Articles de revues : « Structural approaches to representation »

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Fasoulis, Romanos, Georgios Paliouras et Lydia E. Kavraki. « Graph representation learning for structural proteomics ». Emerging Topics in Life Sciences 5, n^o 6 (19 octobre 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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Zhang, Kai Song, Luo Zhong et Xuan Ya Zhang. « Image Restoration via Group l2,1 Norm-Based Structural Sparse Representation ». International Journal of Pattern Recognition and Artificial Intelligence 32, n^o 04 (13 décembre 2017) : 1854008. http://dx.doi.org/10.1142/s0218001418540083.

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Sparse representation has recently been extensively studied in the field of image restoration. Many sparsity-based approaches enforce sparse coding on patches with certain constraints. However, extracting structural information is a challenging task in the field image restoration. Motivated by the fact that structured sparse representation (SSR) method can capture the inner characteristics of image structures, which helps in finding sparse representations of nonlinear features or patterns, we propose the SSR approach for image restoration. Specifically, a generalized model is developed using structured restraint, namely, the group [Formula: see text]-norm of the coefficient matrix is introduced in the traditional sparse representation with respect to minimizing the differences within classes and maximizing the differences between classes for sparse representation, and its applications with image restoration are also explored. The sparse coefficients of SSR are obtained through iterative optimization approach. Experimental results have shown that the proposed SSR technique can significantly deliver the reconstructed images with high quality, which manifest the effectiveness of our approach in both peak signal-to-noise ratio performance and visual perception.

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Gebser, Martin, Tomi Janhunen et Jussi Rintanen. « Declarative encodings of acyclicity properties ». Journal of Logic and Computation 30, n^o 4 (8 septembre 2015) : 923–52. http://dx.doi.org/10.1093/logcom/exv063.

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Abstract Many knowledge representation tasks involve trees or similar structures as abstract datatypes. However, devising compact and efficient declarative representations of such structural properties is non-obvious and can be challenging indeed. In this article, we take a number of acyclicity properties into consideration and investigate various logic-based approaches to encode them. We use answer set programming as the primary representation language but also consider mappings to related formalisms, such as propositional logic, difference logic and linear programming. We study the compactness of encodings and the resulting computational performance on benchmarks involving acyclic or tree structures.

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Wang, Yifei, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu et Pengyu Hong. « Motif-Based Graph Representation Learning with Application to Chemical Molecules ». Informatics 10, n^o 1 (11 janvier 2023) : 8. http://dx.doi.org/10.3390/informatics10010008.

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This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.

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Joaristi, Mikel, et Edoardo Serra. « SIR-GN : A Fast Structural Iterative Representation Learning Approach For Graph Nodes ». ACM Transactions on Knowledge Discovery from Data 15, n^o 6 (19 mai 2021) : 1–39. http://dx.doi.org/10.1145/3450315.

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Graph representation learning methods have attracted an increasing amount of attention in recent years. These methods focus on learning a numerical representation of the nodes in a graph. Learning these representations is a powerful instrument for tasks such as graph mining, visualization, and hashing. They are of particular interest because they facilitate the direct use of standard machine learning models on graphs. Graph representation learning methods can be divided into two main categories: methods preserving the connectivity information of the nodes and methods preserving nodes’ structural information. Connectivity-based methods focus on encoding relationships between nodes, with connected nodes being closer together in the resulting latent space. While methods preserving structure generate a latent space where nodes serving a similar structural function in the network are encoded close to each other, independently of them being connected or even close to each other in the graph. While there are a lot of works that focus on preserving node connectivity, only a few works focus on preserving nodes’ structure. Properly encoding nodes’ structural information is fundamental for many real-world applications as it has been demonstrated that this information can be leveraged to successfully solve many tasks where connectivity-based methods usually fail. A typical example is the task of node classification, i.e., the assignment or prediction of a particular label for a node. Current limitations of structural representation methods are their scalability, representation meaning, and no formal proof that guaranteed the preservation of structural properties. We propose a new graph representation learning method, called Structural Iterative Representation learning approach for Graph Nodes ( SIR-GN ). In this work, we propose two variations ( SIR-GN: GMM and SIR-GN: K-Means ) and show how our best variation SIR-GN: K-Means : (1) theoretically guarantees the preservation of graph structural similarities, (2) provides a clear meaning about its representation and a way to interpret it with a specifically designed attribution procedure, and (3) is scalable and fast to compute. In addition, from our experiment, we show that SIR-GN: K-Means is often better or, in the worst-case comparable than the existing structural graph representation learning methods present in the literature. Also, we empirically show its superior scalability and computational performance when compared to other existing approaches.

