Meaningful Structure Research Articles

A Hebbian Model to Account for Musical Expertise Differences in a Working Memory Task

The TBRS*C computational model provides a mathematical implementation of the cognitive processes involved in complex span tasks. The logic of the core processes, i.e., encoding, refreshing/time-based decay, and chunking, is based on Hebbian learning, synaptic facilitation, and long-term neural plasticity, respectively. The modeling, however, takes place on a cognitive rather than a physiological level. Chunking is implemented as a process of searching for sequences of memoranda in long-term memory and recoding them as a single unit which increases the efficacy of memory maintenance. Using TBRS*C simulations, the present study investigated how chunking and central working memory processes change with expertise. Hobby musicians and music students completed a complex span task in which sequences of twelve note symbols were presented for serial recall of pitch. After the presentation of each memorandum, participants performed an unknown, notated melody on an electric piano. To manipulate the potential for chunking, we varied whether sequences of memoranda formed meaningful tonal structures (major triads) or arbitrary trichords. Hobby musicians and music students were each split up in a higher-expertise and a lower-expertise group and TBRS*C simulations were performed for each group individually. In the simulations, higher-expertise hobby musicians encoded memoranda more rapidly, invested less time in chunk search, and recognized chunks with a higher chance than lower-expertise hobby musicians. Parameter estimates for music students showed only marginal expertise differences. We conclude that expertise in the TBRS model can be conceptualized by a rapid access to long-term memory and by chunking, which leads to an increase in the opportunity and efficacy of refreshing.

Omnipresence of the sensorimotor-association axis topography in the human connectome

Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes.

Bayesian Pyramids: identifiable multilayer discrete latent structure models for discrete data

Abstract High-dimensional categorical data are routinely collected in biomedical and social sciences. It is of great importance to build interpretable parsimonious models that perform dimension reduction and uncover meaningful latent structures from such discrete data. Identifiability is a fundamental requirement for valid modeling and inference in such scenarios, yet is challenging to address when there are complex latent structures. In this article, we propose a class of identifiable multilayer (potentially deep) discrete latent structure models for discrete data, termed Bayesian Pyramids. We establish the identifiability of Bayesian Pyramids by developing novel transparent conditions on the pyramid-shaped deep latent directed graph. The proposed identifiability conditions can ensure Bayesian posterior consistency under suitable priors. As an illustration, we consider the two-latent-layer model and propose a Bayesian shrinkage estimation approach. Simulation results for this model corroborate the identifiability and estimatability of model parameters. Applications of the methodology to DNA nucleotide sequence data uncover useful discrete latent features that are highly predictive of sequence types. The proposed framework provides a recipe for interpretable unsupervised learning of discrete data and can be a useful alternative to popular machine learning methods.

Classification of function-oriented solution elements for MBSE

Many different engineering models are used in the development of technical systems such as the electro-mechanical drive train. The goal of utilising these models is to predict the physical behaviour of the system by virtually testing it. Already existing engineering models have to be identified and reused, to increase the efficiency of the development process. The model-based systems engineering (MBSE) approach motego [1, 2] supports the development of mechanical systems in a holistic and function-oriented manner. The system solution and the solution element are core elements in motego, that realise specific functions. The solution element contains the principle solution and domain models (e.g. engineering models), while the system solution contains solution elements and domain models. The system solution and especially the solution element provides a meaningful structure for organising and reusing models.However, in literature those solution elements are not identified and structured yet. This is also true for the electro-mechanical drive train, which is used as case for this study. It is assumed the solution elements can be identified by analysing the interaction between active surfaces of parts in contact. This contribution proposes a classification procedure for solution elements, which is based on elementary functions and principle solutions as they are elements of established development methods.

Transactional-governance structures:new cross-country data and an application to the effect of uncertainty

AbstractTo what extent are personal trust, mutual interests, and third parties important in enforcing agreements to trade? How do firms combine these to form transactional-governance structures? This article answers these questions in a whole-economy, cross-country setting that considers a full spectrum of transactional-governance strategies. The data collection requires a new survey question answerable in any context. The question is applied in six South American countries using representative samples, with the resultant survey weights facilitating a whole-economy analysis. Without imposing an a priori model, latent class analysis estimates meaningful governance structures. Bilateralism is always used. Law is never used alone. Bilateralism and formal institutions are rarely substitutes. Within country, inter-regional variation in governance is greater than inter-country variation. The usefulness of the data is shown by testing one element of Williamson’s discriminating-alignment agenda: greater uncertainty in the transactional environment increases the involvement of third parties.

