Part-whole Relations Research Articles

What can the taxonomy of predicative possession in Malwai Punjabi tell us?

Abstract The study aims to examine the syntactic and semantic behaviors of predicative possession (i.e., have-possessive constructions) in Malwai Punjabi, an underdocumented dialect within the Indo-Aryan language family. Data were collected from longitudinal online interviews with native speakers as consultants, with audio recordings for transcribed target sentences. The results revealed that all the alienable possession, either permanent/temporary or abstract/concrete, could be marked by the postposition koḷ ‘near/with’, whereas inalienable possession, such as whole-part relation and kinship, could not be encoded using koḷ. The prototypicality model and schema-based metaphors explained why koḷ was widely used to express alienable possession in Malwai Punjabi. The analysis of companion and proximity schemata also justified the extended semantics of predicative possession, suggesting a metaphorical mapping of accompaniment and location onto possession. From a typological angle, the case study can not only provide further evidence for the existence of split possession but also contribute to a cognitive understanding of predicative possession in relation to other languages.

Understanding Children’s Learning of Part–Whole Relations Using Fingu

AbstractIn this article, we investigate the effects of the app Fingu on a child’s understanding of the part-whole concept. Using the Artifact Centric Activity Theory framework, we initially analyse the internalisation and externalisation processes that appear to occur during one child’s use of the app. Based on the learning from this process, we then use other aspects of the framework, in particular the subject-artifact-group triangle, to understand how Fingu might be used productively in mathematics education classrooms. Using a 13-min video of a child playing Fingu as our data source, we analyse how children might, as they learn about part-whole relationships, interact with the game. We also investigate how design principles in creating apps can impact on the experience of using the app in ways that may be unintended by their designers. This work has implications for app designers and for mathematics educators seeking to incorporate digital technologies into mathematics education.

A scalable synergy-first backbone decomposition of higher-order structures in complex systems

In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.

Are pie charts evil? An assessment of the value of pie and donut charts compared to bar charts

Many data visualization experts recommend the use of bar charts over pie charts because they consider comparing the area or angle of segments to be less accurate than comparing bars on a bar chart. However, many studies show that when the pie chart is used to estimate proportions (arguably its main function) it is as accurate as the bar chart. A major issue is that most previous studies have only looked at one method of extracting information from pie charts, for example either by comparing the segment to the circle (the part-whole relationship) or one segment to another (relative magnitude estimation). Therefore, in this study I test multiple metrics to provide a more holistic assessment of the pie and donut chart against the bar chart. I also measured cognitive load through pupillometry. In summary, bar charts were more precise than pie and donut charts for ranking elements, but all charts were equally accurate for extracting the part-whole relationship. There was little difference in cognitive load between chart types, although bar charts were consistently faster to use on average. Overall, the bar chart was more flexible, but where there were statistically significant differences between charts, the effect sizes were often small, and unlikely to prevent effective extraction of quantitative information. That is, as long as they were used appropriately, all chart types were arguably acceptable for displaying simple, categorical data.

Different understandings of the nature of ‘structural relationships’ in phenomenographic research: views of educational researchers

ABSTRACT Phenomenography, as a qualitative research methodology, is distinguished by its simultaneous focus on investigating (a) variation in ways of experiencing phenomena in the world, and (b) structural relationships that link and separate the different ways of experiencing. The focus on structural relationships is often regarded as the most distinctive and useful aspect of the methodology, but it is also the least well understood aspect. To help explicate the nature of structural relationships in phenomenographic research, this paper reports the outcomes of an empirical study of different understandings of structural relationships amongst educational researchers. Structural relationships were variously described as: (1) hierarchical relationships; (2) hierarchically-inclusive relationships; (3) meaning-structure relationships; (4) part-whole relationships; and (5) multi-faceted relationships. The outcomes of the study highlight a number of critical aspects of the notion of structural relationships that researchers need to become aware of in order to use the methodology in a sophisticated way.