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LLADÓS, JOSEP, MARÇAL RUSIÑOL, ALICIA FORNÉS, DAVID FERNÁNDEZ et ANJAN DUTTA. « ON THE INFLUENCE OF WORD REPRESENTATIONS FOR HANDWRITTEN WORD SPOTTING IN HISTORICAL DOCUMENTS ». International Journal of Pattern Recognition and Artificial Intelligence 26, n^o 05 (août 2012) : 1263002. http://dx.doi.org/10.1142/s0218001412630025.

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Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images.

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Zahnitko, Anatolii, Lubomira Hnatiuk et Mariia Antonova. « Structural Configuration of the Text World Polymentality Representation ». Studies about Languages 1, n^o 40 (13 juillet 2022) : 5–16. http://dx.doi.org/10.5755/j01.sal.40.1.29962.

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The article highlights the problem of structural configuration of the representation of polymentality of the textual world. It focuses on the problem of polymentality (ambiguity) of the text. It is proposed to address it in the context of psycholinguistics and cognitive linguistics. On that ground, the study substantiates the figure-background relations as a necessary characteristic of the manifestation of the author's sensorics. Such an approach allows not only revealing cognitive mechanisms that underlie polymentality of the literary text but also systematizing the structural configuration of its representation. The dynamics of figure-background relations (as a way of cognition) is fully manifested in the author's sensorics, often performing certain communicative tasks. The identification of common mechanisms of specificity of the interaction of figure and background in the interpretation of text meaning promotes the use of psychological approaches. The last represent the process of formation and perception of a new meaning as a process of self-organization of a complex system. The results show that the problem of the structural configuration of the representation of the polymentality of the literary text can be solved through the psychological analysis of the author's sensory in terms of figure-background relations in the gestalt-psychological discourse.

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Jinpa, Tenzin, et Yong Gao. « Code Representation Learning Using Prüfer Sequences (Student Abstract) ». Proceedings of the AAAI Conference on Artificial Intelligence 36, n^o 11 (28 juin 2022) : 12977–78. http://dx.doi.org/10.1609/aaai.v36i11.21625.

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An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for code representation learning. In this study, we propose to use the Prufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Results from our experiment show that prufer-sequence-based representation is indeed highly effective and efficient.

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Chaabane, Marwa, Majdi Mansouri, Kamaleldin Abodayeh, Ahmed Ben Hamida, Hazem Nounou et Mohamed Nounou. « Effective fault detection in structural health monitoring systems ». Advances in Mechanical Engineering 11, n^o 9 (septembre 2019) : 168781401987323. http://dx.doi.org/10.1177/1687814019873234.

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A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

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Demidovskij, Alexander, et Eduard Babkin. « Adapting Neural Turing Machines for linguistic assessments aggregation in neural-symbolic decision support systems ». Information and Control Systems, n^o 5 (26 octobre 2021) : 40–50. http://dx.doi.org/10.31799/1684-8853-2021-5-40-50.

