Types Of Representations Research Articles

Vector representations and unit vector representations of fields: Problems of understanding and possible teaching strategies

Vector fields are a highly abstract physical concept that is often taught using visualizations. Although vector representations are particularly suitable for visualizing quantitative data, they are often confusing, especially when describing real fields such as magnetic and electric fields, as the vector arrows can overlap. The present study investigates vector understanding at the end of secondary education. In particular, the extent to which the geometry of the field can be derived from conventional unit vector representations and representations with centered unit vectors was examined. To support this understanding, two exercises were compared. The unirepresentational exercise argued within the conventional unit vector representation, while the multirepresentational exercise attempted to support the link between centered and conventional unit vectors. The results show that almost all test subjects solved the items for generating vector representations correctly, but significant difficulties were encountered in interpreting vector representations. Drawing and interpreting vector representations therefore appear to be different skills that should be practiced intensively and in an integrated way. Various problems could be identified when interpreting vector representations. For example, the number of vectors is often erroneously used to estimate the strength of the field, although more vectors per surface element actually only increase the resolution of the representation. Here, however, the results suggest that the longitudinal density and the transverse density of the drawn vectors are perceived differently by the learners. Furthermore, the learners recognized the field’s geometry much more readily from centered unit vectors than from conventional unit vectors. Errors occur especially when interpreting the geometry of conventional unit vector representations of rotational fields and fields containing both sources and sinks while the geometries of fields containing only sinks were interpreted quite well. The comparison between the two training exercises showed that a promising approach to deepen students’ understanding would be to use an exercise that contrasts conventional and centered unit vector representations and explains how to translate from one representation to the other, rather than describing the main elements of only a single representation. Finally, based on the results of the study, we propose a strategy for teaching vector representations in schools. Given the significantly improved readability of the representation with centered unit vectors, the results even raise the question of whether this type of representation could possibly replace the conventional representation in textbooks and learning materials in the future. Published by the American Physical Society 2024

Working memory limitations constrain visual episodic long-term memory at both specific and gist levels of representation.

Limitations in one's capacity to encode information in working memory (WM) constrain later access to that information in long-term memory (LTM). The present study examined whether these WM constraints on episodic LTM are limited to specific representations of past episodes or also extend to gist representations. Across three experiments, young adult participants (n = 40 per experiment) studied objects in set sizes of two or six items, either sequentially (Experiments 1a and 1b) or simultaneously (Experiment 2). They then completed old/new recognition tests immediately after each sequence (WM tests). After a long study phase, participants completed LTM conjoint recognition tests, featuring old but untested items from the WM phase, lures that were similar to studied items at gist but not specific levels of representation, and new items unrelated to studied items at both specific and gist levels of representation. Results showed that LTM estimates of specific and gist memory representations from a multinomial-processing-tree model were reduced for items encoded under supra-capacity set sizes (six items) relative to within-capacity set sizes (two items). These results suggest that WM encoding capacity limitations constrain episodic LTM at both specific and gist levels of representation, at least for visual objects. The ability to retrieve from LTM each type of representation for a visual item is contingent on the degree to which the item could be encoded in WM.

The radical of functorially finite subcategories

Let Λ be an artin algebra and C be a functorially finite subcategory of mod Λ which contains Λ or DΛ. We use the concept of the infinite radical of C and show that C has an additive generator if and only if radC∞ vanishes. In this case we describe the morphisms in powers of the radical of C in terms of its irreducible morphisms. Moreover, under a mild assumption, we prove that C is of finite representation type if and only if any family of monomorphisms (epimorphisms) between indecomposable objects in C is noetherian (conoetherian). Also, by using injective envelopes, projective covers, left C-approximations and right C-approximations of simple Λ-modules, we give other criteria to describe whether C is of finite representation type. In addition, we give a nilpotency index of the radical of C which is independent from the maximal length of indecomposable Λ-modules in C.

