Hierarchical View Research Articles

Geographical and aesthetic inclusiveness: A new cultural worldview? The case of nine European countries

The European social landscape has changed due to Europeanization, globalization, and migration processes, leading to more transnational exchanges, personal relations connecting people from different European countries, and greater cultural and ethnic diversity. Our research explored whether these processes have led to a new inclusionary cultural worldview and the possible social underpinnings. We analyse the inclusionary cultural worldview on two levels: geographical inclusiveness, reflecting an openness to other cultures, and aesthetic inclusiveness, reflecting an openness to go beyond a traditional hierarchical highbrow view of culture. In our research, based on a recent survey conducted in nine European countries, we find evidence for both inclusionary and exclusionary orientations in both geographical and aesthetic worldviews, with the inclusionary and exclusionary orientations correlating with each other and constituting a cultural worldview. We further find that the inclusionary worldview is more pronounced among better educated and older people, among women, and in more socioeconomically developed countries. In contrast, the exclusionary worldview is more common among less educated and older people, men, and in less socioeconomically developed and less economically stable countries.

Multitype view of knowledge contrastive learning for recommendation

Graph Neural Networks (GNNs) are playing an increasingly vital role in the field of recommender systems. To improve knowledge perception within GNNs, contrastive learning has been applied and has proven to be highly effective. GNNs have the ability to aggregate diverse knowledge and integrate topologies, while contrastive learning seeks supervisory signals from the model data. The combination of GNNs and contrastive learning can improve recommendations. However, thoughtless or incomplete contrastive learning settings limit the effectiveness of GNNs-based recommender systems in learning knowledge from knowledge and interaction graphs. To better exploit the valuable information within knowledge graphs, we propose a novel multitype view of knowledge contrastive learning for recommendations (MVKC) model.The MVKC model generates hierarchical views and augmented views in two modules, performing cross-hierarchical-view and cross-augmented-view contrastive learning and mining graph features in a self-supervised manner. The hierarchical views consist of global and local parts at multiple levels, while the augmented views are fused from the augmented knowledge graph and augmented interaction graph in our augmented processing. These features allow the MVKC model to alleviate the sparsity of user–item interaction graphs, suppress knowledge graph noise, and filter long-tail entities, which has been proven extremely important for a recommendation. The MVKC model also has strong anti-interference ability and robustness, which is crucial for a well-established model. Our experiments with three public datasets demonstrate that the MVKC model outperforms current state-of-the-art methods.

Memperbaiki Hubungan Antara Manusia dan Alam Melalui Pemikiran Daisaku Ikeda

Man and nature are inseparable unity, both are organic unity that must be mutually maintained. Daisaku Ikeda asserted that the state of the world filled with the destruction of nature has diminished the interdependent relationship between humans and nature. The destruction of nature caused by human unconsciousness of nature preservation and hierarchical view which puts human beings as supreme beings among all that exists in nature is a significant warning that should re-awaken human beings of responsibility (guarding and nurturing) to nature. Pope Francis in Laudato Si' emphasizes the ecological repentance that should be imbued with ecological spirituality rooted in the faith of faith in Christ as proclaimed through the Gospel. In this context, ecological conversion can be understood as the process of sharing all the fruits of human encounter with Christ with the surrounding environment. One result of this encounter should be manifested in relation to the universe, i.e. environmental justice.

Application of data element mapping and analysis for system definition to enable model‐based systems engineering

AbstractIn the connected age of the model‐based enterprise and model‐based systems engineering (MBSE), new systems engineering tools are needed to move from a functional, document‐centric, hierarchical view of data and information to the individual units of data or data element level view. Data element mapping and analysis (DEMA) is a technology‐agnostic analytical methodology that combines traditional functional analysis techniques, systems engineering elicitation practices, and novel data mapping techniques to provide a holistic view of a system's data and information flows at the individual units of data (data element level). In this research, DEMA was utilized to enable an enhanced system definition for the development of a verification and validation process as applied to a modeling and simulation environment. DEMA uncovered and visually mapped the hidden flow of approximately 1600 data vessel occurrences as inputs or outputs to 79 functional activities in 23 disparate storage locations. The results reveal that DEMA is a practical tool for both improving existing systems and defining new systems. The data element level view captured by DEMA can be used to define the interconnections between the system elements that are input to systems modeling language (SysML) models. Therefore, DEMA is a necessary and novel tool that can be used to enable system digitalization.