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Introduction: The construction of integrated neurosymbolic systems is an urgent and challenging task. Building neurosymbolic decision support systems requires new approaches to represent knowledge about a problem situation and to express symbolic reasoning at the subsymbolic level. Purpose: Development of neural network architectures and methods for effective distributed knowledge representation and subsymbolic reasoning in decision support systems in terms of algorithms for aggregation of fuzzy expert evaluations to select alternative solutions. Methods: Representation of fuzzy and uncertain estimators in a distributed form using tensor representations; construction of a trainable neural network architecture for subsymbolic aggregation of linguistic estimators. Results: The study proposes two new methods of representation of linguistic assessments in a distributed form. The first approach is based on the possibility of converting an arbitrary linguistic assessment into a numerical representation and consists in converting this numerical representation into a distributed one by converting the number itself into a bit string and further forming a matrix storing the distributed representation of the whole expression for aggregating the assessments. The second approach to translating linguistic assessments to a distributed representation is based on representing the linguistic assessment as a tree and coding this tree using the method of tensor representations, thus avoiding the step of translating the linguistic assessment into a numerical form and ensuring the transition between symbolic and subsymbolic representations of linguistic assessments without any loss of information. The structural elements of the linguistic assessment are treated as fillers with their respective positional roles. A new subsymbolic method of aggregation of linguistic assessments is proposed, which consists in creating a trainable neural network module in the form of a Neural Turing Machine. Practical relevance: The results of the study demonstrate how a symbolic algorithm for aggregation of linguistic evaluations can be implemented by connectionist (or subsymbolic) mechanisms, which is an essential requirement for building distributed neurosymbolic decision support systems.

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ROTH, CAMILLE, SERGEI OBIEDKOV et DERRICK G. KOURIE. « ON SUCCINCT REPRESENTATION OF KNOWLEDGE COMMUNITY TAXONOMIES WITH FORMAL CONCEPT ANALYSIS ». International Journal of Foundations of Computer Science 19, n^o 02 (avril 2008) : 383–404. http://dx.doi.org/10.1142/s0129054108005735.

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We present an application of formal concept analysis aimed at representing a meaningful structure of knowledge communities in the form of a lattice-based taxonomy. The taxonomy groups together agents (community members) who develop a set of notions. If no constraints are imposed on how it is built, a knowledge community taxonomy may become extremely complex and difficult to analyze. We consider two approaches to building a concise representation, respecting the underlying structural relationships while hiding superfluous information: a pruning strategy based on the notion of concept stability and a representational improvement based on nested line diagrams and "zooming". We illustrate the methods on two examples: a community of embryologists and a community of researchers in complex systems.

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LEE, WAN-JUI, VERONIKA CHEPLYGINA, DAVID M. J. TAX, MARCO LOOG et ROBERT P. W. DUIN. « BRIDGING STRUCTURE AND FEATURE REPRESENTATIONS IN GRAPH MATCHING ». International Journal of Pattern Recognition and Artificial Intelligence 26, n^o 05 (août 2012) : 1260005. http://dx.doi.org/10.1142/s0218001412600051.

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Structures and features are opposite approaches in building representations for object recognition. Bridging the two is an essential problem in pattern recognition as the two opposite types of information are fundamentally different. As dissimilarities can be computed for both the dissimilarity representation can be used to combine the two. Attributed graphs contain structural as well as feature-based information. Neglecting the attributes yields a pure structural description. Isolating the features and neglecting the structure represents objects by a bag of features. In this paper we will show that weighted combinations of dissimilarities may perform better than these two extremes, indicating that these two types of information are essentially different and strengthen each other. In addition we present two more advanced integrations than weighted combining and show that these may improve the classification performances even further.

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Schank, Christoph, et Eva Maria Spindler. « Giving Employees a Voice in Times of Digital Transformation : Modelling Employee Representation Voice in Algorithm-Based Decision-Making ». management revue 33, n^o 3 (2022) : 269–302. http://dx.doi.org/10.5771/0935-9915-2022-3-269.

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Digital transformation goes hand in hand with profound changes to company structures. One aspect of digital transformation are algorithm-based decisions which strongly affect decision-making processes in general but also those between the company and employee representatives. This changes where employee representation can be introduced in decision-making, as well as how it is implemented and what competencies are required to do so. This conceptual article looks into how employee representation voice can be kept alive in organisational algorithm-based decision-making processes. To do this, employee (representation) voice will be derived from the German co-determination model. Analogue decision-making is then initially described as a social negotiation process, and modelling is used to show how it is linked to sensemaking in order to back up this claim. In contrast, it is highlighted how algorithm-based decision-making influences this analogue process. To face the resulting changes and challenges, the concept of “big judgement” is described. This concept proposes both structural problem-solving approaches as well as employee representative qualification requirements to provide scope for employee representation voice in algorithm-based decision-making and to avoid a culture of silence.