Meaning in life in adolescents with developmental trauma: A qualitative study

Aim:The purpose of this study was to explore how adolescent patients displaying developmental trauma experience and describe meaning in life. Schnell’s model of meaning in life is applied to explore meaningfulness, crises of meaning and sources of meaning. Method: The study has a qualitative design based on individual interviews with eight adolescents aged 14–18 years in treatment in an outpatient clinic for mental health care for children and adolescents. The interviews were transcribed and analyzed using systematic text condensation. Results: The adolescents related meaning in life to the experience of coherence, intrinsic values, progress and belonging, or the absence of these. Examples of sources of meaning among the informants were positive relations with meaningful others, structure and routines, moments of well-being, achieving goals related to education, work and family, and seeking something else outside oneself such as playing computer games, being engaged in politics, being with animals, having a belief in a god or being outdoors in natural surroundings. Conclusion: Although the topic of meaning in life was unknown to this group, they used sources of meaning intuitively. This group seems to have similar preferences to the general population. More knowledge is needed on how the topic of meaning in life can be useful in therapy for adolescents with development trauma.

PsnGPCRdb: The Structure-network Database of G Protein Coupled Receptors

G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market.Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure.psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well.psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design.psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.

Deep learning-based optical authentication using the structural coloration of metals with femtosecond laser-induced periodic surface structures.

Structurally colored materials present potential technological applications including anticounterfeiting tags for authentication due to the ability to controllably manipulate colors through nanostructuring. Yet, no applications of deep learning algorithms, known to discover meaningful structures in data with far-reaching optimization capabilities, to such optical authentication applications involving low-spatial-frequency laser-induced periodic surface structures (LSFLs) have been demonstrated to date. In this work, by fine-tuning one of the lightweight convolutional neural networks, MobileNetV1, we investigate the optical authentication capabilities of the structurally colorized images on metal surfaces fabricated by controlling the orientation of femtosecond LSFLs. We show that the structural color variations due to a broad range of the illumination incident angles combined with both the controlled orientations of LSFLs and differences in features captured in the image make this system suitable for deep learning-based optical authentication.

Epistemic feelings and the making of the statement as a meaningful linguistic structure. Revisiting Heinrich Gomperz’s psychoaffective model of semantics and semiotics and its significance today

ABSTRACT In the present article, I revisit the feeling-based model of semantics and semiotics proposed in 1908 by the Austrian philosopher Heinrich Gomperz (1873–1942) within the framework of his “semasiology” (Semasiologie). I discuss how Gomperz regarded epistemic (“intellectual”) feelings as the foundations of both conceptual and grammatical meanings, but also of the “semiotization” of the statement (Aussage). Special emphasis is placed on how, for him, affective states help make the statement a global meaningful structure. An analysis of Gomperz’s psychoaffective model leads me to wonder about the soundness of the provocative view that epistemic feelings may be the core psychological components of linguistic meaning.

Modal Component in Meaningful Structure of the Utterance with the Semantics of “Disappearance” in Russian

The article is devoted to the study of modal framework of the utterance with the semantics of “disappearance” in modern Russian language. The empirical basis of the study was the works of Russian writers, such as M.A. Bulgakov, F.M. Dostoevsky, A.I. Kuprin., L.M. Leonov, B.L. Pasternak, K.G. Paustovsky, L.N. Tolstoy, I.S. Turgenev and others. The author focuses special attention on the role of the modal component in the formation of the sentences and its modal semantics. A lot attention is paid to the analysis of the reasons of productivity / low productivity in the functioning of the temporal forms of the verbs that form these utterances. The nature of the material of study determined the choice of the following research methods and techniques: a descriptive method, in which the methods of observation, generalization and classification; structural method using the techniques of component analysis and statistical method using the technique of quantitative analysis. It was revealed that the “lexical meaning of the verb, the mode of action and the minimum or maximum (lexical indicators) context play an essential role in revealing the actuality or non-actuality of time.

Eff-3DPSeg: 3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning.

