Relevant bibliographies by topics / Broad Structural Representation Learning

Academic literature on the topic 'Broad Structural Representation Learning'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Broad Structural Representation Learning"

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Worden, Keith, and Graeme Manson. "The application of machine learning to structural health monitoring." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1851 (December 12, 2006): 515–37. http://dx.doi.org/10.1098/rsta.2006.1938.

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In broad terms, there are two approaches to damage identification. Model-driven methods establish a high-fidelity physical model of the structure, usually by finite element analysis, and then establish a comparison metric between the model and the measured data from the real structure. If the model is for a system or structure in normal (i.e. undamaged) condition, any departures indicate that the structure has deviated from normal condition and damage is inferred. Data-driven approaches also establish a model, but this is usually a statistical representation of the system, e.g. a probability density function of the normal condition. Departures from normality are then signalled by measured data appearing in regions of very low density. The algorithms that have been developed over the years for data-driven approaches are mainly drawn from the discipline of pattern recognition, or more broadly, machine learning. The object of this paper is to illustrate the utility of the data-driven approach to damage identification by means of a number of case studies.
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Arney, Noah D., and Hilary P. Krygsman. "Work-Integrated Learning Policy in Alberta: A Post-Structural Analysis." Canadian Journal of Educational Administration and Policy, no. 198 (February 17, 2022): 97–110. http://dx.doi.org/10.7202/1086429ar.

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In late 2020 the Government of Alberta’s Ministry of Advanced Education sent a guidance document to Alberta post-secondary institutions to lay out how work-integrated learning was to be conducted. This document also informed the institutions that work-integrated learning should be included in all future program proposals. The guidelines were sent without the context or purpose stated. This paper applies Carol Bacchi’s “What’s the Problem Represented to be” post-structural policy discourse analysis to the Ministry of Advanced Education guidelines. There is a broad consensus in work-integrated learning research that work-integrated learning is beneficial for participants beyond employment outcomes. However, this analysis shows the Ministry of Advanced Education’s representation of the problem displays an assumption that the purpose of work-integrated learning is to improve labour market outcomes. The analysis also spotlights that the likely effects of the policy have more to do with making work-integrated learning programs easier to assess than to improve student education. This paper proposes an alternative framework that would integrate the constructivist and humanistic origin of work-integrated learning and allow institutions to develop appropriate experiential learning components for their programs while still standardizing work-integrated learning components across and within institutions. This proposed framework can improve work-integrated learning programs in Canada by widening the focus beyond human capital theory.
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Quintana, Rafael. "The ecology of human behavior: A network perspective." Methodological Innovations 15, no. 1 (March 2022): 42–61. http://dx.doi.org/10.1177/20597991221077911.

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There is a broad agreement that some of the most relevant problems in the social and behavioral sciences are fundamentally structural, and as a consequence require structural explanations. Yet researchers disagree on what a structural explanation is, and what are the specific questions that can only be answered through a structural lens. In this study, I shed some light on the nature of structural explanations by distinguishing between three types of structural questions related to structural proximity, structural cohesion and structural importance. In addition, I show how graphical methods can be used to answer these questions. In particular, I argue that structure learning algorithms can help us gain some understanding regarding causal structures, and network science can help us understand the organization of these structures. I provide an empirical application of these methods using a nationally representative dataset with a wide range of factors related to child development.
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Bingman, Verner P., and Rubén N. Muzio. "Reflections on the Structural-Functional Evolution of the Hippocampus: What Is the Big Deal about a Dentate Gyrus." Brain, Behavior and Evolution 90, no. 1 (2017): 53–61. http://dx.doi.org/10.1159/000475592.