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Hu, Shengxiang, Bofeng Zhang, Hehe Lv, Furong Chang, Chenyang Zhou, Liangrui Wu et Guobing Zou. « Improving Network Representation Learning via Dynamic Random Walk, Self-Attention and Vertex Attributes-Driven Laplacian Space Optimization ». Entropy 24, n^o 9 (30 août 2022) : 1213. http://dx.doi.org/10.3390/e24091213.

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Network data analysis is a crucial method for mining complicated object interactions. In recent years, random walk and neural-language-model-based network representation learning (NRL) approaches have been widely used for network data analysis. However, these NRL approaches suffer from the following deficiencies: firstly, because the random walk procedure is based on symmetric node similarity and fixed probability distribution, the sampled vertices’ sequences may lose local community structure information; secondly, because the feature extraction capacity of the shallow neural language model is limited, they can only extract the local structural features of networks; and thirdly, these approaches require specially designed mechanisms for different downstream tasks to integrate vertex attributes of various types. We conducted an in-depth investigation to address the aforementioned issues and propose a novel general NRL framework called dynamic structure and vertex attribute fusion network embedding, which firstly defines an asymmetric similarity and h-hop dynamic random walk strategy to guide the random walk process to preserve the network’s local community structure in walked vertex sequences. Next, we train a self-attention-based sequence prediction model on the walked vertex sequences to simultaneously learn the vertices’ local and global structural features. Finally, we introduce an attributes-driven Laplacian space optimization to converge the process of structural feature extraction and attribute feature extraction. The proposed approach is exhaustively evaluated by means of node visualization and classification on multiple benchmark datasets, and achieves superior results compared to baseline approaches.

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Li, Yifeng, et Alioune Ngom. « Sparse representation approaches for the classification of high-dimensional biological data ». BMC Systems Biology 7, Suppl 4 (2013) : S6. http://dx.doi.org/10.1186/1752-0509-7-s4-s6.

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Worden, Keith, et Graeme Manson. « The application of machine learning to structural health monitoring ». Philosophical Transactions of the Royal Society A : Mathematical, Physical and Engineering Sciences 365, n^o 1851 (12 décembre 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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Piernik, Maciej, Dariusz Brzezinski, Tadeusz Morzy et Anna Lesniewska. « XML clustering : a review of structural approaches ». Knowledge Engineering Review 30, n^o 3 (29 octobre 2014) : 297–323. http://dx.doi.org/10.1017/s0269888914000216.

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AbstractWith its presence in data integration, chemistry, biological, and geographic systems, eXtensible Markup Language (XML) has become an important standard not only in computer science. A common problem among the mentioned applications involves structural clustering of XML documents—an issue that has been thoroughly studied and led to the creation of a myriad of approaches. In this paper, we present a comprehensive review of structural XML clustering. First, we provide a basic introduction to the problem and highlight the main challenges in this research area. Subsequently, we divide the problem into three subtasks and discuss the most common document representations, structural similarity measures, and clustering algorithms. In addition, we present the most popular evaluation measures, which can be used to estimate clustering quality. Finally, we analyze and compare 23 state-of-the-art approaches and arrange them in an original taxonomy. By providing an up-to-date analysis of existing structural XML clustering algorithms, we hope to showcase methods suitable for current applications and draw lines of future research.

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Saravanos, Dimitris A., et Paul R. Heyliger. « Mechanics and Computational Models for Laminated Piezoelectric Beams, Plates, and Shells ». Applied Mechanics Reviews 52, n^o 10 (1 octobre 1999) : 305–20. http://dx.doi.org/10.1115/1.3098918.

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A considerable number of laminate theories, analytical approaches, numerical solutions and computational models have been reported for the analysis of laminates and structures with piezoelectric actuators or sensors. This article provides a review of published work in this area of mechanics. The reported laminate theories and structural mechanics are classified based on fundamental assumptions, the approximation of the through-the-thickness variation of the electromechanical state variables, the method of representation of piezoelectric layers, and their capability to model curvilinear geometries and thermal effects. The performance, advantages and limitations of the various categories of laminate theories are subsequently assessed by correlating results obtained by representative average models. The capability of each theory to model global structural response, local through-the-thickness variations of electromechanical variables, stresses, and piezoelectric laminates of high thickness is also quantified. This review article includes 103 references.