ViSSR: A visual analytics system for student high-order social relationships at campus

Social relationships among students at campus are closely related to their mental health and academic performance. Therefore, it is a very important task for educators to analyze students’ social relationships. However, existing studies have focused on one-to-one social relationships between students, few ones have explored the high-order community relationships hidden in social networks, especially in a visual manner. To solve this problem, a visual analysis system called ViSSR is proposed in this paper, which utilizes the Louvain algorithm to detect the hierarchical community structure of students’ social network at campus, and then provides four coordinated views to visualize the detection results. Among the views, the hierarchical hypergraph view is to visualize the hierarchical community structure that greatly breaks through the limitations of first-order relationships available in a traditional node-link social network, the community analysis view and individual analysis view show the social characteristics of a community and individual student respectively, and the matrix view displays the behavioral features of students. Case studies and experts evaluation have been conducted to demonstrate the usability of the system.

The image of a Japanese as seen by a Dutchman in the English novel and its Ukrainian translation

The article deals with the problem of rendering a literary image of a foreigner in translation. The object of research is the cumulative literary image of a Japanese on the material of David Mitchell’s novel “The Thousand Autumns of Jacob De Zoet” and the subject of research is the means and strategies of its reproduction in the Ukrainian translation. The aim of the research is to conceptualize the above strategies and determine the factors that have an impact on the translator’s decision-making. In particular, the attention is drawn to the factor of ethnic stereotypes that underline the formation of a foreigner’s image in a literary discourse. The article offers a hierarchic view of a literary image as an interaction of three levels of analysis: mega-image (correlated with the idea/theme/conception of a literary piece), macro-image (literary image per se) and micro-image (linguistic and/or stylistic means of a macro-image embodiment). Correspondingly, the translator’s attention should be concentrated on equivalent rendering micro-images, but in a manner consistent with corresponding macro-images and the mega-image of the whole text. Every literary image of a foreigner is based on certain ethno-stereotypes, also known as heterostereotypes that govern the author’s selection of micro-images and consequently the whole structure of a macro-image. Three aspects of a Japanese’s macro-image were singled out: appearance; speech characteristics; traditions, customs and rituals. At the next stage, the micro-images of these aspects’ verbalization were singled out and the translator’s strategies of their re-creation were analyzed. The conclusion was made that due to the similarity in foreigners’ stereotyping by the British and Ukrainians, the translator had no need for cultural adaptations and their strategy was predominantly aimed at overcoming interlinguistic asymmetry.

Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible.

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity.

The Great Web of Being: Environmental Ethics without Value Hierarchy

Hierarchical views of the world such as the great chain of being have come under sustained critique in recent decades, and rightly so. They have justified not only the domination of non-human creatures but also the devaluation (via animalization/racialization) of many humans. The rejection of hierarchy and the great chain of being, however, does not require the rejection of the Christian Platonic theological vision upon which hierarchy is often based. In this paper, I argue that Christian Platonic theology has always been in tension with the great chain of being and is better suited to a non-hierarchical view of creaturely value. I then develop the ethical implications of this view in dialogue with both environmental and animal ethics and anti-racist and decolonial scholarship.

Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data

Here, we present Anchored-fusion, a highly sensitive fusion gene detection tool. It anchors a gene of interest, which often involves driver fusion events, and recovers non-unique matches of short-read sequences that are typically filtered out by conventional algorithms. In addition, Anchored-fusion contains a module based on a deep learning hierarchical structure that incorporates self-distillation learning (hierarchical view learning and distillation [HVLD]), which effectively filters out false positive chimeric fragments generated during sequencing while maintaining true fusion genes. Anchored-fusion enables highly sensitive detection of fusion genes, thus allowing for application in cases with low sequencing depths. We benchmark Anchored-fusion under various conditions and found it outperformed other tools in detecting fusion events in simulated data, bulk RNA sequencing (bRNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Our results demonstrate that Anchored-fusion can be a useful tool for fusion detection tasks in clinically relevant RNA-seq data and can be applied to investigate intratumor heterogeneity in scRNA-seq data.