The Effect of Intelligence Games on Primary School 4th Grade Students' Mental Skill Levels

This research was aimed to analyze the effects of intelligence games on the mental skill levels of fourth grade students. The research was carried out in a public school located in Manavgat district of Antalya province. The study group of the research consists of 40 volunteer students (20 in the experimental group, 20 in the control group) studying in two different branches of the 4th grades in the spring term of the 2021-2022 academic year. The research was designed in the form of a pre-test and post-test control group and the quantitative data of the research were collected through the 'Mental Skill Assessment Test' developed by Marangoz (2018). The experimental group was given a total of 16 hours of intelligence games training for eight weeks, two lessons per week (80 minutes). In the research, a valid and reliable ready-made test developed by Marangoz (2018) (Mental Skill Assessment Test) was used to evaluate students' mental skill levels (concentration of attention, strategic thinking, analysing, part-whole relationship, visual perception and use of clues). This test was applied to the experimental and control groups as a pre-test and post-test. The Normality Test was conducted to investigate the normal distribution of the data as a result of the research. In the research, 6 different intelligence games were used for eight weeks. In order to examine the relationship between the pre-test and post-test scores of the experimental and control groups before the application, the dependent sample t-test was performed. In the research, it was observed that there was a significant increase in the mental skills of the experimental group students, who were played 6 different intelligence games for eight weeks. According to the results of the research, we can say that intelligence games improve the mental skills levels of students.

FCPN: Pruning redundant part-whole relations for more streamlined pattern parsing

Cropping-and-segmenting pattern parsers often combine diverse inner correlations into a single metric/scheme, resulting in over-generalizations and redundant representations. It is proposed to streamline pattern parsing by using presenting a redundant association elimination network (RAEN) with capsule attention twisters (CATs) and capsule-attention routing agreement (CARA). CATs trim delicate relationships between parts and wholes that are weak and interchangeable. Senior entities can only be updated by primary entities that meet the requirements of inter-part diversity and intra-object cohesiveness. In order to enhance results, CARA is designed to protect against the unnecessary voting signals of traditional routing protocols. Experiments involving facial and human segmentation show that RAEN is better than current remarkable methods, particularly for defining detailed semantic boundaries.

The Way We Divide Forms ’in Our Soul’: Conceived Parthood at Plato’s Sophist 250b8

Abstract What does Plato mean when he declares at Soph. 250b8 that Theaetetus is positing Being in his soul (ἐν τῇ ψυχῇ τιθείς) as a third something encompassing Change and Rest? Is he merely clarifying that the act of positing is a mental act? Or is he making a further point? This paper argues that the locution ‘in the soul’ plays a significant role in the passage in alerting to a contrast between the way Being and its relation to Change and Rest are intelligible to Theaetetus and the way Being really is and relates to those two other kinds. This interpretation is set against another interpretation, defended by Leigh, according to which the phrase should be understood as drawing a contrast between what is done by the agency of the soul and what is done by the agency of the body. The paper then explores the consequences of the proposed interpretation for our understanding of the broader context of the passage. In particular, it argues that the claim that Being is a third something encompassing Change and Rest is more problematic than critics have usually assumed. To account for Theaetetus’ depiction, it develops the notion of ‘conceived parthood’, which are part-whole relations posited by the mind for the needs of a philosophical enquiry.

Acts, occasions and multiplicatives: A mereotopological account

In this paper, I argue for the relevance of structured part-whole configurations in the domain of events. The evidence comes from the well-known event-internal/external distinction, which concerns mutliplicative adverbials quantifying either over separate occasions or occasion-internal acts, respectively (e.g., Cusic 1981, Andrews 1983, Cinque 1999, Zhang 2017). In order to capture this distinction, I postulate that the relationship between the two categories is based on a part-whole relation. In particular, inspired by proposals advocating the role of eventive higher-order units (Landman 2006, Henderson 2017) and building on the theories of (Grimm 2012) and (Mazzola 2019), I propose to extend mereotopology to the domain of events. I argue that this allows for capturing acts as simplex events conceptualized as bounded integrated MSSC wholes, whereas occasions as clusters, i.e., temporally structured configurations, of such simplex events.

Hierarchical Graph Capsule Networks for Molecular Function Classification With Disentangled Representations.