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The vertebrate hippocampal formation has been central in discussions of comparative cognition, nurturing an interest in understanding the evolution of variation in hippocampal organization among vertebrate taxa and the functional consequences of that variation. Assuming some similarity between the medial pallium of extant amphibians and the hippocampus of stem tetrapods, we propose the hypothesis that the hippocampus of modern amniotes began with a medial pallium characterized by a relatively undifferentiated cytoarchitecture, more direct thalamic and olfactory sensory inputs, and a broad role in associative learning and memory processes that nonetheless included the map-like representation of space. From this modest beginning evolved the cognitively more specialized hippocampal formation of birds and the hippocampus of mammals with its confounding dentate gyrus. Much has been made of trying to identify a dentate homologue in birds, but there are compelling reasons to believe no such structural homologue/functional equivalent exists. The uniqueness of the mammalian dentate then raises the question of what might be the functional consequences of a hippocampus with a dentate compared to one without. One might be tempted to speculate that the presence of a dentate gyrus facilitates so-called pattern separation, but birds with their suspected dentate-less hippocampus display excellent hippocampal-dependent pattern separation relying on space. Perhaps one consequence of a dentate is a hippocampus better designed to process a broader array of stimuli beyond space to more robustly support episodic memory. What is clear is that any meaningful reconstruction of hippocampal evolution and the eventual identification of any subdivisional homologies will require more data on the neurobiological and functional properties of the nonmammalian hippocampus, particularly those of amphibians and reptiles.
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Singh, Ajay, Harman Preet Singh, Fakhre Alam, and Vikas Agrawal. "Role of Education, Training, and E-Learning in Sustainable Employment Generation and Social Empowerment in Saudi Arabia." Sustainability 14, no. 14 (July 19, 2022): 8822. http://dx.doi.org/10.3390/su14148822.

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This study analyzes the role of education, training, and e-learning (ETL) in empowering Saudi society, leading to sustainable employment generation in Saudi Arabia. It applies the theory of constructivism, scoping to human aspects of teaching and learning in sustainable employment generation and social empowerment. The study primarily collects the existing variable pools from the available literature on education (EDU), training (TRA), e-learning (ELRN), government policies (GPOL), national culture (NCUL), sustainable employment generation (SUEG), and social empowerment (SEMP). The study performs second-order partial least squares structural equation modeling (PLS-SEM) with moderation analysis. The study aims to obtain the combined effect of ETL on SUEG and SEMP in the presence of GPOL and NCUL in Saudi Arabia. Primarily, the results of the path diagram show that ETL has a significant direct impact on SEMP and SUEG. Secondly, the moderation analysis results show that GPOL has been a significant moderator between ETL and SUEG and ETL and SEMP. In contrast, the analysis results show that the NCUL is not a significant moderator between ETL and SUEG, or between ETL and SEMP. Additionally, the moderation analysis results show that NCUL directly impacts SEMP. In contrast, it does not show a significant direct relationship with SUEG. In the article, the theory of constructivism emphasizes the learners’ active role in constructing knowledge, which is significant for both individuals and society, and the validity of constructed knowledge and its realistic representation in the real world. The practical implementation of the education and e-learning approach of constructivism will help to bridge the gap between the skilled workforce in Saudi Arabia and the rest of the world. Moreover, the students, as learners, will be able to assert their experiences by connecting with the outside world, constructing a sustainable society, leading to sustainable employment generation and social empowerment in Saudi Arabia. The study also has a broad scope for higher educational institutions, training centers, and organizations in Saudi Arabia and the rest of the world.
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McConaghy, Cathryn. "on Pedagogy, Trauma and Difficult Memory: Remembering Namatjira, our Beloved." Australian Journal of Indigenous Education 32 (2003): 11–20. http://dx.doi.org/10.1017/s1326011100003781.

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AbstractOne of the projects engaged in within the text Rethinking Indigenous Education (RIE) (McConaghy, 2000) was an analysis of the colonial regimes that are reproduced within Indigenous education, often despite our emancipatory intentions. Through a detailed critique of the various competitions for epistemic authority in the field, the book explores the structural processes by which certain knowledges are legitimated as “truths” and the material and symbolic effects of these.The focus of the book was on the imagined worlds of various traditions of knowing Indigenous education and their claims to authority. It was a “how” rather than a “who” story that dealt with theoretical assumptions, broad-brush policy and curriculum inquiry and that attempted to avoid the identity politics that had gripped Indigenous education for more than a decade. Importantly the book also suggested that rather than being cumulative, critique is a process that needs to be ongoing, done again and again. This paper, Remembering Namatjira, has sought to move beyond the main projects of RIE, many of them structural in nature, to an analysis of more intimate aspects of Indigenous education. It addresses some of the “who” issues, not in terms of representation politics, who can know and speak what, but in terms of the psychic difficulties that we attach to knowledge in Indigenous education. Whereas RIE drew upon postcolonial and feminist insights, this paper considers the contribution of psychoanalysis to thinking through some of the more intractable issues that remain unexamined or underexamined in the field. Among the issues addressed are the fundamental dilemmas around our ambivalences in education; the notion of pedagogical force (and transferences, resistances and obstacles to learning); the work of ethical witnessing; and issues of difficult knowledge, or knowledge and memories that we cannot bear to know. Central to the work of rethinking Indigenous education again, in moving beyond deconstruction, is the process of making meaning out of the ruins of our lovely knowledges (Britzman, 2003), our comfort knowledges, about what should be done in Indigenous education.
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Orynbaikyzy, Aiym, Ursula Gessner, Benjamin Mack, and Christopher Conrad. "Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies." Remote Sensing 12, no. 17 (August 27, 2020): 2779. http://dx.doi.org/10.3390/rs12172779.

