Different Levels Of Granularity Research Articles

Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.

Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health. This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions. The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention. This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.

A collostructional approach to Japanese noun-modifying clause construction use and acquisition: a learner corpus study

Abstract Japanese features a general noun-modifying clause construction (NMCC) with a more versatile range of semantic and pragmatic interpretations than equivalent constructions in other languages. Motivated by the learning challenge NMCCs pose to Japanese as a foreign language (JFL) learners, this article examines speech data from the International Corpus of Japanese as a Second Language (I-JAS) to compare learner use of NMCCs against a large L1 Japanese corpus. Instances of the construction from both corpora were analyzed to identify high-frequency part-of-speech categories and subcategories in the modifying clause predicate and head noun slots. A simple collexeme analysis was then employed to identify strongly attracted and repelled lexical items among those identified in realizations of the construction. Taken together, findings from these analyses revealed an important connection between the semantic weight of head nouns in NMCCs and the idiomaticity of the construction, with learner productions demonstrating a tendency toward heavy head nouns. This study lays the groundwork for future research seeking to explore the NMCC at different levels of granularity and to improve its treatment in JFL pedagogical materials.

Block-Level Goal Recognition Design

Existing works on goal recognition design (GRD) consider the underlying domain as a classical planning domain and apply modifications to the domain to minimize the worst case distinctiveness. In this paper, we propose replacing existing modifications with blocks, which group several closely related modifications together such that a block can modify a region in a search space with respect to some design constraints. Moreover, there could be blocks within blocks such that the design space becomes hierarchical for modifications at different levels of granularity. We present 1) a new version of pruned-reduce, a successful pruning rule for GRD, for block-level GRD, and 2) a new pruning rule for pruning some branches in both hierarchical and non-hierarchical design space. Our experiments show that searching in hierarchical design spaces greatly speeds up the redesign process.

Imputation of plasma lipid species to facilitate integration of lipidomic datasets

Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

Hi-PART: Going Beyond Graph Pooling with Hierarchical Partition Tree for Graph-Level Representation Learning

Graph pooling refers to the operation that maps a set of node representations into a compact form for graph-level representation learning. However, existing graph pooling methods are limited by the power of the Weisfeiler–Lehman (WL) test in the performance of graph discrimination. In addition, these methods often suffer from hard adaptability to hyper-parameters and training instability. To address these issues, we propose Hi-PART, a simple yet effective graph neural network (GNN) framework with Hi erarchical Par tition T ree (HPT). In HPT, each layer is a partition of the graph with different levels of granularities that are going toward a finer grain from top to bottom. Such an exquisite structure allows us to quantify the graph structure information contained in HPT with the aid of structural information theory. Algorithmically, by employing GNNs to summarize node features into the graph feature based on HPT’s hierarchical structure, Hi-PART is able to adequately leverage the graph structure information and provably goes beyond the power of the WL test. Due to the separation of HPT optimization from graph representation learning, Hi-PART involves the height of HPT as the only extra hyper-parameter and enjoys higher training stability. Empirical results on graph classification benchmarks validate the superior expressive power and generalization ability of Hi-PART compared with state-of-the-art graph pooling approaches.

Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

A Bayesian approach to uncertainty in word embedding bias estimation

Abstract Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number metric. However, such metrics and the related statistical significance calculations rely on treating pre-averaged data as individual data points and employing bootstrapping techniques with low sample sizes. We show that similar results can be easily obtained using such methods even if the data are generated by a null model lacking the intended bias. Consequently, we argue that this approach generates false confidence. To address this issue, we propose a Bayesian alternative: hierarchical Bayesian modeling, which enables a more uncertainty-sensitive inspection of bias in word embeddings at different levels of granularity. To showcase our method, we apply it to Religion, Gender, and Race word lists from the original research, together with our control neutral word lists. We deploy the method using Google, Glove, and Reddit embeddings. Further, we utilize our approach to evaluate a debiasing technique applied to the Reddit word embedding. Our findings reveal a more complex landscape than suggested by the proponents of single-number metrics. The datasets and source code for the paper are publicly available.

