Appropriate Level Of Granularity Research Articles

A hierarchical atlas of the human cerebellum for functional precision mapping

The human cerebellum is activated by a wide variety of cognitive and motor tasks. Previous functional atlases have relied on single task-based or resting-state fMRI datasets. Here, we present a functional atlas that integrates information from seven large-scale datasets, outperforming existing group atlases. The atlas has three further advantages. First, the atlas allows for precision mapping in individuals: the integration of the probabilistic group atlas with an individual localizer scan results in a marked improvement in prediction of individual boundaries. Second, we provide both asymmetric and symmetric versions of the atlas. The symmetric version, which is obtained by constraining the boundaries to be the same across hemispheres, is especially useful in studying functional lateralization. Finally, the regions are hierarchically organized across three levels, allowing analyses at the appropriate level of granularity. Overall, the present atlas is an important resource for the study of the interdigitated functional organization of the human cerebellum in health and disease.

Bringing architecture-based adaption to the mainstream

Software architecture has been shown to provide an appropriate level of granularity for representation of a managed software system and reasoning about the impact of adaptation choices on its properties. Software architecture-based adaptability is the ability to adapt a software system in terms of its architectural elements, such as its components and their interfaces. Despite its promise, architecture-based adaptation has remained largely elusive, mainly because it involves heavy engineering effort of making non-trivial changes to the manner in which a software system is implemented. In this paper, we present Acadia—a framework that automatically enables architecture-based adaptation of practically any Java 9+ application without requiring any changes to the implementation of the application itself. Acadia builds on the Java Platform Module System (JPMS), which has brought extensive support for architecture-based development to Java 9 and subsequent versions. Acadia extends JPMS with the ability to provide and maintain a representation of an application’s architecture and make changes to it at runtime. The results of our experimental evaluation, conducted on three large open-source Java applications, indicate that Acadia is able to efficiently apply dynamic changes to the architecture of these applications without requiring any changes to their implementation.

A multi-layered integrative analysis reveals a cholesterol metabolic program in outer radial glia with implications for human brain evolution.

The definition of molecular and cellular mechanisms contributing to brain ontogenetic trajectories is essential to investigate the evolution of our species. Yet their functional dissection at an appropriate level of granularity remains challenging. Capitalizing on recent efforts that have extensively profiled neural stem cells from the developing human cortex, we develop an integrative computational framework to perform trajectory inference and gene regulatory network reconstruction, (pseudo)time-informed non-negative matrix factorization for learning the dynamics of gene expression programs, and paleogenomic analysis for a higher-resolution mapping of derived regulatory variants in our species in comparison with our closest relatives. We provide evidence for cell type-specific regulation of gene expression programs during indirect neurogenesis. In particular, our analysis uncovers a key role for a cholesterol program in outer radial glia, regulated by zinc-finger transcription factor KLF6. A cartography of the regulatory landscape impacted by Homo sapiens-derived variants reveals signals of selection clustering around regulatory regions associated with GLI3, a well-known regulator of radial glial cell cycle, and impacting KLF6 regulation. Our study contributes to the evidence of significant changes in metabolic pathways in recent human brain evolution.

A hierarchical atlas of the human cerebellum for functional precision mapping.

The human cerebellum is activated by a wide variety of cognitive and motor tasks. Previous functional atlases have relied on single task-based or resting-state fMRI datasets. Here, we present a functional atlas that integrates information from 7 large-scale datasets, outperforming existing group atlasses. The new atlas has three further advantages: First, the atlas allows for precision mapping in individuals: The integration of the probabilistic group atlas with an individual localizer scan results in a marked improvement in prediction of individual boundaries. Second, we provide both asymmetric and symmetric versions of the atlas. The symmetric version, which is obtained by constraining the boundaries to be the same across hemispheres, is especially useful in studying functional lateralization. Finally, the regions are hierarchically organized across 3 levels, allowing analyses at the appropriate level of granularity. Overall, the new atlas is an important resource for the study of the interdigitated functional organization of the human cerebellum in health and disease.

Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the interclass overlaps in a natural way and better capture the underlying multimodel structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">if</small> – <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">then</small> fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule-based construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems.

Traditional versus Microsphere Embolization for Hepatocellular Carcinoma: An Effectiveness Evaluation Using Data Mining.