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Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) data enables a broad representation of biophysical and structural information on target objects, enhancing crop type mapping. However, the fusion of multi-sensor dense time-series data often comes with the challenge of high dimensional feature space. In this study, we (1) evaluate how the usage of only optical, only SAR, and their fusion affect the classification accuracy; (2) identify the combination of which time-steps and feature-sets lead to peak accuracy; (3) analyze misclassifications based on the parcel size, optical data availability, and crops’ temporal profiles. Two fusion approaches were considered and compared in this study: feature stacking and decision fusion. To distinguish the most relevant feature subsets time- and variable-wise, grouped forward feature selection (gFFS) was used. gFFS allows focusing analysis and interpretation on feature sets of interest like spectral bands, vegetation indices (VIs), or data sensing time rather than on single features. This feature selection strategy leads to better interpretability of results while substantially reducing computational expenses. The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets. Similar to most other studies, the optical-SAR combination outperformed single sensor predictions. No significant difference was recorded between feature stacking and decision fusion. Random Forest (RF) appears to be robust to high feature space dimensionality. The feature selection did not improve the accuracies even for the optical-SAR feature stack with 320 features. Nevertheless, the combination of RF feature importance and time- and variable-wise gFFS rankings in one visualization enhances interpretability and understanding of the features’ relevance for specific classification tasks. For example, by enabling the identification of features that have high RF feature importance values but are, in their information content, correlated with other features. This study contributes to the growing domain of interpretable machine learning.
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Fasoulis, Romanos, Georgios Paliouras, and Lydia E. Kavraki. "Graph representation learning for structural proteomics." Emerging Topics in Life Sciences 5, no. 6 (October 19, 2021): 789–802. http://dx.doi.org/10.1042/etls20210225.

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The field of structural proteomics, which is focused on studying the structure–function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storing increasing amounts of protein structural data, in addition to modeled structures becoming increasingly available. This, combined with the recent advances in graph-based machine-learning models, enables the use of protein structural data in predictive models, with the goal of creating tools that will advance our understanding of protein function. Similar to using graph learning tools to molecular graphs, which currently undergo rapid development, there is also an increasing trend in using graph learning approaches on protein structures. In this short review paper, we survey studies that use graph learning techniques on proteins, and examine their successes and shortcomings, while also discussing future directions.
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Li, Cheng-Te, and Hong-Yu Lin. "Structural Hierarchy-Enhanced Network Representation Learning." Applied Sciences 10, no. 20 (October 16, 2020): 7214. http://dx.doi.org/10.3390/app10207214.

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Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should be pushed together and away in the embedding space, nor model coarse-grained community knowledge hidden behind the network topology. In this paper, we propose a novel NRL framework, Structural Hierarchy Enhancement (SHE), to deal with such two issues. The main idea is to construct a structural hierarchy from the network based on community detection, and to utilize such a hierarchy to perform level-wise NRL. In addition, lower-level node embeddings are passed to higher-level ones so that community knowledge can be aware of in NRL. Experiments conducted on benchmark network datasets show that SHE can significantly boost the performance of NRL in both tasks of NC and LP, compared to other hierarchical NRL methods.
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Romero, Lisa S. "Trust, behavior, and high school outcomes." Journal of Educational Administration 53, no. 2 (April 13, 2015): 215–36. http://dx.doi.org/10.1108/jea-07-2013-0079.