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Suchkova, E. L. « The concept of research of psychology of group legal consciousness of convicts ». Psychology and Law 8, n^o 2 (2018) : 101–12. http://dx.doi.org/10.17759/psylaw.2018080208.

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The article presents the concept of the study of psychology of group legal consciousness of convicts, developed on the basis of the integration of private scientific theory of legal consciousness with modern socio-psychological approaches to the study of psychology of large social groups. Appeal to the methodology of social representation allows to identify and describe the specifics of the content of social representations, which are structural components of the psychology of group legal consciousness of convicts, as well as to analyze their key functions. The use of the subjective approach and the theory of deformation of social relations makes it possible to study the laws of formation and deformation of the psychology of group legal consciousness of convicts. It is concluded that the integration of these approaches makes it possible to study the psychology of group legal consciousness of convicts as a complex socio-psychological phenomenon consisting of a set of mental representations produced and shared by the community in the legal sphere, as well as to identify patterns of formation and deformation of its content.

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Zobeiry, Navid, Alireza Forghani, Chao Li, Kamyar Gordnian, Ryan Thorpe, Reza Vaziri, Goran Fernlund et Anoush Poursartip. « Multiscale characterization and representation of composite materials during processing ». Philosophical Transactions of the Royal Society A : Mathematical, Physical and Engineering Sciences 374, n^o 2071 (13 juillet 2016) : 20150278. http://dx.doi.org/10.1098/rsta.2015.0278.

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Given the importance of residual stresses and dimensional changes in composites manufacturing, process simulation has been the focus of many studies in recent years. Consequently, various constitutive models and simulation approaches have been developed and implemented for composites process simulation. In this paper, various constitutive models, ranging from elastic to nonlinear viscoelastic; and simulation approaches ranging from separated flow/solid phases to multiscale integrated phases are presented and their applicability for process simulation is discussed. Attention has been paid to practical aspects of the problem where the complexity of the model coupled with the complexity and size scaling of the structure increases the characterization and simulation costs. Two specific approaches and their application are presented in detail: the pseudo-viscoelastic cure hardening instantaneously linear elastic (CHILE) and linear viscoelastic (VE). It is shown that CHILE can predict the residual stress formation in simple cure cycles such as the one-hold cycle for HEXCEL AS4/8552 where the material does not devitrify during processing. It is also shown that using this simple approach, the cure cycle can be modified to lower the residual stress level and therefore increase the mechanical performance of the composite laminate. For a more complex cure cycle where the material is devitrified during a post-cure, it is shown that a more complex model such as VE is required. This article is part of the themed issue ‘Multiscale modelling of the structural integrity of composite materials’.

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Yan, Zichao, William L. Hamilton et Mathieu Blanchette. « Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions ». Bioinformatics 36, Supplement_1 (1 juillet 2020) : i276—i284. http://dx.doi.org/10.1093/bioinformatics/btaa456.

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Abstract Motivation RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor. Results In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically interpretable representations of the learned sequence and structural motifs. Availability and implementation Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph. Supplementary information Supplementary data are available at Bioinformatics online.

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Khalyavin, Dmitry. « Symmetry constraints in frustrated magnets with strong magnetoelastic coupling ». Acta Crystallographica Section A Foundations and Advances 70, a1 (5 août 2014) : C516. http://dx.doi.org/10.1107/s2053273314094832.

