Distant Entities Research Articles

Bridging the gap

The inclusion of the new mediation scales in the Common European Framework of Reference (CEFR) has fostered the role of second-language (L2) learners as multilingual social agents performing mediation activities that can build bridges between distant entities. However, even though mediation has become a fashionable topic in scholarly debate, there are still very few studies based on empirical evidence that support the theory. This study provides an empirical approach to mediation processes in the L2 classroom, more specifically in a context with Spanish-speaking first-language (L1) students enrolled in a German course in a Colombian university. The mediation processes are analyzed during two activities in which the students had to interact to fulfill the task. The aim of the study is twofold: (1) to measure the proportion of the types of mediation acts used in each activity; and (2) to provide specific examples of the strategies deployed in the mediation acts. The quantitative analysis confirms the presence of three types of mediation proposed by the CEFR – linguistic, communicative and cognitive – but records a low proportion of mediations performed in the L2, which reflects the difficulty associated with cognitively demanding tasks.

Knowledge Graph Embedding via Triplet Component Interactions

In knowledge graph embedding, multidimensional representations of entities and relations are learned in vector space. Although distance-based graph embedding methods have shown promise in link prediction, they neglect context information among the triplet components, i.e., the head_entity, relation, and tail_entity, limiting their ability to describe multivariate relation patterns and mapping properties. Such context information denotes the entity structural association inside the same triplet and implies the correlation between entities that are not directly connected. In this work, we propose a novel knowledge graph embedding model that explicitly considers context information in graph embedding via triplet component interactions (TCIE). To build connections between components and incorporate contextual information, entities and relations are represented as vectors comprised of two specialized parts, enabling comprehensive interaction. By simultaneously interacting with one-hop related head and tail entities, TCIE strengthens the connections between distant entities and enables contextual information to be transmitted across the knowledge graph. Mathematical proofs and experiments are performed to analyse the modelling ability of TCIE in knowledge graph embedding. TCIE shows a strong capacity for modelling four relation patterns (i.e., symmetry, antisymmetry, inverse, and composition) and four mapping properties (i.e., one-to-one, one-to-many, many-to-one, and many-to-many). The experimental evaluation of ogbl-wikikg2, ogbl-biokg, FB15k, and FB15k-237 shows that TCIE achieves state-of-the-art results in link prediction.

Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts.

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.

An efficiency relation-specific graph transformation network for knowledge graph representation learning

Knowledge graph representation learning (KGRL) aims to infer the missing links between target entities based on existing triples. Graph neural networks (GNNs) have been introduced recently as one of the latest trendy architectures serves KGRL task using aggregations of neighborhood information. However, current GNN-based methods have fundamental limitations in both modelling the multi-hop distant neighbors and selecting relation-specific neighborhood information from vast neighbors. In this study, we propose a new relation-specific graph transformation network (RGTN) for the KGRL task. Specifically, the proposed RGTN is the first pioneer model that transforms a relation-based graph into a new path-based graph by generating useful paths that connect heterogeneous relations and multi-hop neighbors. Unlike the existing GNN-based methods, our approach is able to adaptively select the most useful paths for each specific relation and to effectively build path-based connections between unconnected distant entities. The transformed new graph structure opens a new way to model the arbitrary lengths of multi-hop neighbors which leads to more effective embedding learning. In order to verify the effectiveness of our proposed model, we conduct extensive experiments on three standard benchmark datasets, e.g., WN18RR, FB15k-237 and YAGO-10-DR. Experimental results show that the proposed RGTN achieves the promising results and even outperforms other state-of-the-art models on the KGRL task (e.g., compared to other state-of-the-art GNN-based methods, our model achieves 2.5% improvement using H@10 on WN18RR, 1.2% improvement using H@10 on FB15k-237 and 6% improvement using H@10 on YAGO3-10-DR).