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Purpose – The purpose of this paper is to contribute to the literature on student trust and to examine the relationship between student trust, behavior, and academic outcomes in high school. It asks, first, does trust have a positive effect on high school outcomes? Second, does trust influence student behavior, exerting an indirect effect on schooling outcomes? Third, are school size and student socioeconomic status (SES) antecedents of trust? Design/methodology/approach – A nationally representative sample of students attending public high schools in the USA (n=10,585) is drawn from the Educational Longitudinal Study. Structural equation modeling is used to examine the relationship between student trust, behavior and high school outcomes, controlling for SES, school size and prior achievement. Multiple measures of academic achievement are considered. Findings – There is a significant relationship between student trust, behavior and high school outcomes. Students who trust have fewer behavioral incidents and better academic outcomes with results suggesting that trust functions through behavior. This is true regardless of SES, school size or prior achievement. Practical implications – School leaders cannot change parental income or education, but can build trust. Developing and attending to student trust may not only mean that students are better behaved but, more importantly, are more successful academically. Social implications – In spite of decades of policy and legislation intended to improve schools, closing the achievement gap has proven elusive. One reason may be the relentless focus on physical artifacts of schooling, such as school organization, curriculum, testing and accountability, and a concomitant lack of attention to sociocognitive factors key to learning. Schools are social systems, and high levels of learning are unlikely to occur without a nurturing environment that includes trust. Originality/value – This research makes a valuable contribution by focussing on student trust in high schools and by illuminating the relationship between trust, behavior, and academic outcomes. Results suggest that trust impacts a broad range of high school outcomes but functions indirectly through behavior.
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Dissertations / Theses on the topic "Broad Structural Representation Learning"

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Bratu, Claudia. "Machine Learning of Crystal Formation Energies with Novel Structural Descriptors." Thesis, Linköpings universitet, Teoretisk Fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143203.

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To assist technology advancements, it is important to continue the search for new materials. The stability of a crystal structures is closely connected to its formation energy. By calculating the formation energies of theoretical crystal structures it is possible to find new stable materials. However, the number of possible structures are so many that traditional methods relying on quantum mechanics, such as Density Functional Theory (DFT), require too much computational time to be viable in such a project. A presented alternative to such calculations is machine learning. Machine learning is an umbrella term for algorithms that can use information gained from one set of data to predict properties of new, similar data. Feature vector representations (descriptors) are used to present data in an appropriate manner to the machine. Thus far, no combination of machine learning method and feature vector representation has been established as general and accurate enough to be of practical use for accelerating the phase diagram calculations necessary for predicting material stability. It is important that the method predicts all types of structures equally well, regardless of stability, composition, or geometrical structure. In this thesis, the performances of different feature vector representations were compared to each other. The machine learning method used was primarily Kernel Ridge Regression, implemented in Python. The training and validation were performed on two different datasets and subsets of these. The representation which consistently yielded the lowest cross-validated error was a representation using the Voronoi tessellation of the structure by Ward et. al. [Phys. Rev. B 96, 024104 (2017)]. Following up was an experimental representation called the SLATM representation presented by Huang and von Lilienfeld [arXiv:1707.04146], which is partially based on the Radial Distribution Function. The Voronoi representation achieved an MAE of 0.16 eV/atom at 3534 training set size for one of the sets, and 0.28 eV/atom at 10086 training set size for the other set. The effect of separating linear and non-linear energy contributions was evaluated using the sinusoidal and Coulomb representations. The result was that separating these improved the error for small training set sizes, but the effect diminishes as the training set size increases. The results from this thesis implicate that further work is still required for machine learning to be used effectively in the search for new materials.
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Ladhams, Zieba Meagan. "Teaching and learning about reaction mechanisms in organic chemistry." University of Western Australia. School of Biomedical and Chemical Sciences, 2004. http://theses.library.uwa.edu.au/adt-WU2005.0035.

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[Truncated abstract] This study was carried out to investigate the teaching and learning processes occurring in the topic of reaction mechanisms in three tertiary level organic chemistry courses and focussed on investigating perceptions about the importance of teaching and learning about reaction mechanisms and about the difficult aspects of the topic .... In the organic chemistry courses under investigation, students achieved many of the explicitly stated aims that their lecturers identified. The students rarely achieved implicit outcomes anticipated by the lecturer. Lecturers demonstrate a tendency to use particular structural representations when discussing certain types of reaction process. The study identified that students commonly use these same types when working through particular reaction processes. In addition, it was found that the use of a particular structure could cue students into thinking about only one type of reaction process taking place in a given reaction. The use of language that is consistent with a consideration of only single reaction particles was also commonly observed in lectures. While this can be adequate in some circumstances, other aspects of reaction processes are better considered in terms of multiple reaction particles ... The project proposes an integrated model, which takes into account the many levels (macroscopic, single particle molecular, multiple particle molecular and intramolecular) involved when describing reaction processes. It is felt that a consideration of the levels discussed in this model is useful when teaching and learning about reaction mechanisms.
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Matta, Corrado. "A Field of Veiled Continuities : Studies in the Methodology and Theory of Educational Research." Doctoral thesis, Stockholms universitet, Institutionen för pedagogik och didaktik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-140475.