Risk-informed design of advanced nuclear reactor: A target unavailability approach

Incorporating specified safety and production targets during the design phase of an engineered facility can effectively support reductions in construction and operation costs of nuclear power plants. In addition, this supports efforts toward making nuclear power technology more competitive and profitable than other electric power production forms by incorporating operational and design features into the design phase that directly support the increased likelihood of meeting such originating targets. Unavailability is the key parameter to establish plant-level capabilities that cascade down to function, system, and component levels to ensure that all sources of unavailability are addressed in the facility's design phases. In this article, two methods are presented to propose unavailability targets for nuclear reactor systems to risk-inform and optimize the design features, from an availability perspective: the first method is a simple method, and the second is a non-dominated sorting genetic algorithm III (NSGA-III) multi-objective optimization. These iterative methods are demonstrated in this paper for a hypothetical facility with a design maturity such that there is knowing the main components to comply with the engineering and security functions of the systems. With an assumed mature design, it is implied that generic data to execute quantitative reliability, and availability analyses are available at the systems levels that are sufficient to support a design-specific probabilistic risk assessment (PRA). The proposed methods modify the probabilities of basic events within the fault tree structures that model system and component unavailability sources that will be required for the facility’s operating license. The methods provided are applied to visualize the characteristics of the problem and the quantitative results. This work forms the basis for future work in which design alternatives affecting unavailabilities can be estimated and compared to their associated unavailability targets at different levels of granularity.

A multi granularity information fusion text classification model based on attention mechanism

Existing research on Chinese text classification primarily focuses on classifying data information at different granularities, such as character, word, sentence, and chapter. However, this approach often fails to capture the semantic information embedded in these different levels of granularity. To enhance the extraction of the text’s core content, this study proposes a text classification model that incorporates an attention mechanism to fuse multi-granularity information. The model begins by constructing embedding vectors for characters, words, and sentences. Character and word vectors are generated using the Word2Vec training model, allowing the data to be converted into these respective vectors. To capture contextual semantic features, a bidirectional long and short-term memory network is employed for character and word vectors. Sentence vectors, on the other hand, are processed using the FastText model to extract the features they contain. To extract further important semantic information from the different feature vectors, they are fed into an attention mechanism layer. This layer enables the model to prioritize and emphasize the most significant information within the text. Experimental results demonstrate that the proposed model outperforms both single-granularity classification and combinations of two or more granularities. The model exhibits improved classification accuracy across three publicly available Chinese datasets.

Clustered Graph Matching for Label Recovery and Graph Classification

Given a collection of vertex-aligned networks and an additional label-shuffled network, we propose procedures for leveraging the signal in the vertex-aligned collection to recover the labels of the shuffled network. We consider matching the shuffled network to averages of the networks in the vertex-aligned collection at different levels of granularity. We demonstrate both in theory and practice that if the graphs come from different network classes, then clustering the networks into classes followed by matching the new graph to cluster-averages can yield higher fidelity matching performance than matching to the global average graph. Moreover, by minimizing the graph matching objective function with respect to each cluster average, this approach simultaneously classifies and recovers the vertex labels for the shuffled graph. These theoretical developments are further reinforced via an illuminating real data experiment matching human connectomes.

EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation

Electronic health records (EHRs) contain diverse patient information, including medical notes, clinical events, and laboratory test results. Integrating this multimodal data can improve disease diagnoses using deep learning models. However, effectively combining different modalities for diagnosis remains challenging. Previous approaches, such as attention mechanisms and contrastive learning, have attempted to address this but do not fully integrate the modalities into a unified feature space. This paper presents EHR-KnowGen, a multimodal learning model enhanced with external domain knowledge, for improved disease diagnosis generation from diverse patient information in EHRs. Unlike previous approaches, our model integrates different modalities into a unified feature space with soft prompts learning and leverages large language models (LLMs) to generate disease diagnoses. By incorporating external domain knowledge from different levels of granularity, we enhance the extraction and fusion of multimodal information, resulting in more accurate diagnosis generation. Experimental results on real-world EHR datasets demonstrate the superiority of our generative model over comparative methods, providing explainable evidence to enhance the understanding of diagnosis results.

Behavioral Economics and Neuroeconomics of Environmental Values

Identifying mechanisms of real-life human decision-making is central to inform effective, human-centric public policy. Here, we report larger trends and synthesize preliminary lessons from behavioral economic and neuro-economic investigations focusing on environmental values. We review the currently available evidence at different levels of granularity, from insights into how individuals value natural resources (individual level), evidence from work on group externalities, common pool resources, and social norms (social group level) to the study of incentives, policies, and their impact (institutional level). At each level, we identify viable directions for future scientific research and actionable items for policy-makers. Coupled with new technological and methodological advances, we suggest that behavioral economic and neuroeconomic insights may inform an effective strategy to optimize environmental resources. We conclude that the time is ripe for action to enrich policies with scientifically grounded insights, making an impact in the interest of current and future generations.