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Geometrical frustration, related to the specific topology of certain crystal structures, plays a crucial role in forming exotic magnetic ground states. The presence of frustrated spins often leads to the suppression of long-range magnetic ordering and promotes short-range correlations due to fluctuations between nearly or totally degenerate ground states. The well-known structural topologies causing the presence of geometrical frustration are the three-dimensional pyrohlore and two-dimensional Kagome lattices. Compounds whose structural motif embraces these lattices are of great interest as model systems and have been the focus of numerous studies. In some cases, frustration is partially or entirely released by structural distortions through a strong magnetoelastic coupling and long-range magnetic order is established at a finite temperature. In the resulting distorted phases, complex noncollinear or partially disordered spin configurations can be observed. The phase transitions to the ordered state are quite often first order and may involve several irreducible representations of the paramagnetic space group and sometimes, like in the case of ZnCr2O4, even several propagation vectors which do not belong to the same star. The approach to determine magnetic structures in these systems, based on representation theory, should take into account the coupling free-energy invariants relating the magnetic and structural order parameters. Application of magnetic space groups and superspace groups is especially useful and can be efficiently combined with the representation theory. Based on specific examples, I will demonstrate how both approaches can be combined to provide symmetry constraints sufficient to solve complex magnetic structures in some geometrically frustrated systems.

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Roland-Lévy, Christine, Ruxanda Kmiec et Jérémy Lemoine. « How is the economic crisis socially assessed ? » Social Science Information 55, n^o 2 (8 février 2016) : 235–54. http://dx.doi.org/10.1177/0539018416629228.

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Based on the Social Representation Theory, the purpose of this article is to explore how lay-people consider both the economic crisis and risk, and to link these social representations to behavior. The article offers an original approach with the articulation of two studies about the social construction of risk and crises. It also contributes to the development of research methods for studying the connections between representations and practical implications. Based on this, the impact of the social representation of the crisis on the perceived ability to act is approached. The first study focuses on free-association tasks, with two distinct target terms: ‘risk’ and ‘crisis’. The structural approach, with a prototypical analysis, allowed the identification of two different representations: (1) for risk, ‘danger’ is the central element; (2) for crisis, ‘economy’ and ‘money’ constitute the main components of the representation. The second study investigates the links between the two previously detected structures and their relations with the perceived ability to act in a financial crisis context. Some aspects of social knowledge were found to have an impact on perceived ability to act.

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Zaker Esteghamati, Mohsen. « A Holistic Review of GM/IM Selection Methods from a Structural Performance-Based Perspective ». Sustainability 14, n^o 20 (11 octobre 2022) : 12994. http://dx.doi.org/10.3390/su142012994.

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Current fully probabilistic approaches to performance-based earthquake engineering describe structures’ behavior under a wide range of seismic hazard levels. These approaches require a detailed representation of ground motion (GM) uncertainty at all considered hazard levels, yet different GM selection methods lead to different estimations of structural performance. This paper presents a holistic review of the current practices in GM representation and selection for structural demand analysis through a performance-based lens. The multidisciplinary nature of GM selection, ranging from earth science to engineering seismology and statistics, has created a preponderance of literature to find the best practice for probabilistic assessment of structures in terms of computational efficiency and statistical accuracy. Many of these studies focus individually on GM selection or structural analysis, and the relatively scarce review papers either focus on code-based GM selection or do not specifically address risk-based evaluations by overlooking the interaction between GM selection and structural analysis. This paper aims to aid researchers in selecting appropriate GMs as part of a statistically valid and robust probabilistic demand analysis without performing an exhaustive literature review. Discussion on the available computational tools and their trade-offs for risk-based assessment of single structures is provided. While the problem-specific nature of GM selection means that no pre-selected set of GM/IM is applicable to all cases, the comprehensive narrative of this paper is expected to aid analysts in reaching a more informed decision.

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Kawaji, Shigeyasu, et Yuehui Chen. « Evolving Neurofuzzy System by Hybrid Soft Computing Approaches for System Identification ». Journal of Advanced Computational Intelligence and Intelligent Informatics 5, n^o 4 (20 juillet 2001) : 220–28. http://dx.doi.org/10.20965/jaciii.2001.p0220.