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Empirical educational research enjoys a methodological and theoretical debate that is characterized by a number of unresolved and lively debated controversies. This compilation thesis is an attempt to contribute to this debate using the toolbox of philosophy of science. The thesis consists of an introductory chapter and four essays. In the introductory chapter I identify three methodological and theoretical controversies that are discussed within the field of educational research. These are: 1) the controversy concerning the scientific status of educational research; 2) the controversy between cognitive and sociocultural theories of learning; and, 3) the controversy between realist and constructionist interpretations of theories of learning. I provide in the essays a critical assessment of the claims behind each of these controversies, and argue for an alternative reconstruction of these issues. In Essay I, I criticize a view about the interpretation of human action, labeled in the text as interpretivism. This view posits a sharp separation between the natural and social sciences, to the effect that the methods of the latter cannot be applied to the former. The first controversy seems to rest on this position. As I argue, the arguments in support of interpretivism are contradicted by actual research practice. I conclude that the interpretivistic claims lack support and that the general separation claim appears as problematic. A further debate has fueled the first controversy, that is, the supposed distinction between qualitative and quantitative methods. In Essay II, I argue against this distinction. More specifically, I discuss the concept of empirical support in the context of qualitative methods (for short, qualitative support). I provide arguments that although there are two specific and non-trivial properties of qualitative support, there is no methodological separation between quantitative and qualitative methods concerning empirical support. Considered together, the first two essays indicate two points of methodological continuity between educational research and other scientific practices (such as the natural sciences). I therefore conclude that the controversy concerning the scientific status of educational research rests in large part on unjustified claims. Essay III focuses on the second controversy. In this article I argue that Suárez’ inferential approach to the concept of scientific representation can be used as an account of scientific representation in learning, regardless of whether learning is understood as a cognitive or social phenomenon. The third controversy is discussed in Essay IV. Here, I discuss some ontological aspects of the framework of the actor-network theory. Reflecting on the use of this framework in the research field of Networked Learning, I argue that the assumption of an ontology of relations provides the solution for two puzzles about the ontology of networks. The relevance of my argument for the third controversy is that it suggests a point of connection between constructionist and realist interpretations of the ontology of learning. The last two essays suggest two points of continuities between theoretical frameworks that have been and still are argued to be incompatible.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.

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(5929691), Asish Ghoshal. "Efficient Algorithms for Learning Combinatorial Structures from Limited Data." Thesis, 2019.

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Recovering combinatorial structures from noisy observations is a recurrent problem in many application domains, including, but not limited to, natural language processing, computer vision, genetics, health care, and automation. For instance, dependency parsing in natural language processing entails recovering parse trees from sentences which are inherently ambiguous. From a computational standpoint, such problems are typically intractable and call for designing efficient approximation or randomized algorithms with provable guarantees. From a statistical standpoint, algorithms that recover the desired structure using an optimal number of samples are of paramount importance.

We tackle several such problems in this thesis and obtain computationally and statistically efficient procedures. We demonstrate optimality of our methods by proving fundamental lower bounds on the number of samples needed by any method for recovering the desired structures. Specifically, the thesis makes the following contributions:

(i) We develop polynomial-time algorithms for learning linear structural equation models --- which are a widely used class of models for performing causal inference --- that recover the correct directed acyclic graph structure under identifiability conditions that are weaker than existing conditions. We also show that the sample complexity of our method is information-theoretically optimal.

(ii) We develop polynomial-time algorithms for learning the underlying graphical game from observations of the behavior of self-interested agents. The key combinatorial problem here is to recover the Nash equilibria set of the true game from behavioral data. We obtain fundamental lower bounds on the number of samples required for learning games and show that our method is statistically optimal.

(iii) Lastly, departing from the generative model framework, we consider the problem of structured prediction where the goal is to learn predictors from data that predict complex structured objects directly from a given input. We develop efficient learning algorithms that learn structured predictors by approximating the partition function and obtain generalization guarantees for our method. We demonstrate that randomization can not only improve efficiency but also generalization to unseen data.