Efficient Computation of the Max-Plus Semantics of Synchronous Dataflow Graphs

Streaming systems are naturally modeled with synchronous dataflow graphs (SDFGs). The max-plus semantics of an SDFG is a compact matrix representation of its timing behavior. The max-plus semantics enables us to analyze and control timing properties of the systems, such as the obtainable minimum guaranteed throughput and maximum latency. Deriving the max-plus semantics, and consequently, performance analysis may be computationally expensive since the state-of-the-art method simulates one iteration of an SDFG. This holds, in particular, for systems whose components operate at different levels of granularity, as this results in many executions of some components in one iteration. This paper aims at efficiently calculating the max-plus semantics of SDFGs. The paper proposes an optimization framework exploring decompositions of a given SDFG and finding a composition sequence whose computational effort for compositionally obtaining the max-plus semantics is minimal. Not only does our proposed technique accelerate the performance analysis of multiscale streaming systems, but it also allows us to compute the max-plus semantics of some systems for which the state-of-the-art method does not succeed because of memory limitations.

The SCOPE framework – implementing the ideals of responsible research assessment

Background: Research and researchers are heavily evaluated, and over the past decade it has become apparent that the consequences of evaluating the research enterprise and particularly individual researchers are considerable. This has resulted in the publishing of several guidelines and principles to support moving towards more responsible research assessment (RRA). To ensure that research evaluation is meaningful, responsible, and effective the International Network of Research Management Societies (INORMS) Research Evaluation Group created the SCOPE framework enabling evaluators to deliver on existing principles of RRA. SCOPE bridges the gap between principles and their implementation by providing a structured five-stage framework by which evaluations can be designed and implemented, as well as evaluated. Methods: SCOPE is a step-by-step process designed to help plan, design, and conduct research evaluations as well as check effectiveness of existing evaluations. In this article, four case studies are presented to show how SCOPE has been used in practice to provide value-based research evaluation. Results: This article situates SCOPE within the international work towards more meaningful and robust research evaluation practices and shows through the four case studies how it can be used by different organisations to develop evaluations at different levels of granularity and in different settings. Conclusions: The article demonstrates that the SCOPE framework is rooted firmly in the existing literature. In addition, it is argued that it does not simply translate existing principles of RRA into practice, but provides additional considerations not always addressed in existing RRA principles and practices thus playing a specific role in the delivery of RRA. Furthermore, the use cases show the value of SCOPE across a range of settings, including different institutional types, sizes, and missions.

Individuality-enhanced and multi-granularity consistency-preserving graph neural network for semi-supervised node classification

Semi-supervised node classification is an important task that aims at classifying nodes based on the graph structure, node features, and class labels for a subset of nodes. While most graph convolutional networks (GCNs) perform well when an ample number of labeled nodes are available, they often degenerate when the amount of labeled data is limited. To address this problem, we propose a scheme, namely, Individuality-enhanced and Multi-granularity Consistency-preserving graph neural Network (IMCN), which can alleviate the problem of losing individual information within the encoder while providing a reliable supervised signal for learning purposes. First, one simple encoder based on node features only is integrated to enhance node individuality and amend node commonality learned by the GCN-based encoder. Then, three constraints are defined at different levels of granularity, encompassing node embedding agreement, semantic class alignment, and node-to-class distribution identity. They can maintain the consistency between the individuality and commonality of nodes and be leveraged as latent supervised signals for learning representative embeddings. Finally, the trade-off between the individuality and commonality of nodes captured by two encoders is taken into consideration for node classification. Extensive experiments on six real-world datasets have been conducted to validate the superiority of IMCN against state-of-the-art baselines in handling node classification tasks with scarce labeled data.Graphical abstract

Keyword occurrences and journal specialization

Since the borders of disciplines change over time and vary across communities and geographies, they can be expressed at different levels of granularity, making it challenging to find a broad consensus about the measurement of interdisciplinarity. This study contributes to this debate by proposing a journal specialization index based on the level of repetitiveness of keywords appearing in their articles. Keywords represent one of the most essential items for filtering the vast amount of research available. If chosen correctly, they can help to identify the central concept of the paper and, consequently, to couple it with manuscripts related to the same field or subfield of research. Based on these universally recognized features of article keywords, the study proposes measuring the specialization of a journal by counting the number of times that a keyword is Queryrepeated in a journal on average (Sj). The basic assumption underlying the proposal of a journal specialization index is that the keywords may approximate the article’s topic and that the higher the number of papers in a journal based on a topic, the higher the level of specialization of that journal. The proposed specialization metric is not invulnerable to a set of limitations, among which the most relevant seems to be the lack of a standard practice regarding the number and consistency of keywords appearing in each article.