Reflexive Modules on Normal Gorenstein Stein Surfaces, Their Deformations and Moduli

In this paper we generalize Artin-Verdier, Esnault and Wunram construction of McKay correspondence to arbitrary Gorenstein surface singularities. The key idea is the definition and a systematic use of a degeneracy module, which is an enhancement of the first Chern class construction via a degeneracy locus. We study also deformation and moduli questions. Among our main result we quote: a full classification of special reflexive MCM modules on normal Gorenstein surface singularities in terms of divisorial valuations centered at the singularity, a first Chern class determination at an adequate resolution of singularities, construction of moduli spaces of special reflexive modules, a complete classification of Gorenstein normal surface singularities in representation types, and a study on the deformation theory of MCM modules and its interaction with their pullbacks at resolutions. For the proof of these theorems it is crucial to establish several isomorphisms between different deformation functors, that we expect that will be useful in further work as well.

Assessing the Cross-linguistic Utility of Abstract Meaning Representation

Abstract Semantic representations capture the meaning of a text. Abstract Meaning Representation (AMR), a type of semantic representation, focuses on predicate-argument structure and abstracts away from surface form. Though AMR was developed initially for English, it has now been adapted to a multitude of languages in the form of non-English annotation schemas, cross-lingual text-to-AMR parsing, and AMR-to-(non-English) text generation. We advance prior work on cross-lingual AMR by thoroughly investigating the amount, types, and causes of differences that appear in AMRs of different languages. Further, we compare how AMR captures meaning in cross-lingual pairs versus strings, and show that AMR graphs are able to draw out fine-grained differences between parallel sentences. We explore three primary research questions: (1) What are the types and causes of differences in parallel AMRs? (2) How can we measure the amount of difference between AMR pairs in different languages? (3) Given that AMR structure is affected by language and exhibits cross-lingual differences, how do cross-lingual AMR pairs compare to string-based representations of cross-lingual sentence pairs? We find that the source language itself does have a measurable impact on AMR structure, and that translation divergences and annotator choices also lead to differences in cross-lingual AMR pairs. We explore the implications of this finding throughout our study, concluding that, although AMR is useful to capture meaning across languages, evaluations need to take into account source language influences if they are to paint an accurate picture of system output, and meaning generally.

Chimpanzees (Pan troglodytes) recognize that their guesses could be wrong and can pass a two-cup disjunctive syllogism task.

When chimpanzees search for hidden food, do they realize that their guesses may not be correct? We applied a post-decision wagering paradigm to a simple two-cup search task, varying whether we gave participants visual access to the baiting and then asking after they had chosen one of the cups whether they would prefer a smaller but certain reward instead of their original choice (experiment 1). Results showed that chimpanzees were more likely to accept the smaller reward in occluded than visible conditions. Experiment 2 found the same effect when we blocked visual access but manipulated the number of hiding locations for the food piece, showing that the effect is not owing to representation type. Experiments 3 and 4 showed that when given information about the contents of the unchosen cup, chimpanzees were able to flexibly update their choice behaviour accordingly. These results suggest that language is not a pre-requisite to solving the disjunctive syllogism and provides a valuable contribution to the debate on logical reasoning in non-human animals.

The use of portable abdominal bioimpedance analyzer Yscope in the assessment of abdominal obesity.