Promoting Mask-Wearing in COVID-19 Brand Communications: Effects of Gain-Loss Frames, Self- or Other-Interest Appeals, and Perceived Risks

During the COVID-19 pandemic, many businesses displayed signs to promote preventive behaviors, such as wearing masks in public, but the effectiveness of this practice largely remains unknown. This research investigates the interaction effect between gain-loss framing and self-other-interest appeals on individual issue judgment of mask-wearing in public. Additionally, it examines the moderating role of perceived situational risks on such interaction effects. Three experiments show that in low-risk public environments, loss-framed (vs. gain-framed) messages are more persuasive when appealing to the interests of socially distant entities (i.e., family and friends in study 1 and 2, community in study 3), and gain-framed (vs. loss-framed) messages are more persuasive when appealing to the interests of socially proximal entities (i.e., self in study 2). However, in high-risk public environments, gain-framed messages are always more persuasive than loss-framed messages, especially when appealing to the interests of socially distant entities (i.e., community in study 3). Theoretical and practical implications, limitations, and future research directions are discussed.

Extracting Biomedical Entity Relations using Biological Interaction Knowledge.

Discovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rules-based and deep learning approaches rely on plenty of manual annotations, which is laborious, time-consuming and unsatisfactory. It is necessary to be able to combine available annotated gene databases, chemical, genomic, clinical and other types of data repositories as domain knowledge to assist the extraction of biological entity relations from numerous literatures. Under this scenario, this paper proposes BioGraphSAGE model, a Siamese graph neural network with structured databases as domain knowledge to extract biological entity relations from literatures. Our model combines both biological semantic features and positional features to improve the recognition of relations between distant entities in the same literature. The experiment results show that BioGraphSAGE achieves the best F1 score among other relation extraction models on smaller annotated samples. Moreover, the proposed model can still maintain a F1 score of 0.526 without using annotated training samples.

Laser-Induced Electron Transfer in the Dissociative Multiple Ionization of Argon Dimers.

We report on an experimental and theoretical study of the ionization-fragmentation dynamics of argon dimers in intense few-cycle laser pulses with a tagged carrier-envelope phase. We find that a field-driven electron transfer process from one argon atom across the system boundary to the other argon atom triggers subcycle electron-electron interaction dynamics in the neighboring atom. This attosecond electron-transfer process between distant entities and its implications manifests itself as a distinct phase-shift between the measured asymmetry of electron emission curves of the Ar^{+}+Ar^{2+} and Ar^{2+}+Ar^{2+} fragmentation channels. This letter discloses a strong-field route to controlling the dynamics in molecular compounds through the excitation of electronic dynamics on a distant molecule by driving intermolecular electron-transfer processes.

Designing Public Information Campaigns as an Effective Policy Tool: Construal-Level Fit Effects and Evidence from an Experimental Study

Applying the construal-level theory, this study compares two different recycling campaigns to analyze the effects of the interaction between social distance and message orientation on citizens’ (policy-takers) responses to public information campaign messages. The results of an experimental study show that messages focused on high-level (why-laden) features were more persuasive when the messages were framed in terms of socially distant entities (the planet/Earth), while messages focused on low-level (how-laden) features were more effective when asking participants to make judgments about their proximal entities (their country/local community). Policy implications for designing public information campaigns as a policy tool are discussed.

A Middle-range Theory of Legislation in a Globalizing World

It is a common truth that legislation, despite being the source of most of modern law, has never been the subject of deeper reflection on the part of the legal world. In particular, one of the central phases of law-making, namely the transformation of political directives into the legal discourse within legislative law-making processes (i.e. the transformation of politics into statutory provisions), has garnered little attention in the legal debate. Several reasons for such “dis-interest” can be mentioned: the dominance of (a distorted version of) legal positivist paradigms in legal thinking; or a vision of democracy (also distorted) as the builder of a massive wall between the making of the law. In recent decades, many such institutional conditions have changed. However, legislative studies have come up against another significant barrier that is becoming a central topic for legal scholarship and practices: the globalization of the law. The circulation of legal models around the world, applied as valid law (regardless of whether these are formally inserted in the various legal systems), has widened the gap between the process of creation of laws and their concrete implementation by the legal actors. Nowadays, the legal models valid in a certain legal system are often produced in legislative (and law-making) processes taking place in foreign and distant countries and/or supranational entities. Secondly, the globalization of the law has contributed to the fading of the nation state as primary regulator and thus a weakening of the nation state’s major regulatory tool, legislative law-making. Finally, due to the progressive specialization of law and (even more importantly) the globalization of law (and the consequent weakening of the traditional dogmas of “democratic” and national parliament-based law-making), there has been a process of increasing the distance between actors operating at the macro-level (e.g. political actors and political thinkers) and actors working at the micro-level (e.g. legislative drafters and legislative studies scholars). The purpose of this work is to offer a way to establish a link between these two levels (macro/politics and micro/drafters): a middle-range theory of legislation. The goal of this theory is to offer a structure capable of channeling the messages coming from the political world (e.g. as expressed in national and/or international assemblies) into viable and concrete legislative products (e.g. functioning legislative provisions).