Exploiting Flat Namespace to Improve File System Metadata Performance on Ultra-Fast, Byte-Addressable NVMs

The conventional file system provides a hierarchical namespace by structuring it as a directory tree. Tree-based namespace structure leads to inefficient file path walk and expensive namespace tree traversal, underutilizing ultra-low access latency and superior sequential performance provided by non-volatile memories (NVMs). This article proposes FlatFS+, an NVM file system that features a flat namespace architecture while providing a compatible hierarchical namespace view. FlatFS+ incorporates three novel techniques: the direct file path walk model, range-optimized B r tree, and compressed index key design with scan and write dual optimization, to fully exploit flat namespace to improve file system metadata performance on ultra-fast, byte-addressable NVMs. Evaluation results demonstrate that FlatFS+ achieves significant performance improvements for metadata-intensive benchmarks and real-world applications compared to other file systems.

Swedish bureaucratic biodiversity: Analysing municipal worker discourse with the theory of sociocultural viability

AbstractCities are important sites for societal transitions towards sustainability, which is increasingly recognised around the issue of biodiversity conservation and protection. However, cities are often characterised by the need to develop and grow. Furthermore, efforts to promote sustainable development have been criticised as failing to address the fundamental causes of environmental destruction. In this article, based on interviews with bureaucrats and documentary analysis, I explore urban planning and biodiversity protection at four Swedish municipalities. Biodiversity protection has been an official goal in Sweden for 30 years. As such, my research aim is to explore how Swedish bureaucrats represent efforts to balance imperatives to develop cities and protect biodiversity. Taking an institutional approach, I identify what information is included and excluded. In assessing municipal discourse, I utilise the theory of sociocultural viability, which provides an analytical typology of four worldviews. I identify that within respondents' discourse, biodiversity primarily emerges as a product of a hierarchical view of reality, as a measurable object; an indicator; a characteristic; and as a provider that is both engineerable and replaceable. This was despite numerous respondents articulating an egalitarian desire for more holistic interpretations of biodiversity in urban planning, appreciative of its inherent worth. This suggests that biodiversity has largely been integrated into extant hierarchical conceptualisations of public administration. According to cultural theory, addressing wicked policy problems effectively requires insight from several of the typology's worldviews. As such, current practice may reiterate dominant contemporary views on nature rather than innovation towards a radically different society.

Metaphor of Nation and Nationalism

This study extends prior theoretical discussions on metaphor as a rhetorical topos, a discourse statement, and a cognitive tool, with a particular focus on the metaphor of nation due to its enduring relevance in contemporary discourse. It explores the intricate role of metaphor in shaping, perpetuating, and challenging nationalist discourse, recognizing that metaphors possess a dual capacity to both wield power and constrain truths. The metaphors of the nation, contextualized within the CONTAINMENT metaphor framework, play a pivotal role in the construction and reinforcement of nationalist discourse. From a feminist perspective, nationalist discourse and its associated metaphors are deeply rooted in patriarchal ideals, leading to the marginalization of women and the portrayal of the nation as a patriarchal family or brotherhood. When viewed through colonial and postcolonial lenses, these metaphors reveal concepts of hierarchy and equality between nations. For instance, Rabindranath Tagore metaphorically characterizes the nation as a soulless monster, emphasizing the imperative to reject this notion in favor of fostering a more balanced community that harmonizes both body and spirit. In contrast, Thomas Hobbes employs the metaphor of the commonwealth as a Leviathan, firmly establishing a hierarchical structure that underscores the significance of obedience in the relationship between the soul and the body of the commonwealth, with the soul positioned as superior to its constituent elements. Tagore’s perspective aligns with notions of equality, anti-nationalism, and postcolonialism, whereas Hobbes’ hierarchical view could potentially justify the legitimacy of colonialism.