Obesity, especially abdominal obesity, is strongly correlated with metabolic and other health risks. Diagnosis and assessment of obesity is important in clinical and routine preventive practice. From the point of view of risk, it is necessary to distinguish not only the area of fat tissue accumulation, but also its type. The aim of the study was to use a new portable abdominal bioimpedance analyzer, which is intended for the area of abdominal adipose tissue, as part of the evaluation of the body structure of a selected group of volunteers with a focus on the differentiation of subcutaneous and visceral adipose tissue and to assess its usefulness in practice. Body composition was analyzed using a portable abdominal bioimpedance analyzer Yscope (PA-BIA) in combination with a bioimpedance device InBody 970 (high-frequency bioelectrical impedance/HF-BIA). Eighty-three volunteers at the age of 24.92±7.24 years with representation of both sexes participated in the study. Abdominal fat did not differ significantly between the sexes, women reached an average value of 2.01±1.14 kg, men 2.22±1.60 kg (p>0.05). Gender differentiation was manifested in the case of visceral fat (p<0.01) and visceral fat area (p<0.01), the values of which were lower in women than in men. In the case of subcutaneous fat, we found the opposite trend of values in relation to gender, where lower values were achieved by men, but there were no significant differences (p>0.05). Visceral fat was most correlated with abdominal fat (r=0.86) and waist circumference (r=0.85), subcutaneous fat had the strongest positive correlations with abdominal fat (r=0.93) and with body fat mass (r=0.93). PA-BIA in combination with HF-BIA makes it possible to determine the representation of subcutaneous and visceral fat in the abdominal area, which the conventional MFS-BIA method does not allow. When evaluating body composition, significant gender differentiation is confirmed, which is an important factor affecting different health risks related to gender and the representation of different types of fat tissue localized and accumulated in different parts of the body.

GEUKE: A geographic entities uniformly explicit knowledge embedding model

AbstractKnowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.

#1344 Molecular signatures of assembloids generated from stem cell derived nephron progenitors and ureteric bud cells as potential kidney organoid therapy

Abstract Background and Aims Novel therapies are needed to address the unmet clinical need of patients suffering from end stage kidney disease (ESKD) and cell therapy is an innovative strategy to deliver effective, curative treatment options to renal patients. There are several reports that have developed protocols to differentiate human pluripotent stem cells (hPSCs) into kidney-lineage progenitor derived organoid models and we have developed a robust and scalable method to produce kidney organoids. Method hPSCs were used to generate nephron progenitor cells (NPCs) and ureteric bud cells (UB) through previously described stem cell differentiation protocols. NPCs on day 9 of differentiation and UB on day 9 were combined for 2 days at 3:1 ratio respectively, to form ‘assembloids’ in-vitro and implanted in-vivo for 2 weeks. We used kidney capsule implantation in a NOD background mouse model and studied integration and phenotype using micro-CT and immunohistochemistry. Results RNA sequencing, flow cytometry and histology of NPCs, UB and assembloids in vitro shows broad representation of renal cell types by expression of markers representing several cell populations present in the developing nephron. We can currently show in-vitro molecular signatures of NPC derived organoids versus assembloids, with superiority of UB derived structures such as connecting tubules and intercalated cells. In-vivo histological examination of implanted assembloids showed maturation of human renal compartments such as collecting ducts, distal tubules as well as glomeruli-like structures. Micro-CT analysis showed integration with host and host derived endothelial vascularisation. Conclusion These observations demonstrate the use and differentiation potential of hPSCs and organoids in a pre-clinical setting towards more functional like kidney compartments by combining different progenitor populations. More work is needed evaluating these and other renal progenitor populations for future efforts to examine efficacy studies to develop regenerative therapies for ESKD.

Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model

Deep learning models adapted from natural language processing offer new opportunities for the prediction of active compounds via machine translation of sequential molecular data representations. For example, chemical language models are often derived for compound string transformation. Moreover, given the principal versatility of language models for translating different types of textual representations, off-the-beaten-path design tasks might be explored. In this work, we have investigated generative design of active compounds with desired potency from target sequence embeddings, representing a rather provoking prediction task. Therefore, a dual-component conditional language model was designed for learning from multimodal data. It comprised a protein language model component for generating target sequence embeddings and a conditional transformer for predicting new active compounds with desired potency. To this end, the designated “biochemical” language model was trained to learn mappings of combined protein sequence and compound potency value embeddings to corresponding compounds, fine-tuned on individual activity classes not encountered during model derivation, and evaluated on compound test sets that were structurally distinct from training sets. The biochemical language model correctly reproduced known compounds with different potency for all activity classes, providing proof-of-concept for the approach. Furthermore, the conditional model consistently reproduced larger numbers of known compounds as well as more potent compounds than an unconditional model, revealing a substantial effect of potency conditioning. The biochemical language model also generated structurally diverse candidate compounds departing from both fine-tuning and test compounds. Overall, generative compound design based on potency value-conditioned target sequence embeddings yielded promising results, rendering the approach attractive for further exploration and practical applications.Scientific contributionThe approach introduced herein combines protein language model and chemical language model components, representing an advanced architecture, and is the first methodology for predicting compounds with desired potency from conditioned protein sequence data.