Reliable and automated 3-dimensional (3D) plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly supervised deep learning method was proposed for plant organ segmentation. The method contained (a) pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and (b) fine-tuning the pretrained model with about only 0.5% points being annotated to implement plant organ segmentation. After, 3 phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised setting. Our method achieved 95.1%, 96.6%, 95.8%, and 92.2% in the precision, recall, F1 score, and mIoU for stem-leaf segmentation for the soybean dataset and 53%, 62.8%, and 70.3% in the AP, AP@25, and AP@50 for leaf instance segmentation for the Pheno4D dataset. This study provides an effective way for characterizing 3D plant architecture, which will become useful for plant breeders to enhance selection processes. The trained networks are available at https://github.com/jieyi-one/EFF-3DPSEG.

Mutagenicity of N-acyloxy-N-alkoxyamides – QSAR determination of factors controlling activity

This account describes the origins of our extensive investigations into the mutagenicity of N-acyloxy-N-alkoxyamides. Since their discovery as biologically active anomeric amides that mutate DNA in the Ames reverse mutation assay without the need for metabolic activation, we have used activities in the Ames test to understand the impact of structural variation on cellular access to, binding to and reactivity with DNA. We have developed an understanding of the roles played by hydrophobicity, electrophilic reactivity, steric effects and, importantly, intercalation on mutagenicity levels and therefore interactions with DNA. The evolution and application of meaningful quantitative structure–activity relationships is described, and examples of their utility in explaining molecule–DNA interactions are given. Their ability to explain previous mutagenicity data and, importantly, to predict meaningful mutagenic behaviour is also demonstrated.

Enhancing Picture Book Reading for Young EFL Learners

With increasing globalization, global English and its association with literacy worldwide have gained widespread attention. Following the law of language learning, more and more countries are making efforts to help their citizens become literate in English from their early years. Picture books have played a crucial part in developing children’s emergent literacy skills. They provide children with fascinating illustrations, advancement in vocabularies, and meaningful sentence structures that go beyond the present contexts. However, picture book reading can be difficult for young English as a Foreign Language (EFL) learners due to the lack of English-speaking environment and language accumulation. With regard to this situation, this article analyzes and discusses the common debates on the print concept, phonological awareness, and vocabularies within an EFL early picture book reading setting to shed light on the practical direction in this field. Following this, this article coins relevant supporting strategies to ensure effective English picture book reading among children by developing supplementary activities, encouraging parent-child interaction, and applying multimedia materials. This article ends with a brief conclusion and questions that may need further exploration and research.

Easy2Hard: Learning to Solve the Intractables From a Synthetic Dataset for Structure-Preserving Image Smoothing.

Image smoothing is a prerequisite for many computer vision and graphics applications. In this article, we raise an intriguing question whether a dataset that semantically describes meaningful structures and unimportant details can facilitate a deep learning model to smooth complex natural images. To answer it, we generate ground-truth labels from easy samples by candidate generation and a screening test and synthesize hard samples in structure-preserving smoothing by blending intricate and multifarious details with the labels. To take full advantage of this dataset, we present a joint edge detection and structure-preserving image smoothing neural network (JESS-Net). Moreover, we propose the distinctive total variation loss as prior knowledge to narrow the gap between synthetic and real data. Experiments on different datasets and real images show clear improvements of our method over the state of the arts in terms of both the image cleanness and structure-preserving ability. Code and dataset are available at https://github.com/YidFeng/Easy2Hard.

Multi-material topology optimization using Wachspress interpolations for designing a 3-phase electrical machine stator

This work uses multi-material topology optimization (MMTO) to maximize the average torque of a 3-phase permanent magnet synchronous machine (PMSM). Eight materials are considered in the stator: air, soft magnetic steel, three electric phases, and their three returns. To address the challenge of designing a 3-phase PMSM stator, a generalized density-based framework is used. The proposed methodology places the prescribed material candidates on the vertices of a convex polytope, interpolates material properties using Wachspress shape functions, and defines Cartesian coordinates inside polytopes as design variables. A rational function is used as penalization to ensure convergence towards meaningful structures, without the use of a filtering process. The influences of different polytopes and penalization parameters are investigated. The results indicate that a hexagonal-based diamond polytope is a better choice than the classical orthogonal domains for this MMTO problem. In addition, the proposed methodology yields high-performance designs for 3-phase PMSM stators by implementing a continuation method on the electric load angle.

ANALYSIS OF CORE SENTENCES IN KYRGYZ LANGUAGE

In the late 1980s, there was an intense interest in model studies in Turkology. Research in this direction was generally aimed at examining simple sentence models in Siberian languages. On the basis of these studies, it has been proven that the central unit of the language system is the 'simple sentence'. Meaningful sentence structure elements form the essence of the sentence and have unlimited possibilities to expand the structural composition of the sentence. In this study, we will try to analyze the model study of core sentences in Kyrgyz language by using simple sentences according to the sentence structure features.

