Scale Selection Research Articles

A Novel Class-Specific Object-Based Method for Urban Change Detection Using High-Resolution Remote Sensing Imagery

A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data.

Concordance of the WHO, RTOG, and CTCAE v4.0 grading scales for the evaluation of oral mucositis associated with chemoradiation therapy for the treatment of oral and oropharyngeal cancers.

The ability to consistently and accurately assess oral mucositis (OM) is critical to descriptions of its incidence and severity and in evaluating the effectiveness of potential interventions. The lack of a single grading scale compounds outcome interpretation. Consequently, we assessed the concordance of three of the most commonly used OM grading criteria (World Health Organization (WHO), Radiation Therapy Oncology Group (RTOG), and the common terminology criteria for adverse events (CTCAE). Data was evaluated from two hundred patients with oropharyngeal or oral cavity cancers who underwent chemoradiation therapy and were enrolled in a double-blind, randomized, placebo-controlled trial in which trained assessors evaluated patients twice weekly. WHO, RTOG, and CTCAE scores were assigned centrally by independent evaluators blinded to the study group. Concordance among the three scales for all OM scores and severe OM scores (score ≥ 3) was defined as the percentage agreement and measured using Cohen's weighted Kappa. Of 3,578 OM assessments, 57% had identical scores for all three scales. When any score was considered, the concordance between WHO and RTOG scales was 71% (kappa 0.58; 95%CI: 0.56-0.60), 62% for the WHO and CTCAE scales (kappa 0.46; 95%CI: 0.44-0.48) and 78% for the CTCAE and RTOG scales (kappa 0.69; 95%CI: 0.68-0.71). When patients had severe OM (WHO score ≥ 3), 99.6% (521/523) of the CTCAE OM assessments had scores of 3 or 4 (kappa 0.98; 95%CI: 0.98-0.999) and 97.7% of the RTOG ones (511/523) had scores of 3 or 4 (kappa 0.69; 95%CI: 0.62-0.75). Among patients who had a WHO score of 4, 31.7% (63/199) and 96.0% (196/199) of patients had RTOG or CTCAE scores of 2 or 3, respectively. Discordance was seen with patients who exhibited mild to moderate OM or most severe OM (grade 4) as described by WHO criteria. Whereas scale selection seems less critical in studies in which general "severe mucositis" is the primary outcome, it is particularly important in accurately describing OM's clinical trajectory and the frequency and impact in its most severe forms.

Multi-granulation-based optimal scale selection in multi-scale information systems

The notions of multi-granulation and multi-scale are two important issues for the granular computing, both of which can describe the granular structure in certain ways. In this paper, we investigate the belief structure and the plausibility structure by defining belief and plausibility functions from the multi-granulation viewpoint, and discuss how to construct multi-granulation rough set (MGRS) models in multi-scale information systems (MSISs). Based on the MGRS in MSISs, the optimal scale selection methods with various requirements are studied in two aspects of optimistic and pessimistic multi-granulation for a multi-scale decision information system (MSDIS). To interpret and understand the proposed theories, some important properties of optimistic and pessimistic multi-granulation optimal scale selection are analyzed for the MSDIS, which could indicate the inner relationships among the different selection methods of the optimal scales. Furthermore, an example is provided to illustrate and verify these investigated properties.

Enhancing validity, reliability and participation in self-reported health outcome measurement for children and young people: a systematic review of recall period, response scale format, and administration modality

IntroductionSelf-report is the gold standard for measuring children’s health-related outcomes. Design of such measures is complex and challenging. This review aims to systematically appraise the evidence on recall period, response scale format, mode of administration and approaches needed to enable children and young people < 19 years to participate in valid and reliable self-reporting of their health outcomes.MethodPsycInfo, Medline, CINAHL and Embase were searched from 1 January 1990 to 15 March 2020, and citation searching undertaken in Scopus. Articles were included if they were primary research or case reports of ≥ 3 participants reporting the following: recall period, response scale selection, administration modality. Quality was assessed using QualSyst, and results synthesised narratively. This review was conducted and reported according to PRISMA guidelines.Results81 of 13,215 retrieved articles met the inclusion criteria. Children < 5 years old cannot validly and reliably self-report health outcomes. Face scales demonstrate better psychometric properties than visual analogue or Likert scales. Computerised and paper scales generally show equivalent construct validity. Children prefer computerised measures. Children ≤ 7 years old think dichotomously so need two response options. Those > 8 years old can reliably use a 3-point scale.ConclusionThe results of this review have both clinical and research implications. They can be used to inform appropriate choice of PROM for use with CYP in the clinical setting. We also give eight recommendations for future development of self-reported outcome measures for children and young people.