Facial representations of complex affective states combining pain and a negative emotion.

Pain is rarely communicated alone, as it is often accompanied by emotions such as anger or sadness. Communicating these affective states involves shared representations. However, how an individual conceptually represents these combined states must first be tested. The objective of this study was to measure the interaction between pain and negative emotions on two types of facial representations of these states, namely visual (i.e., interactive virtual agents; VAs) and sensorimotor (i.e., one's production of facial configurations). Twenty-eight participants (15 women) read short written scenarios involving only pain or a combined experience of pain and a negative emotion (anger, disgust, fear, or sadness). They produced facial configurations representing these experiences on the faces of the VAs and on their face (own production or imitation of VAs). The results suggest that affective states related to a direct threat to the body (i.e., anger, disgust, and pain) share a similar facial representation, while those that present no immediate danger (i.e., fear and sadness) differ. Although visual and sensorimotor representations of these states provide congruent affective information, they are differently influenced by factors associated with the communication cycle. These findings contribute to our understanding of pain communication in different affective contexts.

Bi-syntax guided transformer network for aspect sentiment triplet extraction

Aspect Sentiment Triplet Extraction is an emerging and challenging task that attempts to present a complete picture of aspect-based sentiment analysis. Prior research efforts mostly leverage various tagging schemes to extract the three elements in a triplet. However, these methods fail to explicitly model the complicated relations between aspects and opinions and the boundaries of multi-word aspects and opinions. In this paper, we propose a bi-syntax guided transformer network in an end-to-end manner to address these challenges. Firstly, we devise three types of representations, including sequence distance representation, constituency distance representation, and dependency distance representation, to learn the comprehensive language representation. Specifically, sequence distance representation utilizes sequence distance between words to enhance the contextual representation. Constituency distance representation adopts constituency distance between words in a constituency tree to capture the intra-span relation between words. Dependency distance representation employs dependency distance between words in a dependency tree to capture the long-distance relation between aspects and opinions. Extensive experiments are conducted on four benchmark datasets to validate the effectiveness of our method. The results demonstrate that the proposed approach achieves better performance than baseline methods. We conduct further detailed analysis to demonstrate that our method effectively handles multi-word terms and overlapping triplets.

Cultivating change in food consumption practices: The reception of the social representation of alternative proteins by consumers

This article is concerned with the dynamics of change in protein consumption practices from the perspective of the consumer. It is based on a model, informed by social representation theory, that aims to understand the role played by various types of representation of alternative proteins in the process of changing food consumption practices. It discusses the reception, by consumers, of the representations associated with alternative proteins on Instagram. Methodologically, three focus groups were organized with different consumer segments (omnivorous, flexitarian and vegetarian and vegan consumers), as well as seven individual interviews. Participants were submitted to the social representations of alternative proteins, and visual stimuli from social media were mobilized for this purpose. Results show that the publications which boast the environmental, animal welfare or health attributes of alternative proteins generally contribute to the cultivation of new elements of practices. While this kind of publications is essential to help consumers question their established practices linked to meat and dairy consumption, they can also generate a critical reception that is not conducive to change, making them a double-edge sword. Publications that relate to the representations involved in daily food consumption proteins (e.g. that alternative proteins are versatile and crowd-pleasing) emerge as being safer in terms of reception, although as standalone they may not be able to achieve a deep level of change in food consumption practices. The results of this study show the importance of deploying a diverse communication strategy about alternative proteins that appeal to a variety of consumer segments.