In biochemistry, graph structures have been widely used for modeling compounds, proteins, functional interactions, etc. A common task that divides these graphs into different categories, known as graph classification, highly relies on the quality of the representations of graphs. With the advance in graph neural networks, message-passing-based methods are adopted to iteratively aggregate neighborhood information for better graph representations. These methods, though powerful, still suffer from some shortcomings. The first challenge is that pooling-based methods in graph neural networks may sometimes ignore the part-whole hierarchies naturally existing in graph structures. These part-whole relationships are usually valuable for many molecular function prediction tasks. The second challenge is that most existing methods do not take the heterogeneity embedded in graph representations into consideration. Disentangling the heterogeneity will increase the performance and interpretability of models. This paper proposes a graph capsule network for graph classification tasks with disentangled feature representations learned automatically by well-designed algorithms. This method is capable of, on the one hand, decomposing heterogeneous representations to more fine-grained elements, whilst on the other hand, capturing part-whole relationships using capsules. Extensive experiments performed on several public-available biochemistry datasets demonstrated the effectiveness of the proposed method, compared with nine state-of-the-art graph learning methods.

The MTSK and TSD: didactic planning as a proposal for the learning of rational numbers in fifth grade primary school pupils

This research shows how, based on an analysis proposal based on the MTSK (Mathematics Teacher Specialized Knowledge) (Carrillo, Montes, Contreras, & Climent, 2017), related to the use of the TSD didactic device (Theory of the didactic situations), it was possible to favor the learning of the content of rational numbers, specifically from the part-whole relationship sub construct and the understanding of the same in students of the 5th grade of a primary school. The results show a greater mastery of the content in question and with it, the students' learning advanced towards different levels of complexity through the established representations and relationships.

Parametric modeling and fabrication as capturing knowledge: A design computation workflow for historical brick surfaces in Anatolia

Using computational design tools to create meaningful digital representations of architectural heritage delivers both challenges and opportunities. On one hand, digital tools aid the fast and detailed three-dimensional modeling of architectural elements. On the other hand, these models do not sufficiently document the materials and techniques of making. This research proposes a workflow to use computational design tools to analyze historic Anatolian brick elements while integrating their geometry, construction, and part-whole relations in parametric modeling and robotic fabrication processes. Our approach demonstrates a correlation between the design of the surface pattern and material application in historical bricklaying. The proposed workflow can be applied to formalize implicit design knowledge, integrating it into the digital environment and numeric control production codes. This holistic approach to heritage prioritizes both the tangible aspects, such as form and material, and intangible aspects such as the knowledge base of applied techniques.

Cumulative Part-Whole Relations as a Verbal and Visual Rhetorical Strategy: Song of Songs 7 and Composite Beings of the Southern Levant

In biblical studies, a growing interest in metaphors as the conceptual key to the ancient mind has arisen. However, in contrast with the popularity of metaphor, other tropes have received little attention. This article sheds light on another rhetorical feature: ‘cumulative part-whole relations,’ which construct a new whole by compounding disparate body parts, a textual and visual strategy deliberately used to cross boundaries of individual parts, to integrate the parts into a larger whole, and to demonstrate superiority and supernatural powers. Through examining visual and textual examples from the ancient Near East, including Song 7.1–6 and the composite creatures ( Mischwesen) from the Levant, this article reveals that the rhetorical mechanism of the cumulation of part-whole relations follows the logic that the whole is more than the sum of its parts, while that whole remains a single one no matter how many parts are compounded.

MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction

Background and ObjectiveProtein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include antibody therapeutics, vaccines, and drug discovery. The problem of sequence-based PPI prediction has been a long-standing issue in computational biology. MethodsWe introduce MaTPIP, a cutting-edge deep-learning framework for predicting PPI. MaTPIP stands out due to its innovative design, fusing pre-trained Protein Language Model (PLM)-based features with manually curated protein sequence attributes, emphasizing the part-whole relationship by incorporating two-dimensional granular part (amino-acid) level features and one-dimensional whole-level (protein) features. What sets MaTPIP apart is its ability to integrate these features across three different input terminals seamlessly. MatPIP also includes a distinctive configuration of Convolutional Neural Network (CNN) with Transformer components for concurrent utilization of CNN and sequential characteristics in each iteration and a one-dimensional to two-dimensional converter followed by a unified embedding. The statistical significance of this classifier is validated using McNemar's test. ResultsMaTPIP outperformed the existing methods on both the Human PPI benchmark and cross-species PPI testing datasets, demonstrating its immense generalization capability for PPI prediction. We used seven diverse datasets with varying PPI target class distributions. Notably, within the novel PPI scenario, the most challenging category for Human PPI Benchmark, MaTPIP improves the existing state-of-the-art score from 74.1% to 78.6% (measured in Area under ROC Curve), from 23.2% to 32.8% (in average precision) and from 4.9% to 9.5% (in precision at 3% recall) for 50%, 10% and 0.3% target class distributions, respectively. In cross-species PPI evaluation, hybrid MaTPIP establishes a new benchmark score (measured in Area Under precision-recall curve) of 81.1% from the previous 60.9% for Mouse, 80.9% from 56.2% for Fly, 78.1% from 55.9% for Worm, 59.9% from 41.7% for Yeast, and 66.2% from 58.8% for E.coli. Our eXplainable AI-based assessment reveals an average contribution of different feature families per prediction on these datasets. ConclusionsMaTPIP mixes manually curated features with the feature extracted from the pre-trained PLM to predict sequence-based protein-protein association. Furthermore, MaTPIP demonstrates strong generalization capabilities for cross-species PPI predictions.