LSV-ANet: Deep Learning on Local Structure Visualization for Feature Matching

Feature matching is a fundamental and important task in many applications of remote sensing and photogrammetry. Remote sensing images often involve complex spatial relationships due to the ground relief variations and imaging viewpoint changes. Therefore, using a pre-defined geometrical model will probably lead to inferior matching accuracy. In order to find good correspondences, we propose a simple yet efficient deep learning network, which we term the “local structure visualization-attention” network (LSV-ANet). Our main aim is to transform outlier detection into a dynamic visual similarity evaluation. Specifically, we first map the local spatial distribution into a regular grid as descriptor LSV, and then customized a spatial SCale Attention (SCA) module and a spatial STructure Attention (STA) module, which explicitly allows structure manipulation and scale selection of LSV within the network. Finally, the embedded SCA and STA deduce optimal LSV for solving feature matching task by training the LSV-ANet end-to-end. In order to demonstrate the robustness and universality of our LSV-ANet, extensive experiments on various real image pairs for general feature matching are conducted and compared against eight state-of-the-art methods. The experiment results demonstrate the superiority of our method over state of the art.

Depression or resilience? A participatory study to identify an appropriate assessment tool with Kanien'kéha (Mohawk) and Inuit in Quebec.

We present a study on selection of a psychometric scale to be clinically used among Indigenous people with depression. Our aim was to select a psychometric tool for cultural adaptation with Mohawk and Inuit in Quebec. We selected three depression scales and three protective factor scales based on: strong validity for psychometric properties, evidence for good psychometric qualities across translations, avoidance of cognitively complex sentences, brevity, and clarity. We submitted the scales for consultation, and followed qualitative participatory methods with Mohawks of Kahnawake and Inuit from Nunavik living in an urban environment. We collected data through ten focus groups with advisory committees, and carried out a thematic analysis of the information. The advisory groups considered the measurement scales to be unsafe. The major components that hindered their acceptance were: numeric rating, self-evaluation (versus supportive interaction), and a focus on symptoms rather than supportive factors. The participants preferred the Growth and Empowerment Measure due to its empowering approach. They voiced that it is necessary to develop a culturally sensitive and safe tool which facilitates interactions between the person and the practitioner. This project provides valuable information about the perspectives of local Indigenous peoples regarding mental health and factors of empowerment and resilience. The ideal tool should be flexible in terms of the content and its use as compared to the conventional psychometric strategies. A tool developed with the Indigenous perspective on wellbeing could be used in psychological and psychiatric intervention as well as in social and community services.

On selection of optimal cuts in complete multi-scale decision tables

In this paper, a novel optimal scale selection method in complete multi-scale decision tables has been proposed. Unlike the existing approaches in the literature, we employ the tools of granularity trees and cuts for each attribute. Each granularity tree has many different local cuts, which represent various scale selection methods under a specific attribute. Different local cuts collectively forms a global cut of a multi-scale information table, which in turn induces an information table with a mixed scale. One distinct feature of such tables is that the attribute values of different objects may be obtained at different scales for the same attribute. By keeping maximal consistency of the derived mixed-scale decision table, we introduce the notions of optimal cuts in multi-scale decision tables. Then, a comparative study between different types of optimal scale selection methods is performed. Finally, an algorithm is designed to verify the validity of the proposed approach.

Multivariate color scale image analysis – Thin layer chromatography for comprehensive evaluation of complex samples fingerprint

A chemometric evaluation of the information provided by different color scale fingerprints in thin layer chromatographic analysis of complex samples is proposed for the correct classification of a set of medicinal plant extracts. The fingerprints of the samples were acquired on HPTLC Silica gel 60 F254 and HPTLC Silica gel 60 plates using multiple levels of visualization under UV light. Images processing on red (R), green (G), blue (B) and respectively grey (K) color scale selection was used in order to evaluate the complete chromatographic profile of the extracts. Combination of Principal Component Analysis (PCA) and Factor Analysis (FA) method was applied in order to reveal the individual contribution of each color scales in the analysis of chromatographic fingerprints. The suggested technique provides an applicable strategy to screen for efficacy-associated color scale for grouping/classification of the extracts exploiting the information provided by HPTLC fingerprints. The principal component analysis and linear discriminant analysis (PCA-LDA) method was applied for the evaluation of numerical data provided by color scale fingerprints digitization and for samples classification. A correct classification of the analyzed extracts according to the plants phylum was revealed by color scale fingerprints analysis. The proposed methodology could be considered as a promising tool with future applications in plant material investigations even from the taxonomic perspective classification.