Background: For hepatocellular carcinoma (“HCC”), the current standard of treatment is hepatic artery embolization, generally through trans-catheter arterial chemoembolization (“TACE”). There are two types: traditional (“conventional” or “cTACE”) and microsphere (“DC bead TACE”). Unfortunately, the literature comparing the relative effectiveness of cTACE versus DC bead TACE is inconclusive, partially due to the complexity of HCC and its response to treatment. Data mining is an excellent method to extract meaning from complex data sets. Purpose: Through the application of data mining techniques, to compare the relative effectiveness of cTACE and DC bead TACE using a large patient database and to use said comparison to establish usable guidelines for developing treatment plans for HCC patients. Materials and Methods: The data of 372 HCC patients who underwent TACE in Taichung Veterans General Hospital were analyzed. The chi-square test was used to compare the difference in the effectiveness of the two therapies was compared. Logistic regression was used to calculate the odds ratios. Furthermore, using the C4.5 decision tree, the two therapies were classified into applicable fields. Chi-square test, the t-test, and logistic regression were used to verify the classification results. Results: In Barcelona Clinic Stages A and B cancers, cTACE was found to be 22.7% more effective than DC bead TACE. By using the decision tree C4.5 as a classifier, the effectiveness of either treatment for small tumors was 8.475 times than that for large tumors. DC bead TACE was 3.39 times more successful in treating patients with a single tumor than with multiple tumors. For patients with a single tumor, the chi-square test showed that 100–300 μm microspheres were significantly more effective than 300–500 μm. While these findings provide a reference for the selection of an appropriate TACE approach, we noted that overall accuracy was somewhat low, possibly due to the limited population. Conclusions: We found that data mining could be applied to develop clear guidelines for physician and researcher use in the case of complex pathologies such as HCC. However, some of our results contradicted those elsewhere in the literature, possibly due to a relatively small sample size. Significantly larger data sets with appropriate levels of granularity could produce more accurate results.

Critical evaluation of the customisation process of the UNDRR disaster resilience scorecard for cities to earthquake-induced soil liquefaction disaster events

As cities become larger and more densely populated the impacts of major earthquake events on city communities become more severe. Improving community resilience to earthquake events relies on the complex relationships that exist between different community stakeholder groups (citizens, businesses, community groups, emergency services, critical infrastructure providers, politicians etc.). This paper reports results from a major EU funded study (LIQUEFACT) that developed a tool for assessing community resilience to Earthquake Induced Liquefaction Disaster (EILD) events. The tool is based on a customised version of the UNDRR Disaster Resilience Scorecard for Cities. The paper reviews alternative approaches to measuring community resilience and describes the process used in the LIQUEFACT project to develop and validate the customised scorecard. The paper presents the results of a questionnaire survey to identify the best generic approach to measure community resilience and a series of semi-structured group interviews to define a range of specific metrics for assessing community resilience to EILD events; and the results of a validation workshop to assess the effectiveness and usability of the customised scorecard. The paper concludes that it is possible to develop a customised version of the UNDRR Scorecard at an appropriate level of granularity to support improved community resilience to earthquake induced soil liquefaction disaster events. The paper also presents key lessons that could assist those developing similar customised versions of the UNDRR scorecard for use in different geographical settings or against different disaster scenarios.

Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data

Alzheimer’s disease (AD) is a severe neurodegenerative disease. The identification of patients at high risk of conversion from mild cognitive impairment to AD via earlier close monitoring, targeted investigations, and appropriate management is crucial. Recently, several machine learning (ML) algorithms have been used for AD progression detection. Most of these studies only utilized neuroimaging data from baseline visits. However, AD is a complex chronic disease, and usually, a medical expert will analyze the patient’s whole history when making a progression diagnosis. Furthermore, neuroimaging data are always either limited or not available, especially in developing countries, due to their cost. In this paper, we compare the performance of five widely used ML algorithms, namely, the support vector machine, random forest, k-nearest neighbor, logistic regression, and decision tree to predict AD progression with a prediction horizon of 2.5 years. We use 1029 subjects from the Alzheimer’s disease neuroimaging initiative (ADNI) database. In contrast to previous literature, our models are optimized using a collection of cost-effective time-series features including patient’s comorbidities, cognitive scores, medication history, and demographics. Medication and comorbidity text data are semantically prepared. Drug terms are collected and cleaned before encoding using the therapeutic chemical classification (ATC) ontology, and then semantically aggregated to the appropriate level of granularity using ATC to ensure a less sparse dataset. Our experiments assert that the early fusion of comorbidity and medication features with other features reveals significant predictive power with all models. The random forest model achieves the most accurate performance compared to other models. This study is the first of its kind to investigate the role of such multimodal time-series data on AD prediction.