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This paper studies optimizing neurofuzzy system using a hybrid approach of a modified probabilistic incremental program evolution algorithm (MPIPE), random search algorithm, and evolutionary programming (EP). Neurofuzzy system is a combination of fuzzy system and neural network. The performance of a neurofuzzy system depends largely on selection of fuzzy membership functions, partition of input space and fuzzy rules. Two neurofuzzy models, additive and direct, are proposed in which neurofuzzy system calculation is based on tree structural representation. Without prior knowledge of the plant, parameters of fuzzy membership functions, the number of fuzzy rules and weights of neurofuzzy system are optimized using a hybrid method of MPIPE and EP algorithms simultaneously. Simulation results for identification of nonlinear systems show the feasibility and effectiveness of the proposed method.

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Snodgrass, Sam, et Santiago Ontañón. « Leveraging Multi-Layer Level Representations for Puzzle-Platformer Level Generation ». Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 13, n^o 2 (25 juin 2021) : 125–31. http://dx.doi.org/10.1609/aiide.v13i2.12966.

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Procedural content generation via machine learning (PCGML) has been growing in recent years. However, many PCGML approaches are only explored in the context of linear platforming games, and focused on modeling structural level information. Previously, we developed a multi-layer level representation, where each layer is designed to capture specific level information. In this paper, we apply our multi-layer approach to Lode Runner, a game with non-linear paths and complex actions. We test our approach by generating levels for Lode Runner with a constrained multi-dimensional Markov chain (MdMC) approach that ensures playability and a standard MdMC sampling approach. We compare the levels sampled when using multi-layer representation against those sampled using the single-layer representation; we compare using both the constrained sampling algorithm and the standard sampling algorithm.

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Bobek, Michal. « Landtová, Holubec, and the Problem of an Uncooperative Court : Implications for the Preliminary Rulings Procedure ». European Constitutional Law Review 10, n^o 1 (15 avril 2014) : 54–89. http://dx.doi.org/10.1017/s1574019614001047.

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The ultra vires judgment of the Czech Constitutional Court in Holubec and its significance - Evolution and unfolding of Czechoslovak pension saga - Landtová judgment of the Court - Later developments and approaches of the various actors - Holubec as a revolt that did not take place - Broader structural implications for the preliminary ruling procedure and its reform - The functions of the preliminary rulings procedure - Atomisation of national judicial hierarchies and its consequences - Voice and representation before the Court - The role of governmental agent in proceedings before the Court - Law-making without representation - The position of constitutional courts in the European judicial system - When judicial cooperation turns uncooperative - Conceptualizing judicial non-cooperation and disobedience.

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Fagert, Jonathon, Mostafa Mirshekari, Pei Zhang et Hae Young Noh. « Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration Sensing ». Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, n^o 1 (29 mars 2022) : 1–33. http://dx.doi.org/10.1145/3517229.

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In this paper, we present a multiple concurrent occupant identification approach through footstep-induced floor vibration sensing. Identification of human occupants is useful in a variety of indoor smart structure scenarios, with applications in building security, space allocation, and healthcare. Existing approaches leverage sensing modalities such as vision, acoustic, RF, and wearables, but are limited due to deployment constraints such as line-of-sight requirements, sensitivity to noise, dense sensor deployment, and requiring each walker to wear/carry a device. To overcome these restrictions, we use footstep-induced structural vibration sensing. Footstep-induced signals contain information about the occupants' unique gait characteristics, and propagate through the structural medium, which enables sparse and passive identification of indoor occupants. The primary research challenge is that multiple-person footstep-induced vibration responses are a mixture of structurally-codependent overlapping individual responses with unknown timing, spectral content, and mixing ratios. As such, it is difficult to determine which part of the signal corresponds to each occupant. We overcome this challenge through a recursive sparse representation approach based on cosine distance that identifies each occupant in a footstep event in the order that their signals are generated, reconstructs their portion of the signal, and removes it from the mixed response. By leveraging sparse representation, our approach can simultaneously identify and separate mixed/overlapping responses, and the use of the cosine distance error function reduces the influence of structural codependency on the multiple walkers' signals. In this way, we isolate and identify each of the multiple occupants' footstep responses. We evaluate our approach by conducting real-world walking experiments with three concurrent walkers and achieve an average F1 score for identifying all persons of 0.89 (1.3x baseline improvement), and with a 10-person "hybrid" dataset (simulated combination of single-walker real-world data), we identify 2, 3, and 4 concurrent walkers with a trace-level accuracy of 100%, 93%, and 73%, respectively, and observe as much as a 2.9x error reduction over a naive baseline approach.