“I’m not angry!”: language ideologies, misunderstanding, and marginalization among North Korean refugees in rural South Korea

Abstract This qualitative study examines the language attitudes and language use of two North Korean refugees living in the Gyeongsang provincial region of South Korea and actively trying to assimilate into mainstream Korean society. In interviews, the participants expressed a hierarchical view of three varieties of Korean (their North Korean Hamgyong dialect, the South Korean Gyeongsang dialect, and standard South Korean). They discussed how their North Korean accents exacerbated their marginalization, described the Gyeongsang dialect as “ignorant” and “rude,” and explained how and why they were trying to acquire standard South Korean. They also described how their North Korean accent continued to affect their communication with local South Korean speakers, who often perceived them as sounding angry and commanding. The participants had developed diverse communicative strategies in response to these language-related challenges, including smiling so as to not appear aggressive, remaining silent to avoid being outed by their speech, speaking carefully to appear more South Korean and avoid potential misunderstandings, and proactively revealing their North Korean background and seeking their interlocutors’ understanding in advance. Based on the findings, the study offers practical implications for language-support programs designed for North Korean refugees.

TopicBubbler: An interactive visual analytics system for cross-level fine-grained exploration of social media data

How to explore fine-grained but meaningful information from the massive amount of social media data is critical but challenging. To address this challenge, we propose the TopicBubbler, a visual analytics system that supports the cross-level fine-grained exploration of social media data. To achieve the goal of cross-level fine-grained exploration, we propose a new workflow. Under the procedure of the workflow, we construct the fine-grained exploration view through the design of bubble-based word clouds. Each bubble contains two rings that can display information through different levels, and recommends six keywords computed by different algorithms. The view supports users collecting information at different levels and to perform fine-grained selection and exploration across different levels based on keyword recommendations. To enable the users to explore the temporal information and the hierarchical structure, we also construct the Temporal View and Hierarchical View, which satisfy users to view the cross-level dynamic trends and the overview hierarchical structure. In addition, we use the storyline metaphor to enable users to consolidate the fragmented information extracted across levels and topics and ultimately present it as a complete story. Case studies from real-world data confirm the capability of the TopicBubbler from different perspectives, including event mining across levels and topics, and fine-grained mining of specific topics to capture events hidden beneath the surface.

HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction

Risk prediction, usually achieved by learning representations from patient’s physiological sequence or user’s behavioral sequence data, and has been widely applied in healthcare and finance. Despite that, some recent time-aware deep learning methods have led to superior performances in such sequence representation learning tasks, such improvement is limited due to a lack of guidance from hierarchical global view. To address this issue, we propose a novel end-to-end H ierarchical G lobal V iew-guided (HGV) sequence representation learning framework. Specifically, the Global Graph Embedding (GGE) module is proposed to learn sequential clip-aware representations from temporal correlation graph (TCG) at instance level. Furthermore, following the way of key-query attention, the harmonic β-attention (β-Attn) is also developed for making a global tradeoff between time-aware decay and observation significance at channel level adaptively. Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view. Experimental results on both healthcare risk prediction benchmark and SMEs credit overdue risk prediction task from the real-world industrial scenario in MYBank, Ant Group, have illustrated that the proposed model can achieve competitive prediction performance compared with other known baselines. The code has been released public available at: https://github.com/LiYouru0228/HGV.

Fast hierarchical low-rank view factor matrices for thermal irradiance on planetary surfaces