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Books on the topic "Broad Structural Representation Learning"

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Nygaard, Taylor, and Jorie Lagerwey. Horrible White People. NYU Press, 2020. http://dx.doi.org/10.18574/nyu/9781479885459.001.0001.

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At the same time that reactionary conservative political figures like Donald Trump were elected and disastrous socioeconomic policies like Brexit were voted into law, representations of bleakly comic white fragility spread across television screens. Analyzing a cycle of transatlantic television programs that emerged mostly between 2014 and 2016 targeting affluent, liberal, white audiences, Horrible White People examines the complicity of the white Left, obsessed with its own anxiety and suffering, in the rise and maintenance of the Far Right—particularly in the mobilization, representation, and sustenance of structural white supremacy on television. The authors use a combined methodology of media-industry analysis and feminist cultural studies, especially close textual analysis, to interrogate a cycle of US and British programming, like Broad City, Casual, You’re the Worst, Catastrophe, Fleabag, and Transparent, that features the abjection of middle-class, liberal, young white people. Throughout, they put these “horrible white people” in conversation with similar upmarket comedies from creators and casts of color, like Insecure, Atlanta, Dear White People, and Master of None, to highlight the ways those shows negotiate prestige TV’s dominant aesthetics of whiteness to push back against the centering of white suffering in a time of cultural crisis. The authors argue that multiple, concurrent, interrelated crises—recession, the emergent mainstreaming of feminism(s), and the unmasked visibility of racial inequality and violence—have caused upheaval among liberals. These crises are represented in this cycle as a collection of circumstances inextricable from and intertwined with the reactionary conservatism, antifeminism, and racism of the rising Right.
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Papafragou, Anna, John C. Trueswell, and Lila R. Gleitman, eds. The Oxford Handbook of the Mental Lexicon. Oxford University Press, 2022. http://dx.doi.org/10.1093/oxfordhb/9780198845003.001.0001.

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The present handbook is a state-of-the-art compilation of papers from leading scholars on the mental lexicon—the representation of language in the mind/brain at the level of individual words and meaningful sub-word units. In recent years, the study of words as mental objects has grown rapidly across several fields including linguistics, psychology, philosophy, neuroscience, education, and computational cognitive science. This comprehensive collection spans multiple disciplines, topics, theories, and methods, to highlight important advances in the study of the mental lexicon, identify areas of debate, and inspire innovation in the field from present and future generations of scholars. The book is divided into three parts. Part I presents modern linguistic and cognitive theories of how the mind/brain represents words at the phonological, morphological, syntactic, semantic, and pragmatic levels. This part also discusses broad architectural issues pertaining to the organization of the lexicon, the relation between words and concepts, and the role of compositionality. Part II discusses how children learn the form and meaning of words in their native language drawing from the key domains of phonology, morphology, syntax, semantics, and pragmatics. Multiple approaches to lexical learning are introduced to explain how learner- and environment-driven factors contribute to both the stability and the variability of lexical learning across both individual learners and communities. Part III examines how the mental lexicon contributes to language use during listening, speaking, and conversation, and includes perspectives from bilingualism, sign languages, and disorders of lexical access and production.
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Bever, Thomas G. The Unity of Consciousness and the Consciousness of Unity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190464783.003.0005.

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Every language-learning child eventually automatically segments the organization of word sequences into natural units. Within the natural units, processing of normal conversation reveals a disconnect between listener’s representation of the sound and meaning of utterances. A compressed or absent word at a point early in a sequence is unintelligible until later acoustic information, yet listeners think they perceived the earlier sounds and their interpretation as they were heard. This discovery has several implications: Our conscious unified experience of language as we hear and simultaneously interpret it is partly reconstructed in time-suspended “psychological moments”; the “poverty of the stimulus language learning problem” is far graver than usually supposed; the serial domain where such integration occurs may be the “phase,” which unifies the serial percept with structural assignment and meanings; every level of language processing overlaps with others in a “computational fractal”; each level analysis-by-synthesis interaction of associative-serial and structure dependent processes.
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Book chapters on the topic "Broad Structural Representation Learning"

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Cantoni, Virginio, Alessio Ferone, Ozlem Ozbudak, and Alfredo Petrosino. "Protein Structural Blocks Representation and Search through Unsupervised NN." In Artificial Neural Networks and Machine Learning – ICANN 2012, 515–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_64.