Scale consistency for investigating urbanization level, vegetation coverage, and their correlation

Accurate quantification of the relationship between urbanization level and vegetation coverage can provide scientific support for urban sustainable development. However, the interaction between them across multi-scales is under-researched because of its complexity. Based on climate change (i.e. precipitation, temperature and solar radiation), partial correlation relationship between urbanization level (nighttime light, NTL) and vegetation coverage (leaf area index, LAI) at different scales (1–20 km) in the Pearl River Delta (PRD) was explored. Mean local variance was used to determine the appropriate scale of the relationship, at which these metrics could have the minimum information missing. The consistency in appropriate analysis scales of NTL, LAI, and NTL-LAI, was quantified to identify which factor more influenced the appropriate analysis scale of NTL-LAI correlation. The results showed from 2000 to 2012, the urbanization level increased at the rate of 0.58/yr and the average LAI increased slowly at the rate of 0.02 m2/m2/yr. The partial correlation between NTL and LAI depicted that urbanization promoted vegetation coverage in the PRD. Meanwhile, the appropriate analysis scale of NTL-LAI correlation was more determined by the analysis scale of urbanization level, than that of vegetation coverage. This study revealed the scale effect of the relationship between urbanization level and vegetation coverage, and identified the determine factor of appropriate analysis scale of the relationship, which could help for the scale selection in related studies.

Cross-Scale Residual Network: A General Framework for Image Super-Resolution, Denoising, and Deblocking.

In general, image restoration involves mapping from low-quality images to their high-quality counterparts. Such optimal mapping is usually nonlinear and learnable by machine learning. Recently, deep convolutional neural networks have proven promising for such learning processing. It is desirable for an image processing network to support well with three vital tasks, namely: 1) super-resolution; 2) denoising; and 3) deblocking. It is commonly recognized that these tasks have strong correlations, which enable us to design a general framework to support all tasks. In particular, the selection of feature scales is known to significantly impact the performance on these tasks. To this end, we propose the cross-scale residual network to exploit scale-related features among the three tasks. The proposed network can extract spatial features across different scales and establish cross-temporal feature reusage, so as to handle different tasks in a general framework. Our experiments show that the proposed approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations for multiple image restoration tasks.

Global research on cognitive behavioural therapy for schizophrenia from 2000 to 2019: a bibliometric analysis via CiteSpace

BackgroundCognitive behavioural therapy (CBT) is considered to be an effective treatment for schizophrenia. In recent years, researchers have published clinical research results on CBT for schizophrenia. However, there is currently...

Optimal segmentation scale selection and evaluation of cultivated land objects based on high-resolution remote sensing images with spectral and texture features.

As the remote sensing technology develops, there are increasingly more kinds of remote sensing images available from different sensors. High-resolution remote sensing images are widely used in the detection of land cover/land change due to their plenty of characteristics of a specific feature in terms of spectrum, shape, and texture. Current studies regarding cultivated land resources that are the material basis for the human beings to survive and develop focus on the method to accurately obtain the quantity of cultivated land in a region and understand the conditions and the trend of change of the cultivated land. Pixel-based method and object-oriented method are the main methods to extract cultivated land in remote sensing field. Pixel-based method ignores high-level image information, while object-oriented method takes the image spot after image segmentation as the basic unit of information extraction, which can make full use of spectral features, spatial features, semantic features, and contextual features. Image segmentation is a key step of object-oriented method; the core problem is how to obtain the optimal segmentation scale. Traditional methods for determining the optimal segmentation scale of features (such as the homogeneity-heterogeneity method, the maximum area method, and the mean variance method), in which only the spectral and geometrical characteristics are considered, while the textural characteristics are neglected. Based on this, the Quickbird and unmanned aerial vehicle (UAV) images obtained in Xiyu Village, Pengzhou City, Sichuan Province, China, were selected as experimental objects, and the texture mean and spectral grayscale mean method (MANC method based on GLCM), which comprehensively considered the spectrum, shape, and texture features, was proposed to calculate the optimal segmentation scale of cultivated land in the study area. The error segment index (ESI) and centroids distance index (CDI) were adopted to evaluate image segmentation quality based on the method of area and position differences. The experimental results show that the MANC method based on GLCM can obtain higher segmentation precision than the traditional methods, and the segmentation results are in good agreement with the cultivated land boundary obtained by visual interpretation.