Electoral Systems in Sudan: In Quest for an Appropriate Type

This article reviews the types of electoral systems applied in Sudan since independence – in both democratic and military rules. It goes beyond that to touch on the general features of electoral behavior as well. It employs historical and descriptive approaches to examine some facts pertaining to the reality of elections and hindrances to democracy in the Sudan: &amp;lt;i&amp;gt;First&amp;lt;/i&amp;gt;, the democratic process has been obstructed several times by military coups and regimes. &amp;lt;i&amp;gt;Second,&amp;lt;/i&amp;gt; children who grew up under al-Bashir&amp;apos;s Militant Islamic Regime (MIR) were deprived form exercising democracy or know anything about voting. &amp;lt;i&amp;gt;Third,&amp;lt;/i&amp;gt; the lack of continual political participation hindered the process of accumulating political awareness that could enrich the formation of political culture. &amp;lt;i&amp;gt;Fourth,&amp;lt;/i&amp;gt; the tribal influence in the socio-political life in many parts of the country, had negatively affected the political behavior of the ordinary people as well as the politicians. This political reality shatters any hope for proper democracy to work in the country. This essay argues that for such pluralism and ethno-cultural multiplicity entails the adoption of consensual democracy. However, it believes that this consociationalism should be supported by the Proportional Representation type of elections –along with federalism and presidential system.

Critical Linguistics of The Crimea Bridge Explosion on The Guardian and CNN Online News

This research focuses on analyzing the types of grammar and ideological representations in the news texts of The Guardian and CNN online news using the Critical Linguistics analysis model of Roger Fowler et.al (1979). Then the Discourse Historical Approach from Ruth Wodak (2001) is used to know the historical background of this topic. This research uses a descriptive qualitative method to find out what types of grammar are contained in the news text and how the grammar represents the ideology of the news. The explosion occurred as a result of the conflict between Russia and Ukraine. This topic has attracted public attention, therefore, online news media such as The Guardian and CNN are discussing it. At this time online news media plays an important role in disseminating information, but in fact the language used in online news media can contain certain ideologies that benefit one group and harm other groups. Based on the results of Critical Linguistics analysis from Roger Fowler et.al (1979), it is found that the ideology of the two online media is anti-Russian and pro-Ukrainian. The findings of this study show that there are 28 types of grammar in The Guardian and 17 grammatical strategies in CNN. The type of grammar that often appears from both media is transitivity grammar. In representing their ideology, both online media often use negative lexical choices to give negative references to the Russian side. Future researchers can develop their analysis by using different theoretical studies and objects.

Brauer Analysis of Some Cayley and Nilpotent Graphs and Its Application in Quantum Entanglement Theory

Cayley and nilpotent graphs arise from the interaction between graph theory and algebra and are used to visualize the structures of some algebraic objects as groups and commutative rings. On the other hand, Green and Schroll introduced Brauer graph algebras and Brauer configuration algebras to investigate the algebras of tame and wild representation types. An appropriated system of multisets (called a Brauer configuration) induces these algebras via a suitable bounded quiver (or bounded directed graph), and the combinatorial properties of such multisets describe corresponding indecomposable projective modules, the dimensions of the algebras and their centers. Undirected graphs are examples of Brauer configuration messages, and the description of the related data for their induced Brauer configuration algebras is said to be the Brauer analysis of the graph. This paper gives closed formulas for the dimensions of Brauer configuration algebras (and their centers) induced by Cayley and nilpotent graphs defined by some finite groups and finite commutative rings. These procedures allow us to give examples of Hamiltonian digraph constructions based on Cayley graphs. As an application, some quantum entangled states (e.g., Greenberger–Horne–Zeilinger and Dicke states) are described and analyzed as suitable Brauer messages.

Bridging the Chasm Between Cognitive Representations and Formal Structures of Linguistic Meanings.