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Choi, Jun-Sik, Eunho Lee, and Heung-Il Suk. "Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis." In Machine Learning in Medical Imaging, 64–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00919-9_8.

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Yu, Bin, Xinhang Xu, Chao Wen, Yu Xie, and Chen Zhang. "Hierarchical Graph Representation Learning with Structural Attention for Graph Classification." In Artificial Intelligence, 473–84. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20500-2_39.

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Zheng, Kai, Zhu-Hong You, Lei Wang, Yi-Ran Li, Yan-Bin Wang, and Han-Jing Jiang. "MISSIM: Improved miRNA-Disease Association Prediction Model Based on Chaos Game Representation and Broad Learning System." In Intelligent Computing Methodologies, 392–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26766-7_36.

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Dutta, Saikat, Zixin Huang, and Sasa Misailovic. "SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning." In Fundamental Approaches to Software Engineering, 123–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99429-7_7.

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AbstractProbabilistic programming aims to open the power of Bayesian reasoning to software developers and scientists, but identification of problems during inference and debugging are left entirely to the developers and typically require significant statistical expertise. A common class of problems when writing probabilistic programs is the lack of convergence of the probabilistic programs to their posterior distributions.We present SixthSense, a novel approach for predicting probabilistic program convergence ahead of run and its application to debugging convergence problems in probabilistic programs. SixthSense’s training algorithm learns a classifier that can predict whether a previously unseen probabilistic program will converge. It encodes the syntax of a probabilistic program as motifs – fragments of the syntactic program paths. The decisions of the classifier are interpretable and can be used to suggest the program features that contributed significantly to program convergence or non-convergence. We also present an algorithm for augmenting a set of training probabilistic programs that uses guided mutation.We evaluated SixthSense on a broad range of widely used probabilistic programs. Our results show that SixthSense features are effective in predicting convergence of programs for given inference algorithms. SixthSense obtained Accuracy of over 78% for predicting convergence, substantially above the state-of-the-art techniques for predicting program properties Code2Vec and Code2Seq. We show the ability of SixthSense to guide the debugging of convergence problems, which pinpoints the causes of non-convergence significantly better by Stan’s built-in warnings.
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de las Heras, Lluís-Pere, and Gemma Sánchez. "And-Or Graph Grammar for Architectural Floor Plan Representation, Learning and Recognition. A Semantic, Structural and Hierarchical Model." In Pattern Recognition and Image Analysis, 17–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21257-4_3.

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Tomar, Dimpal, and Pradeep Tomar. "Artificial Intelligence-Based Knowledge Representation and Reasoning." In Impact of AI Technologies on Teaching, Learning, and Research in Higher Education, 134–49. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4763-2.ch008.

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The quality of higher education can be enhanced only by upgrading the content and skills towards knowledge. Hence, knowledge representation and reasoning play a chief role to represent the facts, beliefs, and information, and inferring the logical interpretation of represented knowledge stored in the knowledge bases. This chapter provide a broad overview of knowledge, representation, and reasoning along with the related art of study in the field of higher education. Various artificial intelligent-based knowledge representation and reasoning techniques and schemes are provided for better representation of facts, beliefs, and information. Various reasoning types are discussed in order to infer the right meaning of the knowledge followed by various issues of knowledge representation and reasoning. .
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Mbati, Lydia Sophia. "Capabilities-Based Transformative Online Learning Pedagogy for Social Justice." In Advances in Educational Marketing, Administration, and Leadership, 253–72. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9108-5.ch014.

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This chapter presents pedagogical approaches for online learning with a focus on pedagogies that support interaction and active learning amongst diverse student populations. Understanding the challenges that feed social inequality are broad and complex nexus challenges, the various aspects and activities that go into learning design are discussed from the perspective of fostering equal representation and social justice in the online learning environment. While this chapter provides possible angles that may be employed to facilitate learning in a diverse student population, empirical studies need to be undertaken to test the efficacy of the approaches suggested.
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Mbati, Lydia Sophia. "Capabilities-Based Transformative Online Learning Pedagogy for Social Justice." In Research Anthology on Instilling Social Justice in the Classroom, 1175–94. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7706-6.ch067.