Event abstraction in process mining: literature review and taxonomy

The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., the focus of process mining, yields a detailed understanding of the process, e.g., we are able to discover the control flow of the process and detect compliance and performance issues. Most process mining techniques assume that the event data are of the same and/or appropriate level of granularity. However, in practice, the data are extracted from different systems, e.g., systems for customer relationship management, Enterprise Resource Planning, etc., record the events at different granularity levels. Hence, pre-processing techniques that allow us to abstract event data into the right level of granularity are vital for the successful application of process mining. In this paper, we present a literature study, in which we assess the state-of-the-art in the application of such event abstraction techniques in the field of process mining. The survey is accompanied by a taxonomy of the existing approaches, which we exploit to highlight interesting novel directions.

Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder.

Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construction of predictive models, such as deep learning models, from fMRI requires addressing key choices about the model's architecture, including the number of layers and number of neurons per layer. Meanwhile, deriving functional connectivity (FC) features from fMRI requires choosing an atlas with an appropriate level of granularity. Once an accurate diagnostic model has been built, it is vital to determine which features are predictive of ASD and if similar features are learned across atlas granularity levels. Identifying new important features extends our understanding of the biological underpinnings of ASD, while identifying features that corroborate past findings and extend across atlas levels instills model confidence. To identify aptly suited architectural configurations, probability distributions of the configurations of high versus low performing models are compared. To determine the effect of atlas granularity, connectivity features are derived from atlases with 3 levels of granularity and important features are ranked with permutation feature importance. Results show the highest performing models use between 2-4 hidden layers and 16-64 neurons per layer, granularity dependent. Connectivity features identified as important across all 3 atlas granularity levels include FC to the supplementary motor gyrus and language association cortex, regions whose abnormal development are associated with deficits in social and sensory processing common in ASD. Importantly, the cerebellum, often not included in functional analyses, is also identified as a region whose abnormal connectivity is highly predictive of ASD. Results of this study identify important regions to include in future studies of ASD, help assist in the selection of network architectures, and help identify appropriate levels of granularity to facilitate the development of accurate diagnostic models of ASD.

Using Clustered Heat Maps in Mineral Exploration to Visualize Volcanic-Hosted Massive Sulfide Alteration and Mineralization

This study proposes an extension of a visualization approach common in biochemistry (the clustered heat maps—CHMs) to geochemical data with the main objective of detecting hydrothermal alteration and mineralization. The approach allows superior visualization of unsupervised cluster analysis results. We consider two examples: a synthetic case study and an application to public data derived from the Canadian Flin Flon volcanic-hosted massive sulfide deposits. A series of experiments were run on a synthetic dataset with the aim of understanding the effect of noise and how random data sampling of variable specimen population size influences results of a variety of clustering algorithms (including K-means and other hierarchical methods) and their visualization using CHMs. These experiments on synthetic data provided the basis to propose a possible workflow for the selection of optimal classifiers to be applied on natural data and the definition of an appropriate parametrization (distance metrics and clustering algorithm). Natural data analysis provides direct evidence of how CHMs can be a fruitful approach in mineral exploration if compared to other cluster analysis methods (e.g., classic K-means or hierarchical methods), CHMs provide the opportunity of examining an additional dimension of clustering and still view chemical compositions (although in a transformed space) in a single plot. Facilitated selection of appropriate levels of granularity (G), which regulates the scale of clustering in a CHM, was found to be an instrumental tool and led to the successful separation of clusters representative of major lithological transitions vs. smaller clusters, at higher granularity, isolating VHMS alteration and mineralization. Integration of statistical tests conducted on synthetic data, together with CHM’s visualization of the classification results led us to consider the Manhattan–Ward classifier as an optimal pair for the Flin Flon dataset, despite its limitations induced by the ‘uniform effect.’