Agency, Goal-Directed Behavior, and Part-Whole Relationships in Biological Systems

In this essay we aim to present some considerations regarding a minimal but concrete notion of agency and goal-directed behavior that are useful for characterizing biological systems at different scales. These considerations are a particular perspective, bringing together concepts from dynamical systems, combinatorial problem-solving, and connectionist learning with an emphasis on the relationship between parts and wholes. This perspective affords some ways to think about agents that are concrete and quantifiable, and relevant to some important biological issues. Instead of advocating for a strict definition of minimally agential characteristics, we focus on how (even for a modest notion of agency) the agency of a system can be more than the sum of the agency of its parts. We quantify this in terms of the problem-solving competency of a system with respect to resolution of the frustrations between its parts. This requires goal-directed behavior in the sense of delayed gratification, i.e., taking dynamical trajectories that forego short-term gains (or sustain short-term stress or frustration) in favor of long-term gains. In order for this competency to belong to the system (rather than to its parts or given by its construction or design), it can involve distributed systemic knowledge that is acquired through experience, i.e., changes in the organization of the relationships among its parts (without presupposing a system-level reward function for such changes). This conception of agency helps us think about the ways in which cells, organisms, and perhaps other biological scales, can be agential (i.e., more agential than their parts) in a quantifiable sense, without denying that the behavior of the whole depends on the behaviors of the parts in their current organization.

Introduction: the limits of the explanatory potential of the alienability contrast

Abstract This introductory article outlines the central topic of the present special issue, viz. the contrast between alienable and inalienable possession, and how this contrast is reflected in the grammars and lexicons of natural languages. It sketches the historical background of the alienability contrast in the linguistic literature and points to a number of biases that need to be overcome in order to (1) advance our understanding of the contrast and (2) face the limits of its explanatory potential. Specifically, the present introduction, as well as the contributions to the present issue, proposes to move beyond prototypical possessive relationships (ownership, part-whole, and kinship relations), prototypical possessor categories (human possessors) as well as prototypical possessee categories (artifacts, body-parts and kin). In addition, the issue contains three contributions dealing with Amazonian languages, thus filling in an important gap in previous crosslinguistic studies on possession. The data presented in the special issue show that many morphosyntactic phenomena that have been explained in terms of the alienability contrast – or are amenable to such explanations – cannot be reduced to it, and are sometimes even better described without recourse to alienability at all. The present article thus concludes that the alienability contrast is at best regarded as a heuristic tool in exploring linguistic data, and cautions that, if used as the only explanatory principle, it could actually hamper an adequate description of the data.

FCPN: Pruning redundant part-whole relations for more streamlined pattern parsing

Most cropping-and-segmenting pattern parsers typically establish a single metric/scheme to reason diverse inner correlations, resulting in over-general and redundant representations. To make pattern parsing more streamlined and efficient, a fragile correlation pruner network (FCPN) with correlation-steered attention shifters (CSASs) and graph attention expectation-maximum routing agreement (GAEMRA) is proposed. CSASs prune fragile (weak and substitutable) part-to-whole correlations. They stipulate that only those primary entities (representing components) fulfilling the criteria of inter-part diversity and intra-object cohesiveness can update senior entities (representing the whole/intermediate composites). To further boost effects, GAEMRA is defined to shield the redundant voting signals of conventional routing agreement. With CSASs and GAEMRA, FCPN gradually parses objective semantic patterns by clustering highly associated secondary entities in a bottom-up "part backtracking" manner. Quantitative and ablation experiments surrounding face and human parsing demonstrate the superiority of FCPN over the state-of-the-arts, especially for the definition of fine-grained semantic boundaries.

