentation allows recording of cultural heritage at a reasonable cost with precision and quality through digital photography and SfM (Structure from Motion) photogrammetry with specialised software (De Reu et al., 2013).</p><p>In this project, recording and documentation with digital photography and automated photogrammetric techniques are applied to the Fontcalent stone slab for its digitisation and subsequent 3D representation. From the resulting model, a two-folded line of study is obtained. On the one hand, a Digital Elevation Model (DEM) is generated to study the microtopographies of the stone with geographic analysis techniques provided by Geographic Information Systems (GIS) from different lighting conditions and surface reflections, which are calculated by hillshading or LRM (Local Relief Model) for the interpretation of the object (Carrero-Pazos, Vilas, Romaní, & Rodríguez, 2014;Gawior, Rutkiewicz, Malik & Wistuba, 2017).On the other hand, from both the 3D model and the point cloud, the study is completed with the application of the methods of analysis and visualisation based on the Morphological Residue Model (MRM) which stands out every single detail of the surface morphology of the object (Caninas, Pires, Henriques, & Chambino, 2016;Correia, Pires, & Sousa, 2014). Further visualisations are based on Reflectance Transformation Imaging (RTI) which provides different shadows and reflections over the object from the application of a multidirectional illumination (Happa et al., 2010; Malzbender, Gelb, Wolters, & Zuckerman, 2000; Mudge et al., 2010).</p><p>The results thus obtained of the Fontcalent stone slab allow us to visualise several characteristic elements. The anthropomorphous figure awaking interest is also combined with the figure resulting from different visualisations applied with GIS techniques which may resemble a zoomorph. The use of visualisation techniques shown in this study has been fundamental in order to recognise the latter element. The composition reveals a zigzag line already appreciated before the study so that it is interesting to check if visualisations based on GIS techniques are able to highlight it though being shallow incisions. In our experience regarding this study, visualisation by using the hillshading technique shows a greater level of 3D detail than that provided by the application of the sky-view factor technique which offers a flattering view. However, the former technique may occasionally show shadows which hide other details, unlike the latter technique which plots the entire slab surface illuminated while differentiating the associated microtopography on the basis of its marks. The use of shaders in combination with hillshading and particularly combined with high pass filtering, contributes to improving the visualisation and accuracy of shadowed areas. As a result, we conclude that the results obtained in this work by lighting techniques with GIS add a greater level of detail in comparison to those provided by the mesh or the point cloud.</p><p>The study of the Fontcalent stone slab paves the way for two working hypotheses to be developed: on the one hand, its anthropological origin possibly related to the Chalcolithic, and on the other hand, its study as natural geological formations with ichnofossils.</p><p>The digitisation of cultural heritage with available 3D technologies should be a mandatory requirement when facing any study, analysis or intervention. With the current development of such techniques, we have verified their contribution to fundamental characteristics in the corresponding stages of visualisation and study. Thus, the proposed methodology is presented as an accurate and complete alternative for the study and analysis of the existing cultural heritage, and opens new ways for the revision, reinterpretation and revaluation of the previously evaluated heritage through traditional techniques.</p>

Towards precision medicine: from quantitative imaging to radiomics.

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.