Formal analysis of XACML policies using SMT

The eXtensible Access Control Markup Language (XACML) has attracted significant attention from both industry and academia, and has become the de facto standard for the specification of access control policies. However, its XML-based verbose syntax and rich set of constructs make the authoring of XACML policies difficult and error-prone. Several automated tools have been proposed to analyze XACML policies before their actual deployment. However, most of the existing tools either cannot efficiently reason about non-Boolean attributes, which often appear in XACML policies, or restrict the analysis to a small set of properties. This work presents a policy analysis framework for the verification of XACML policies based on SAT modulo theories (SMT). We show how XACML policies can be encoded into SMT formulas, along with a query language able to express a variety of well-known security properties, for policy analysis. By being able to reason over non-Boolean attributes, our SMT-based policy analysis framework allows a fine-grained policy analysis while relieving policy authors of the burden of defining an appropriate level of granularity of the analysis. An evaluation of the framework shows that it is computationally efficient and requires less memory compared to existing approaches.

Configuring use case models in product families

In many domains such as automotive and avionics, the size and complexity of software systems is quickly increasing. At the same time, many stakeholders tend to be involved in the development of such systems, which typically must also be configured for multiple customers with varying needs. Product Line Engineering (PLE) is therefore an inevitable practice for such systems. Furthermore, because in many areas requirements must be explicit and traceability to them is required by standards, use cases and domain models are common practice for requirements elicitation and analysis. In this paper, based on the above observations, we aim at supporting PLE in the context of use case-centric development. Therefore, we propose, apply, and assess a use case-driven configuration approach which interactively receives configuration decisions from the analysts to generate product-specific (PS) use case and domain models. Our approach provides the following: (1) a use case-centric product line modeling method (PUM), (2) automated, interactive configuration support based on PUM, and (3) an automatic generation of PS use case and domain models from Product Line (PL) models and configuration decisions. The approach is supported by a tool relying on Natural Language Processing (NLP) and integrated with an industrial requirements management tool, i.e., IBM DOORS. We successfully applied and evaluated our approach to an industrial case study in the automotive domain, thus showing evidence that the approach is practical and beneficial to capture variability at the appropriate level of granularity and to configure PS use case and domain models in industrial settings.

Developing a coding scheme for analysing classroom dialogue across educational contexts

The research reported sought to develop a framework for systematically analysing classroom dialogue for application across a range of educational settings. The paper outlines the development and refinement of a coding scheme that attempts to represent and operationalise commonalities amongst some key theorists in the field concerning productive forms of educational dialogue. The team has tested it using video recordings from classroom settings in the UK and Mexico, across age phases, subject areas, and different interactional contexts including whole class, group and paired work. Our Scheme for Educational Dialogue Analysis (SEDA) is situated within a sociocultural paradigm, and draws on Hymes' Ethnography of Communication to highlight the importance of context. We examined how such a tool could be used in practice. We found that concentrating on the ‘communicative act’ to explore dialogue between participants was an appropriate level of granularity, while clustering the 33 resulting codes according to function of the acts helped to highlight dialogic sequences within lessons. We report on the application of the scheme in two different learning contexts and reflect on its fitness for purpose, including perceived limitations. Development of specialised sub-schemes and a version for teachers is underway.

Inferring the mesoscale structure of layered, edge-valued, and time-varying networks.

Many network systems are composed of interdependent but distinct types of interactions, which cannot be fully understood in isolation. These different types of interactions are often represented as layers, attributes on the edges, or as a time dependence of the network structure. Although they are crucial for a more comprehensive scientific understanding, these representations offer substantial challenges. Namely, it is an open problem how to precisely characterize the large or mesoscale structure of network systems in relation to these additional aspects. Furthermore, the direct incorporation of these features invariably increases the effective dimension of the network description, and hence aggravates the problem of overfitting, i.e., the use of overly complex characterizations that mistake purely random fluctuations for actual structure. In this work, we propose a robust and principled method to tackle these problems, by constructing generative models of modular network structure, incorporating layered, attributed and time-varying properties, as well as a nonparametric Bayesian methodology to infer the parameters from data and select the most appropriate model according to statistical evidence. We show that the method is capable of revealing hidden structure in layered, edge-valued, and time-varying networks, and that the most appropriate level of granularity with respect to the additional dimensions can be reliably identified. We illustrate our approach on a variety of empirical systems, including a social network of physicians, the voting correlations of deputies in the Brazilian national congress, the global airport network, and a proximity network of high-school students.