The Role of Regulatory Focus, Social Distance, and Involvement in Anti-High-Risk Drinking Advertising: A Construal-Level Theory Perspective

The present study examines the effects of regulatory focus, social distance, and involvement interplay on responses to anti-high-risk advertising messages. Results indicate that when asked to make judgments for distant entities, individuals are more persuaded by a promotion-focused frame in terms of ad attitudes, and responsible drinking attitudes and intentions, whereas there are no differential framing effects on judgments associated with proximal entities. The findings suggest the moderating effects of social distance on regulatory focus consistent with construal-level theory. In addition, in examining boundary conditions the results reveal that the construal-level effects are dependent on individuals’ involvement levels.

Calling out to the Faraway: Accessing History through Public Gestures in Howrah, West Bengal

This article narrates events, performances and gestures from public life on the rural-urban corridors of Howrah District, on the west bank of the river Hooghly, in southern West Bengal. Howrah is a place of decadent industrial activity, owing much of its spatial organization to colonial manufacture and trade on the edges of the colonial capital of Calcutta. Public figures in Howrah attempt to lift a place and an associated public out of its current political and historical anonymity. Each uses public gesture to appropriate distant phenomena, quite far-fetched and tenuously linked to their immediate existence. Such public gesture emerges as a key tool for gaining historical luminosity. Their words, gestures and stances are used to access history and historical significance, linking the historical charge of distant entities to immediate worlds, and aiding access to larger scale in space and time.

Abundances of microRNAs in human cells can be estimated as a function of the abundances of YRHB and RHHK tetranucleotides in these microRNAs as an ill-posed inverse problem solution

Mature microRNAs (miRNAs) are small endogenous non-coding RNAs 18–25 nt in length. They program the RNA Induced Silencing Complex (RISC) to make it inhibit either messenger RNAs or promoter DNAs. We have found that the mean abundance of miRNAs in Arabidopsis is correlated with the abundance of DRYD tetranucleotides near the 3′-end and the abundance of WRHB tetranucleotides in the center of the miRNA sequence. Based on this correlation, we have estimated miRNA abundances in seven organs of this plant, namely: inflorescences, stems, siliques, seedlings, roots, cauline, and rosette leaves. We have also found that the mean affinity of miRNAs for two proteins in the Argonaute family (Ago2 and Ago3) in man is correlated with the abundance of YRHB tetranucleotides near the 3′-end and that the preference of miRNAs for Ago2 is correlated with the abundance of RHHK tetranucleotides in the center of the miRNA sequence. This allowed us to obtain statistically significant estimates of miRNA abundances in human embryonic kidney cells, HEK293T. These findings in relation to two taxonomically distant entities (man and Arabidopsis) fit one another like pieces of a jigsaw puzzle, which allowed us to heuristically generalize them and state that the miRNA abundance in the human brain may be determined by the abundance of YRHB and RHHK tetranucleotides in these miRNAs.

Analogy and dynamic geometry system used to introduce three-dimensional geometry

We present a sequence of classroom activities on Euclidean geometry, both plane and space geometry, used to make three dimensional geometry more catchy and simple. The activity consists of a guided research activity that leads the students to discover unexpected properties of two apparently distant geometrical entities, quadrilaterals and tetrahedra. The activity has been realised by means of an efficacious conceptual tool, the analogy, and an operative one, a dynamic geometry system.