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This chapter presents pedagogical approaches for online learning with a focus on pedagogies that support interaction and active learning amongst diverse student populations. Understanding the challenges that feed social inequality are broad and complex nexus challenges, the various aspects and activities that go into learning design are discussed from the perspective of fostering equal representation and social justice in the online learning environment. While this chapter provides possible angles that may be employed to facilitate learning in a diverse student population, empirical studies need to be undertaken to test the efficacy of the approaches suggested.
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Howson, Camille B. Kandiko, and Ian Kinchin. "Mapping the Doctorate." In Cases on Teaching Critical Thinking through Visual Representation Strategies, 446–65. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5816-5.ch017.

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This chapter reports on the results of a four-year longitudinal study of PhD students and their supervisors, from which the evidence gained suggests that the students tend to focus on the PhD in terms of a product to be completed (in terms of writing a thesis and peer-reviewed journals), whilst the supervisors tend to concentrate more on the process of learning and scientific development, placing the student's contribution into the wider disciplinary discourse. The structural observations from the concept maps generated within this research are that the students perceive the PhD as a linear structure, whereas the supervisors are more likely to generate a cyclic structure to illustrate the dynamic, iterative processes of research more generally. Further structural elements emerge from the analysis of the maps, indicating the need for holistic understanding of the content, structure, and meanings in concept maps and their relationship with safe spaces for the development of critical thinking.
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Conference papers on the topic "Broad Structural Representation Learning"

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Wang, Hu, Guansong Pang, Chunhua Shen, and Congbo Ma. "Unsupervised Representation Learning by Predicting Random Distances." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/408.

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Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretically proven approach to obtain approximately preserved distances. To well predict these distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Empirical results on 19 real-world datasets show that our learned representations substantially outperform a few state-of-the-art methods for both anomaly detection and clustering tasks. Code is available at: \url{https://git.io/RDP}
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Chen, C. L. Philip. "Broad Learning System and its Structural Variations." In 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2018. http://dx.doi.org/10.1109/sisy.2018.8524681.

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Rossi, Ryan A., Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao, and Yasin Abbasi-Yadkori. "A Structural Graph Representation Learning Framework." In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3336191.3371843.

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Quan, Tianhong, Ye Yuan, Youyi Song, Teng Zhou, and Jing Qin. "Fuzzy Structural Broad Learning for Breast Cancer Classification." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761496.

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Quan, Tianhong, Ye Yuan, Youyi Song, Teng Zhou, and Jing Qin. "Fuzzy Structural Broad Learning for Breast Cancer Classification." In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022. http://dx.doi.org/10.1109/isbi52829.2022.9761496.

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Chen, Nenglun, Lingjie Liu, Zhiming Cui, Runnan Chen, Duygu Ceylan, Changhe Tu, and Wenping Wang. "Unsupervised Learning of Intrinsic Structural Representation Points." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00914.

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Liu, Lin, Xin Li, William K. Cheung, and Chengcheng Xu. "A Structural Representation Learning for Multi-relational Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/565.

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Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Freebase demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.
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Carpenter, Justin, Janet Layne, Edoardo Serra, and Alfredo Cuzzocrea. "Detecting Botnet Nodes via Structural Node Representation Learning." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671728.

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Yang, Mengyue, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang. "CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00947.

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Chen, Xiaotian, Yuwang Wang, Xuejin Chen, and Wenjun Zeng. "S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00305.

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Reports on the topic "Broad Structural Representation Learning"

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Ustinova, Viktoriia O., Svitlana V. Shokaliuk, Iryna S. Mintii, and Andrey V. Pikilnyak. Modern techniques of organizing computer support for future teachers’ independent work in German language. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3255.

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The purpose of the study is to elucidate the theoretical and methodological aspects of computer support organization for independent work in a foreign (German) language for future teachers of different subjects. The subject of the study is a methodological technique of organizing effective computer support for future teachers to work independently in a foreign (German) language. Objectives of the study: to state the goals of studying foreign languages in its broad and narrow sense, the requirements for the results of future teachers’ training in different subjects; to explore ways of organizing computer support for future teachers’ independent work; to determine the list and purpose of the basic and auxiliary structural elements of a typical e-learning Moodle course in a foreign language; to provide methodological recommendations for the organization of future teachers’ independent work in the content of a separate training module of the Moodle course “Foreign (German) Language”. The article summarizes the experience of organizing computer support for future teachers’ independent work and the substantive and methodological features of its implementation into the process of experimental introduction of the Moodle course “Foreign (German) Language” into the educational process carried out on the basis of Kryvyi Rih State Pedagogical University.
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