High-dimensional portfolio optimization based on tree-structured factor model

Certain characteristics are known to predict cross-sectional expected stock returns and risk exposures. We propose a novel portfolio optimization procedure to incorporate multiple characteristic information, which requires minimal parameters and no stringent assumptions. Instead of investing in individual stocks, we perform portfolio optimization on a large panel of characteristic portfolios generated by a tree-structured portfolio sorting method, which can capture the non-linearities and interactions among stock characteristics to help predict expected returns and covariances. Simulations show that our tree-structured Lasso-based mean–variance (MV) strategy has better out-of-sample Sharpe ratios than the three benchmark strategies: the stock-based MV strategy, the stock-based global minimum variance (GMV) strategy, and the double-sorted portfolio-based MV strategy. We use daily stock data from the Chinese A-share market from 2002-04-01 to 2022-12-31 to compare the out-of-sample performance of the tree-structured Lasso-MV with other classical strategies, such as GMV and equally weighted (EW) strategies, etc. The empirical results suggest that the tree-structured Lasso-MV strategy can achieve a higher Sharpe ratio, a smaller standard deviation, and a lower turnover. These results are robust to different levels of granularity.

Feature selection in threes: Neighborhood relevancy, redundancy, and granularity interactivity

As a fundamental granular computing strategy, neighborhood granulation has been acknowledged as an intuitive and effective approach to feature evaluation and selection. However, such an approach always has a bias towards a fixed neighborhood granularity, while ignoring the observations across different levels of granularity. To this end, a novel algorithm based on Neighborhood relevancY, redundancY, and granularity interactivitY (N3Y) is proposed. Technically, N3Y adheres well to the rudiment of three-way decision, evaluating and selecting features in threes: 1) feature-to-class relevancy; 2) feature-to-feature redundancy; 3) granularity-to-granularity interactivity. Specifically, firstly, the neighborhood symmetrical uncertainty induced by neighborhood measures is adopted to evaluate the relevancy and redundancy of candidate feature subset; secondly, the proposed neighborhood granularity interactivity allows an uncertainty quantification for finer-to-coarser granularity, and is leveraged as a supplemental factor to guide the relevancy and redundancy, making our procedure more comprehensive; thirdly, a forward-greedy selector is devised, which is required to maximize the evaluation criterion integrating neighborhood relevancy, redundancy, and granularity interactivity. Extensive experiments demonstrate that N3Y outperforms several other advanced feature selectors.

Health data hubs: an analysis of existing data governance features for research

BackgroundDigital transformation in healthcare and the growth of health data generation and collection are important challenges for the secondary use of healthcare records in the health research field. Likewise, due to the ethical and legal constraints for using sensitive data, understanding how health data are managed by dedicated infrastructures called data hubs is essential to facilitating data sharing and reuse.MethodsTo capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generation of health data governance patterns was carried out. The target audience of this study was national, European, and global data hubs. In total, the designed survey was sent to a representative list of 99 health data hubs in January 2022.ResultsIn total, 41 survey responses received until June 2022 were analysed. Stratification methods were performed to cover the different levels of granularity identified in some data hubs’ characteristics. Firstly, a general pattern of data governance for data hubs was defined. Afterward, specific profiles were defined, generating specific data governance patterns through the stratifications in terms of the kind of organization (centralized versus decentralized) and role (data controller or data processor) of the health data hub respondents.ConclusionsThe analysis of the responses from health data hub respondents across Europe provided a list of the most frequent aspects, which concluded with a set of specific best practices on data management and governance, taking into account the constraints of sensitive data. In summary, a data hub should work in a centralized way, providing a Data Processing Agreement and a formal procedure to identify data providers, as well as data quality control, data integrity and anonymization methods.

Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (CAM) system design based on general modeling network specification (GMNS)

This study presents a novel framework and open-source tools for simulating and managing connected and automated mobility (CAM) systems, taking into account their hierarchical nature and various levels of scheduling. The framework is based on a multi-layered network representation, which allows for efficient and accurate modeling of CAM systems at different levels of granularity, from macroscopic to microscopic. By employing this hierarchical approach, we achieve a balance between the level of detail in the representation and computational efficiency. Additionally, a spatial-discrete virtual track-based representation is introduced for precise vehicle dynamics modeling and for ensuring consistency with higher-level routing decisions. This facilitates individualized active traffic management for CAM applications. As part of our research, we have developed osm2gmns, an open-source package that allows users to effortlessly access and process transportation networks from OpenStreetMap in the General Modeling Network Specification (GMNS) format, facilitating data sharing and research collaboration. Furthermore, we explore traffic simulation, optimization, and operation methodologies for CAM systems, particularly focusing on the extent of scheduling capabilities. To support the research community, we further introduce an open-source package CAMLite for CAM system modeling. The effectiveness of our proposed methodologies and tools is demonstrated through a series of numerical experiments.