Dynamic Cultural Contextualisation of Educational Content in Intelligent Learning Environments using ICON

Cultural awareness, when applied to Intelligent Learning Environments (ILEs), contours the overall appearance, behaviour, and content used in these systems through the use of culturally-relevant student data and information. In most cases, these adaptations are system-initiated with little to no consideration given to student-initiated control over the extent of cultural-awareness being used. This paper examines some of the issues relevant to these challenges through the development of the ICON (Instructional Cultural cONtextualisation) system. The paper explores computational approaches for modelling the diversity of students within subcultures, and the necessary semantic formalisms for representing and reasoning about cultural backgrounds at an appropriate level of granularity for ILEs. The paper investigates how student-initiated control of dynamic cultural adaptation of educational content can be achieved in ILEs, and examines the effects of cultural variations of language formality and contextualisation on student preferences for different types of educational content. Evaluations revealed preliminary insight into quantifiable thresholds at which student perception for specific types of culturally-contextualised content vary. The findings further support the notion put forth in the paper that student-initiated control of cultural contextualisation should be featured in ILEs aiming to cater for diverse groups of students.

Time to specify mechanisms of community health benefit or harm

Time to specify mechanisms of community health benefit or harm

Grammatical profiles and the interaction of the lexicon with aspect, tense, and mood in Russian

Abstract We propose the “grammatical profile” as a means of probing the aspectual behavior of verbs. A grammatical profile is the relative frequency distribution of the inflected forms of a word in a corpus. The grammatical profiles of Russian verbs provide data on two crucial issues: a) the overall relationship between perfective and imperfective verbs and b) the identification of verbs that characterize various intersections of aspect, tense and mood (TAM) with lexical classes. There is a long-standing debate over whether Russian aspectual “pairs” are formed only via suffixation (the Isacenko hypothesis) or whether they are formed via both suffixation and prefixation (the traditional view). We test the Isacenko hypothesis using data on the corpus frequency of inflected forms of verbs. We find that the behavior of perfective and imperfective verbs is the same regardless of whether the aspectual relationship is marked by prefixes or suffixes; our finding thus supports the traditional view. Introspective descriptions of Russian aspect have often connected the use of particular inflectional forms with certain uses of aspect; for example, the use of imperative forms with the imperfective aspect to produce expressions that are very polite. Grammatical profiles make it possible to identify verbs that behave as outliers, presenting unusually large proportions of usage in parts of the paradigm. This analysis both gives substance to and extends previous introspective descriptions by identifying the verbs most involved in certain TAM-category interactions. On a methodological level, this study contributes to current discussions on the use of inflected forms vs. lemmas in corpus studies. Newman (Aiming low in linguistics: Low-level generalizations in corpus-based research: National Chiao Tung University, 2008) finds valuable information at the level of the inflectional form, and Gries (forthcoming) argues that inflectional forms do not necessarily provide a better basis for analysis than lemmas. We agree with them that the appropriate level of granularity is determined by both the language and the linguistic phenomenon under analysis.

Mixing Bayes and empirical Bayes inference to anticipate the realization of engineering concerns about variant system designs

Mixing Bayes and Empirical Bayes inference provides reliability estimates for variant system designs by using relevant failure data – observed and anticipated – about engineering changes arising due to modification and innovation. A coherent inference framework is proposed to predict the realization of engineering concerns during product development so that informed decisions can be made about the system design and the analysis conducted to prove reliability. The proposed method involves combining subjective prior distributions for the number of engineering concerns with empirical priors for the non-parametric distribution of time to realize these concerns in such a way that we can cross-tabulate classes of concerns to failure events within time partitions at an appropriate level of granularity. To support efficient implementation, a computationally convenient hypergeometric approximation is developed for the counting distributions appropriate to our underlying stochastic model. The accuracy of our approximation over first-order alternatives is examined, and demonstrated, through an evaluation experiment. An industrial application illustrates model implementation and shows how estimates can be updated using information arising during development test and analysis.

Reusable Models in Industrial Automation: Experiences in Defining Appropriate Levels of Granularity

AbstractDomain engineering approaches are widely used in the software engineering. They focus on building reusable models in a domain. At the Institute of Industrial Automation and Software Engineering (IAS) of the University of Stuttgart, a new domain engineering approach has been developed. The approach considers entire industrial automation systems, including hardware and software. Models that capture structure and behavior of entire systems have to be carefully tailored, in order to be reusable. The level of granularity deserves particular attention, since too coarse or too fine grained reusable models discourage reuse. This paper addresses the issue of finding appropriate levels of granularity and proposes a set of recommendations for determining appropriate levels of granularity for reusable models created during domain engineering.