A Treatment Effect Method for Merger Analysis with an Application to Parking Prices in Paris

Most retrospective merger studies resort to the treatment effect approach, comparing the price dynamics in a treatment group and in a control group. We propose a systematic method to construct the groups, which applies to any industry with spatial competition. The method is consistent with the fact that mergers alter oligopolistic equilibria in complex ways, and thus that seemingly distant entities may be affected through indirect channels. An illustration based on a merger in the Parisian parking market is provided.

Spontaneous trait inference and construal level theory: Psychological distance increases nonconscious trait thinking

Can psychological distance affect how much perceivers form spontaneous trait inferences (STI) from others’ behaviors? On the basis of construal level theory (CLT) which posits that distant (vs. near) entities are represented more in terms of their abstract, global, and decontextualized features, we predicted that perceived distance would increase the tendency for perceivers to draw spontaneous trait inferences from behavioral information about actors. In two experiments, participants learned about people who were perceived as being distant or proximal to the self, and STI formation was subsequently assessed. We found that perceivers were more likely to form STIs about distant vs. near actors from the same behavioral information. These findings generalized across two distance dimensions: space and time. In addition, we found that priming individuals to adopt a high-level (vs. low-level) construal mindset also resulted in increased STI (Experiment 3). In sum, psychological distance facilitates STI formation, and this occurs via high-level construal of actors and their behaviors.

Managing the tensions in integrating global organisations: The role of performance management systems

The paper explores the role of performance management systems (PMSs) in integrating global organisations (GOs). It investigates the ways in which the diverse entities that comprise a GO can be co-ordinated and integrated to achieve a global unity of effort, while leaving space for local adaptation, differentiation and flexibility. The business literature on GOs highlights several tensions that characterise processes of integration: vertical vs. lateral relations; standardization vs. differentiation of practices; centralization vs. decentralization of decision making. Interpreting PMS as a series of practices that carry organisational knowledge and rationales across organisations, the paper relies on the case of Nestlé Waters to study how PMS can be used to manage the tensions involved in integrating GOs. On the one hand, PMS can be used to re-shape existing boundaries by supporting the development of new organisational arrangements, where standardization and centralization around shared strategies are achieved. On the other hand, PMS can become trapped by the contradictory forces emanating from multiple centres of decision making. Importantly, when attempting to bind together distant entities in the same “organisational space”, informal control mechanisms may become relevant, but they have the capacity to both reinforce and damage the potential of PMS in managing the tensions involved in integrating GOs.

The Neoliberal Seduction: Governing-at-a-Distance, Community Development and the Battle over Financial Services Provision in Australia

This paper is concerned with understanding the reasons for the apparent success of neoliberalism: why the model of the ‘entrepreneurial, self-reliant community’ has been adopted so widely and readily across Australia. It does this through an analysis of two events in the restructuring of financial services provision in regional Australia during the 1990s and 2000s: the John Laws/Australian Bankers’ Association ‘cash for comment’ affair, and the rise of ‘alternative’ financial service providers in the wake of the major trading banks’ financial service withdrawal programmes of the 1990s. This analysis is conducted using the conceptual toolkit of the governmentality literature. In this context, the paper explores the notion of translation — how authorities, agencies, etc., exert control over distant entities, whether these entities be branch staff or a far-flung consumer market. In examining the often fragile character of ‘governing-at-a-distance’ in modern forms of rule, it is argued that some recent advances in the ‘geography of power’ have much to offer in highlighting both the important roles played by space and scale in the execution of power in its various guises and the ways in which resistance to the more regressive features of neoliberal philosophy and policy may best focus.

"Defeated by the Age": On Ambiguity in Quevedo's "Mire los muros de la patria mia"

en la esteril regi6n de nuestros afios (Villamediana) to N the notes that follow, the sonnet Mire los mu, ros de la patria mia is studied in the context of Quevedo's moral thought, and in the light of some of its English translations. I hope to show that I several of its images are more ambiguous and far richer than has previously been supposed. It ?' has often been observed that this sonnet focuses the reader's attention first on large, distant entities (muros, patria) and then on small, immediate ones (casa, espada). In analyzing the poem, I shall do the opposite. Let us begin by elucidating the meaning of a single image in the earliest known version of the poem.