We present an algorithm for compressing the radiosity view factor model commonly used in radiation heat transfer and computer graphics. We use a format inspired by the hierarchical off-diagonal low rank format, where elements are recursively partitioned using a quadtree or octree and blocks are compressed using a sparse singular value decomposition—the hierarchical matrix is assembled using dynamic programming. The motivating application is time-dependent thermal modeling on vast planetary surfaces, with a focus on permanently shadowed craters which receive energy through indirect irradiance. In this setting, shape models are comprised of a large number of triangular facets which conform to a rough surface. At each time step, a quadratic number of triangle-to-triangle scattered fluxes must be summed; that is, as the sun moves through the sky, we must solve the same view factor system of equations for a potentially unlimited number of time-varying righthand sides. We first conduct numerical experiments with a synthetic spherical cap-shaped crater, where the equilibrium temperature is analytically available. We also test our implementation with triangle meshes of planetary surfaces derived from digital elevation models recovered by orbiting spacecraft. Our results indicate that the compressed view factor matrix can be assembled in quadratic time, which is comparable to the time it takes to assemble the full view matrix itself. Memory requirements during assembly are reduced by a large factor. Finally, for a range of compression tolerances, the size of the compressed view factor matrix and the speed of the resulting matrix vector product both scale linearly (as opposed to quadratically for the full matrix), resulting in orders of magnitude savings in processing time and memory space.

HKGCL: Hierarchical graph contrastive learning for multi-domain recommendation over knowledge graph

Multi-domain recommendation (MDR) aims to improve the recommendation performance in all target domains simultaneously by leveraging rich data from relevant domains. However, conventional approaches either only rely on sparse interactions in each separate scenario, or simply combine multiple shared recommendation solutions, resulting in MDR systems still suffering from severely low recommendation accuracy. To tackle the above issues, we propose a novel recommendation model called Hierarchical Graph Contrastive Learning for Multi-Domain Recommendation over Knowledge Graph (HKGCL). Different from previous MDR models, our HKGCL can treat each relevant domain as a hierarchy in the interaction network. Based on the LightGCN aggregation strategy, a hierarchical message passing mechanism over the knowledge graph is designed to aggregate hierarchical knowledge representations for users and items. Then, a hierarchical node dropping scheme is proposed as our data augmentation technique on different hierarchical views to obtain more self-supervised semantic signals. Subsequently, three graph contrastive learning tasks in multiple and separate domains are proposed to explore domain-shared and domain-specific preference features for target users, respectively. Additionally, a novel Multi-domain Bayesian Personalized Ranking (MBPR) approach is proposed to assist in training our multi-task learning framework. Extensive experiments conducted on two real-world datasets demonstrate the consistent superiority of our proposed HKGCL over various state-of-the-art baselines, and also verify that the HKGCL can achieve strong performance on sparse interaction scenarios.

Geometry-Aware Planar Embedding of Treelike Structures

The growing complexity of spatial and structural information in 3D data makes data inspection and visualization a challenging task. We describe a method to create a planar embedding of 3D treelike structures using their skeleton representations. Our method maintains the original geometry, without overlaps, to the best extent possible, allowing exploration of the topology within a single view. We present a novel camera view generation method which maximizes the visible geometric attributes (segment shape and relative placement between segments). Camera views are created for individual segments and are used to determine local bending angles at each node by projecting them to 2D. The final embedding is generated by minimizing an energy function (the weights of which are user adjustable) based on branch length and the 2D angles, while avoiding intersections. The user can also interactively modify segment placement within the 2D embedding, and the overall embedding will update accordingly. A global to local interactive exploration is provided using hierarchical camera views that are created for subtrees within the structure. We evaluate our method both qualitatively and quantitatively and demonstrate our results by constructing planar visualizations of line data (traced neurons) and volume data (CT vascular and bronchial data).

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties. To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It's anticipated that this work can boost de novo molecule design. The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.

Are we developing leaders? Connecting undergraduate leadership identities to their needs and contexts

Graduates of the college are expected to have the skills needed to enter the workforce including leadership competencies. The purpose of this mixed methods case study was to identify leadership development influences and assess the leadership needs of undergraduate student leaders (n = 17) in the [college] at the [university]. We conducted the research through in-person, semi-structured interviews. We utilized a concurrent, sequential, multi-phase mixed methods design with a qualitative priority. In phase one, we open coded transcriptions and used an iterative process to find emerging themes. Results revealed four themes: engaged leadership, leadership experiences, group dynamics, and resources. In phase two, we employed a cross case comparison to explore similarities and differences across emergent themes related to leadership identity. it was evident that differing hierarchical and relational views of leadership existed based upon leadership identities.