Deep unsupervised part-whole relational visual saliency

Deep Supervised Salient Object Detection (SSOD) excessively relies on large-scale annotated pixel-level labels which consume intensive labour acquiring high quality labels. In such precondition, deep Unsupervised Salient Object Detection (USOD) draws public attention. Under the framework of the existing deep USOD methods, they mostly generate pseudo labels by fusing several hand-crafted detectors’ results. On top of that, a Fully Convolutional Network (FCN) will be trained to detect salient regions separately. While the existing USOD methods have achieved some progress, there are still challenges for them towards satisfactory performance on the complex scene, including (1) poor object wholeness owing to neglecting the hierarchy of those salient regions; (2) unsatisfactory pseudo labels causing by unprimitive fusion of hand-crafted results. To address these issues, in this paper, we introduce the property of part-whole relations endowed by a Belief Capsule Network (BCNet) for deep USOD, which is achieved by a multi-stream capsule routing strategy with a belief score for each stream within the CapsNets architecture. To train BCNet well, we generate high-quality pseudo labels from multiple hand-crafted detectors by developing a consistency-aware fusion strategy. Concretely, a weeding out criterion is first defined to filter out unreliable training samples based on the inter-method consistency among four hand-crafted saliency maps. In the following, a dynamic fusion mechanism is designed to generate high-quality pseudo labels from the remaining samples for BCNet training. Experiments on five public datasets illustrate the superiority of the proposed method. Codes have been released on: https://github.com/Mirlongue/Deep-Unsupervised-Part-Whole-Relational-Visual-Saliency.

From research to prototypes: Developing a digital game to foster fraction equivalence.

Fraction understanding is a significant predictor of future math achievement. In addition to more traditional instruction, digital game-based approaches have been found successful in facilitating conceptual understanding of fractions (including aspects such as part-whole relations represented as pie-charts, or measurement, i.e., magnitude in terms of number lines). Nevertheless, the number of existing games that address the aspect of fraction equivalence is limited and if so, these typically replicate common paper-and-pencil tasks like identifying and matching equivalent fractions. This, however, hardly takes advantage of the medium’s potential and affordances nor strives for intrinsic integration of the learning content with game mechanics. Seeking to overcome this shortcoming, the present article describes the development of a digital game fostering fraction equivalence. To do so, among others, we developed visual animations illustrating the transitions from circular part-whole representations (pie-charts) to magnitudes on a number line—drawing on previous research and games. In particular, a wheel is presented, whose circumference matches the length of the number line which it rolls along. Players can manipulate the number of spikes on the wheel reflecting the number of parts of the whole (e.g., 2 spikes halve the circle, while 4 cut it into quarters etc.). By rolling the wheel, the spikes puncture the number line thus segmenting it into equal parts, essentially transforming the aspects from part-whole relations to measurement (magnitude) on the number line. Additionally, as punctures of 2 and 4 spikes will coincide at the 1/2 - 2/4 location of a 0-1 number line, players can find equivalent fractions. The game under development will be evaluated with late primary/early secondary school students to test its game design, affordances, and possible impact on fraction understanding.

Multi-focus image fusion using structure-guided flow

Existing deep learning based methods have shown their advantages in multi-focus image fusion task. However, most methods still suffer from inaccurate focus region detection. In this paper, we employ the property of part-whole relationships embedded by the Capsule Network (CapsNet) to solve the problem. Specifically, we introduce CapsNet in multi-focus image fusion task, and design a structure-guided flow module, which fully utilizes structure information to help locate focus regions. CapsNet is introduced to extract structure features by supervising gradient information of the image. Compared with traditional convolutional neural networks (CNNs), CapsNet takes into account the correlation of features from different positions, such that it encodes more compact features. Once structure features are obtained, a flow alignment module is introduced to learn flow field between structure and image features, and propagate effectively structure features to image features to make confident focus region detection. Experimental results show the proposed method achieves robust fusion performance on three publicly available multi-focus datasets, and outperforms or is comparable to the state-of-the-art methods.