Learning graph from graph signals: An approach based on sensitivity analysis over a deep learning framework

Utilizing a meaningful graph plays an essential part in the performance of graph-based algorithms. However, a ground-truth graph representing the relationships between data points is not readily available in many applications. This paper proposes a graph learning method based on sensitivity analysis over a deep learning framework called GL-SADL. The proposed method is composed of two steps. First, it estimates the signal value for each vertex using the signal values corresponding to the other vertices with a Deep Neural Network (DNN) block. Then a sensitivity analysis approach is applied to each DNN block to determine how the input signal values influence the DNN’s response. This procedure leads us to the underlying graph structure. The utilization of DNNs allows us to take advantage of the non-linearity characteristics of neural networks in modeling the observed graph signals. In addition, since the DNNs are considered as general approximators, there is no need to make any prior assumptions about the distribution of the observed graph signals. Experiments with synthetic and real-world datasets demonstrate that the proposed method can infer meaningful graph structures from observed graph signals.

Using joint models to study the association between CD4 count and the risk of death in TB/HIV data

BackgroundThe association structure linking the longitudinal and survival sub-models is of fundamental importance in the joint modeling framework and the choice of this structure should be made based on the clinical background of the study. However, this information may not always be accessible and rationale for selecting this association structure has received relatively little attention in the literature. To this end, we aim to explore four alternative functional forms of the association structure between the CD4 count and the risk of death and provide rationale for selecting the optimal association structure for our data. We also aim to compare the results obtained from the joint model to those obtained from the time-varying Cox model.MethodsWe used data from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) AIDS Treatment programme, the Starting Antiretroviral Therapy at Three Points in Tuberculosis (SAPiT) study, an open-label, three armed randomised, controlled trial between June 2005 and July 2010 (N=642). In our analysis, we combined the early and late integrated arms and compared results to the sequential arm. We utilized the Deviance Information Criterion (DIC) to select the final model with the best structure, with smaller values indicating better model adjustments to the data.ResultsPatient characteristics were similar across the study arms. Combined integrated therapy arms had a reduction of 55% in mortality (HR:0.45, 95% CI:0.28-0.72) compared to the sequential therapy arm. The joint model with a cumulative effects functional form was chosen as the best association structure. In particular, our joint model found that the area under the longitudinal profile of CD4 count was strongly associated with a 21% reduction in mortality (HR:0.79, 95% CI:0.72-0.86). Where as results from the time-varying Cox model showed a 19% reduction in mortality (HR:0.81, 95% CI:0.77-0.84).ConclusionsIn this paper we have shown that the “current value” association structure is not always the best structure that expresses the correct relationship between the outcomes in all settings, which is why it is crucial to explore alternative clinically meaningful association structures that links the longitudinal and survival processes.

Cross-validation of correlation networks using modular structure

Correlation networks derived from multivariate data appear in many applications across the sciences. These networks are usually dense and require sparsification to detect meaningful structure. However, current methods for sparsifying correlation networks struggle with balancing overfitting and underfitting. We propose a module-based cross-validation procedure to threshold these networks, making modular structure an integral part of the thresholding. We illustrate our approach using synthetic and real data and find that its ability to recover a planted partition has a step-like dependence on the number of data samples. The reward for sampling more varies non-linearly with the number of samples, with minimal gains after a critical point. A comparison with the well-established WGCNA method shows that our approach allows for revealing more modular structure in the data used here.

Analytical approach in higher predict residual error on MHD mixed convective motion of MoS2 engine-oil based nanofluid

Abstract We obtain the clean semi-analytical solutions with method of directly defining inverse mapping (MDDiM) to the system of nonlinear equations arising in the magnetohydrodynamic (MHD) convection motion of Molybdenum disulfide (MoS2) engine-oil intrinsic nanofluid in a circumnavigatethe structure is considered for analysis. Finding the solutions by using MDDiM is a novel idea and first time solving for the system of nonlinear partial differential equations. We have chosen inverse linear mapping for the five-term solution and it emphasizes by residual error and this gives the low error (10−2 to 10−17) and can easily derive deformation terms by spending very low CPU time. Based on the proposed method, the convergence rate, accuracy, and efficiency of the governing equations are demonstrated, and result outputs shown in tabular and graphically, which exhibit meaningful structures and advantages in science and engineering.