This paper aims to show that properties of cognitive/conceptual representations and formal-logical structures of linguistic meaning can be inter-translated, recast, transformed into one another, and so united together, even though cognitive/conceptual representations and formal-logical structures of linguistic meaning are apparently distinct in ontology and divergent in their form or character. While cognitive/conceptual representations are ultimately rooted in sensory-motor systems, formal-logical structures of linguistic meaning are abstractions detached from and independent of the actualized world. This paper sketches out the foundations of how representations of linguistic meaning in terms of cognitive/conceptual structures in Cognitive/Conceptual Semantics can be unified with those in terms of formal-logical structures in Formal Semantics. This is done by recasting cognitive/conceptual representations in terms of formal-logical structures of linguistic meaning and re-encoding formal-logical structures of linguistic meaning in terms of cognitive/conceptual representations. Then, these two types of semantic representations, thus shown representationally equivalent, will be related to a series of derivations across levels in neuronal networks and dynamics. The general discussion on unifying cognitive/conceptual representations of linguistic meaning with formal-logical structures is contextualized within the broader context of theorizing in cognitive science.

Avicenna on representation: towards an existential-relational account of intentionality

ABSTRACT Many scholars consider Avicenna’s theory of cognitive forms a theory of representation, which raises two questions. First, why does a cognitive form represent a particular object instead of another? This issue is known as the determination problem. Second, what is the nature of intentionality of the cognitive form? This is known as the nature problem. This paper examines Avicenna’s theory of cognitive forms and focuses on how he would address the two problems. I argue that Avicenna offers a pluralistic approach to the determination problem: depending on the type of representation, the object of representation is determined differently. Additionally, Avicenna views the nature of intentionality as a referential relation towards the object that can be attributed to the representation in virtue of the object somehow existing in the subject immaterially.

LGBTQ+ individuals are not explicitly represented in emergency medicine simulation curricula

Background Medical educational societies have emphasized the inclusion of marginalized populations, including the lesbian, gay, bisexual, transgender and queer (LGBTQ+) population, in educational curricula. Lack of inclusion can contribute to health inequality and mistreatment due to unconscious bias. Little didactic time is spent on the care of LGBTQ+ individuals in emergency medicine (EM) curricula. Simulation based medical education can be a helpful pedagogy in teaching cross-cultural care and communication skills. In this study, we sought to determine the representation of the LGBTQ+ population in EM simulation curricula. We also sought to determine if representations of the LGBTQ+ population depicted stigmatized behavior. Methods We reviewed 971 scenarios from six simulation case banks for LGBTQ+ representation. Frequency distributions were determined for major demographic variables. Chi-Squared or Fisher’s Exact Test, depending on the cell counts, were used to determine if relationships existed between LGBTQ+ representation and bank type, author type, and stigmatized behavior. Results Of the 971 scenarios reviewed, eight (0.82%) scenarios explicitly represented LGBTQ+ patients, 319 (32.85%) represented heterosexual patients, and the remaining 644 (66.32%) did not specify these patient characteristics. All cases representing LGBTQ+ patients were found in institutional case banks. Three of the eight cases depicted stigmatized behavior. Conclusions LGBTQ+ individuals are not typically explicitly represented in EM simulation curricula. LGBTQ+ individuals should be more explicitly represented to reduce stigma, allow EM trainees to practice using gender affirming language, address health conditions affecting the LGBTQ+ population, and address possible bias when treating LGBTQ+ patients.

Problem solving in mathematics education: tracing its foundations and current research-practice trends

In tracing recent research trends and directions in mathematical problem-solving, it is argued that advances in mathematics practices occur and take place around two intertwined activities, mathematics problem formulation and ways to approach and solve those problems. In this context, a problematizing principle emerges as central activity to organize mathematics curriculum proposals and ways to structure problem-solving learning environments. Subjects’ use of concrete, abstract, symbolic, or digital tools not only influences the ways to pose and pursue mathematical problems; but also shapes the type of representation, exploration, and reasoning they engage to work and solve problems. Problem-solving foundations that privilege learners’ development of habits of mathematical practices that involve an inquiry method to formulate conjectures, to look for different ways to represent and approach problems, and to support and communicate results shed light on directions of current research trends and the relevance of rethinking curriculum proposals and extending problem-solving environments in terms of teachers/students’ consistent use of digital tools and online developments.