Viscous and hyperviscous filtering for direct and large-eddy simulation

This work is dedicated to the solution filtering technique for performing direct and large-eddy simulation. It is shown that this approach is equivalent to the use of spectral viscosity as a possible ersatz of subgrid-scale modelling. In the framework of finite-difference schemes, the filter operator can be designed to ensure time consistency while easily controlling the level and scale selectivity of the dissipation thus introduced. Then, a new family of filter schemes is developed in order to represent both the molecular and artificial dissipations. The resulting viscous filter operator is straightforward to implement through a simple modification of its coefficients that depend on the molecular/artificial viscosity and the time step. A definitive advantage in terms of computational efficiency is obtained for computational configurations where the time step is restricted by the Fourier condition, as a simple alternative to implicit time integration of the viscous term. Numerical tests clearly show that this viscous filtering method is flexible, accurate and numerically stable at large Fourier number.

A utility of pores as level 3 features in latent fingerprint identification

Latent fingerprint identification is the most prevalent process used by the forensic community from a long time. Smudgy, blurred, and small fingerprint area of the latent impressions results into the deficiency of the level 2 features i.e. minutiae. Most of the commercially available Automated Fingerprint Identification Systems (AFISs) which mainly dependent on minutiae; seems less efficient in latent fingerprint matching. In this paper, the usefulness of pores (level 3 features) besides minutiae in latent fingerprint matching is examined. An algorithm based on Lindeberg’s automatic scale selection method is proposed for pores extraction in latent fingerprints. The fusion of pores and minutiae at score level is used to re-rank the minutiae based latent matcher. The effectiveness of the proposed algorithm and pores utility are evaluated by observing and comparing the latent recognition accuracy obtained for minutiae matching and matching after fusion. Both minutiae and pores are automatically extracted in latent and reference fingerprints. The experimental results show that the fusion significantly improves the latent recognition rate in comparison to the minutiae matching.

Adaptive Morphological Analysis Method and Its Application for Bearing Fault Diagnosis

The vibration response of faulty bearing is always characterized by periodic transient impulses in the signal. Generally, these fault-related features are inevitably submerged in noise and harmonic components. Mathematical morphology is an excellent method of noise reduction, which can retain the detail information of impulses in the time domain. However, the filtering effect of traditional morphological operator (MO) might be easily affected by random impulses, and the proper selection of the structure element (SE) depends heavily on the experience of researchers. In order to effectively remove these interferences and extract the fault features accurately, an improved method, named adaptive morphological filter (AMF), is proposed in this article. This method utilizes autocorrelation to lift MO in time domain to enhance periodic components, and the scale of SE can, therefore, be calculated with the local maximum of the autocorrelation spectrum. Since the selection of the optimal SE scale is adaptive, researchers’ experience is no longer needed, and there is also no need to calculate the fault characteristic frequency (FCF) for the determination of maximum scale of SE. The vibration and acoustical signals of faulty locomotive wheel set bearing are analyzed with this method, and the results verify its effectiveness and ability.

Development of the Experienced Communication in Dementia Questionnaire: A Qualitative Study.

Communication problems with their caregivers are common in people with dementia. Although interventions for improvement of communication are being developed, a tool to measure how participants experience their communication is lacking. The objective of this article is to describe the development of a questionnaire that measures the “experienced communication” of persons with dementia (ECD-P) as well as of their caregivers (ECD-C). Interviews were conducted with five person with dementia—caregiver dyads who had recently received a new communication intervention. Reflexive thematic analysis was performed on the transcripts using ATLAS.ti. Codes were created, categories and themes were identified, and items for the questionnaires were generated. Selection of items and response scales was done in collaboration with the same dyads. The final version was established after pilot testing with seven other dyads and discussion with five experts in the field of dementia care. Analysis of the transcripts resulted in 212 codes and 17 categories within four themes: caregiver competence, social communication, communication difficulties in daily life, and experienced emotions during conversations. The final version of the ECD-P consists of part 1 with 22 items and 4-point Likert scales, and part 2 with two items and 1 to 10 scales. In the final ECD-C (proxy version), part 1 and part 2 are similar to the ECD-P, while a part 3 was added to assess caregivers’ own perspective and emotions (five items). Based on the experiences of people with dementia and their caregivers, we constructed a face-valid questionnaire. This justifies future research to test its clinimetric characteristics.

Comprehensive Optimal Scheduling Strategy of Multi-Element Charging Station for Bounded Rational Users

While providing charging service for electric vehicles, charging stations are also aggregation centers which supply adjustment capabilities to the power grid. It is necessary for charging stations to take care of all parties' interests with respect to users' independent choice. This article focuses on multi-element charging stations which contain conventional load, energy storage and distributed renewable energy generation besides charging piles. Firstly, charging price mechanism that improves participation motivation of users in providing charging flexibility were designed. Next, rational model based on expected utility and bounded rational model based on prospect theory were established respectively. Model predictive control(MPC) has been selected to handle uncertainties caused by users' behavior and output of distributed renewable energy generation. Both operating economy and adjustment flexibility of charging stations have been taken into account in proposed comprehensive scheduling strategy. Based on the setting of dummy variables, double-layer optimization has been converted into single-layer optimization which can reduce the calculation time significantly, making the strategy suitable for online application. Advantage of the bounded rational model in predicting users' behavior was verified by experiments towards real people. The selection of time scale for MPC was discussed through simulation experiments in different scenarios. In the simulation experiment, the proposed scheduling model integrating social-physical methods has shown good performance in power grid requirement following and economic optimization.

Texture feature extraction and optimization of facial expression based on weakly supervised clustering

In order to improve the recognition rate of weak annotation data in facial expression recognition task, this paper proposes a multi-scale and multi-region vector triangle texture feature extraction scheme based on weakly supervised clustering algorithm. According to the information gain rate of extracted features, combined with threshold selection and random dropout strategy, the best selection of vector triangle texture feature scale is explored, and the feature space is optimized under the premise of sufficient feature space information, the reduction of feature space is realized and the information redundancy is reduced. For the positive and negative expression units, the facial expression images in the data set are divided into two categories. The positive and negative facial expressions are taken to form the same kind of samples, the positive and negative facial expressions are taken to form the positive and negative samples, and the annotation labels are taken to form the weak annotation labels. The experimental results show that the best recognition rate of the proposed scheme is 84.1%, which is 5.8% higher than the unoptimized texture feature scheme.

Robust data assimilation with noise: Applications to cardiac dynamics.

Reconstructions of excitation patterns in cardiac tissue must contend with uncertainties due to model error, observation error, and hidden state variables. The accuracy of these state reconstructions may be improved by efforts to account for each of these sources of uncertainty, in particular, through the incorporation of uncertainty in model specification and model dynamics. To this end, we introduce stochastic modeling methods in the context of ensemble-based data assimilation and state reconstruction for cardiac dynamics in one- and three-dimensional cardiac systems. We propose two classes of methods, one following the canonical stochastic differential equation formalism, and another perturbing the ensemble evolution in the parameter space of the model, which are further characterized according to the details of the models used in the ensemble. The stochastic methods are applied to a simple model of cardiac dynamics with fast-slow time-scale separation, which permits tuning the form of effective stochastic assimilation schemes based on a similar separation of dynamical time scales. We find that the selection of slow or fast time scales in the formulation of stochastic forcing terms can be understood analogously to existing ensemble inflation techniques for accounting for finite-size effects in ensemble Kalman filter methods; however, like existing inflation methods, care must be taken in choosing relevant parameters to avoid over-driving the data assimilation process. In particular, we find that a combination of stochastic processes-analogously to the combination of additive and multiplicative inflation methods-yields improvements to the assimilation error and ensemble spread over these classical methods.

Eady edge waves, frontal wave-trains and type-B cyclogenesis

Eady edge waves (EEWs) propagate on surface baroclinic zones and they resemble synoptic and sub-synoptic scale frontal wave-trains. The wave-trains are frequently depicted on surface charts, and occur per se on a front with a local baroclinicity extremum. The possible linkage of EEWs to frontal waves is explored within the framework of so-called ‘surface quasi-geostrophic dynamics’ by establishing the properties of free and forced EEWs propagating on an ambient flow comprising a laterally confined baroclinic zone. It is shown that the dispersion properties of the free EEWs modes are influenced significantly both by the zone’s baroclinicity extremum and by key features that are akin to those of cold and warm fronts. In particular the larger-scale waves are nondispersive and a local wave-packet can retain coherency, the leading modes on a pseudo-cold front possess a phase speed related to the baroclinicity extremum, whereas waves on a pseudo-warm front propagate in the reverse direction. Also theoretical considerations demonstrate that forced modes can undergo resonant secular growth. It is further shown that transient or sustained forcing of a front by an upper-tropospheric potential vorticity anomaly (sic. short-wave trough) can serve to instigate features akin to frontal wave-trains and type-B cyclogenesis. More generally the study hints at the significance for NWP of a front’s structure and its dispersion properties, and that an element of predictability is introduced by the latter features due to dynamical scale selection.