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Wong, May L. Y. « Analysing aggression of social actors in political protests : combining corpus and cognitive approaches to discourse analysis ». Journal of Aggression, Conflict and Peace Research 9, n^o 3 (10 juillet 2017) : 178–94. http://dx.doi.org/10.1108/jacpr-09-2016-0250.

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Purpose The purpose of this paper is to examine the newspaper representations of the aggressive behaviour of social actors in political protests and explore the benefits of integrating corpus linguistics and cognitive approaches to a critical discourse analysis in analysing press reports. Design/methodology/approach This paper uses methods from corpus linguistics and theoretical constructs from cognitive linguistics to examine patterns of representation around Occupy Central, a recent political protest in Hong Kong, in two corpora of English-language newspaper articles published in China Daily and the South China Morning Post (SCMP). Findings An analysis of the ten most frequent collocates of the word police showed that the China Daily corpus articles typically index the presentation of police as vulnerable yet professional in their handling of violent protesters, whereas in SCMP, police officers are often presented as aggressors. The analysis subsequently considered three discursive strategies, namely structural configuration, framing and identification that are mediated through conceptualisations that representations in text evoke. Research limitations/implications In the proposed integrated approach, quantitative investigations of corpus examples could be focussed and contextualised in such a way that particular linguistic instantiations in discourse which are proved statistically salient can be further analysed in relation to conceptual phenomena which serve specific ideological purposes. Originality/value Hopefully, the study could serve as the first ever attempt to adopt an integrative analytical framework in the study of aggression and conflict in news discourse.

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Shinozuka, Masanobu, et George Deodatis. « Simulation of Multi-Dimensional Gaussian Stochastic Fields by Spectral Representation ». Applied Mechanics Reviews 49, n^o 1 (1 janvier 1996) : 29–53. http://dx.doi.org/10.1115/1.3101883.

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The subject of this paper is the simulation of multi-dimensional, homogeneous, Gaussian stochastic fields using the spectral representation method. Following this methodology, sample functions of the stochastic field can be generated using a cosine series formula. These sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic field when the number of terms in the cosine series is large. The ensemble-averaged power spectral density or autocorrelation function approaches the corresponding target function as the sample size increases. In addition, the generated sample functions possess ergodic characteristics in the sense that the spatially-averaged mean value, autocorrelation function and power spectral density function are identical with the corresponding targets, when the averaging takes place over the multi-dimensional domain associated with the fundamental period of the cosine series. Another property of the simulated stochastic field is that it is asymptotically Gaussian as the number of terms in the cosine series approaches infinity. The most important feature of the method is that the cosine series formula can be numerically computed very efficiently using the Fast Fourier Transform technique. The main area of application of this method is the Monte Carlo solution of stochastic problems in structural engineering, engineering mechanics and physics. Specifically, the method has been applied to problems involving random loading (random vibration theory) and random material and geometric properties (response variability due to system stochasticity).

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Bostenaru Dan, M., et I. Armas. « Earthquake impact on settlements : the role of urban and structural morphology ». Natural Hazards and Earth System Sciences 15, n^o 10 (12 octobre 2015) : 2283–97. http://dx.doi.org/10.5194/nhess-15-2283-2015.

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Abstract. This study is aimed to create an alternative to the classical GIS representation of the impact of earthquake hazards on urban areas. To accomplish this, the traditional map was revised, so that it can cope with contemporary innovative ways of planning, namely strategic planning. As in the theory of fractals, the building dimension and the urban neighbourhood dimension are addressed as different geographic scales between which lessons for decisions can be learned through regression. The interaction between the two scales is useful when looking for alternatives, for the completion of a GIS analysis, and in choosing the landmarks, which, in the case of hazards, become strategic elements in strategic planning. A methodology to innovate mapping as a digital means for analysing and visualising the impact of hazards is proposed. This method relies on concepts from various geography, urban planning, structural engineering and architecture approaches related to disaster management. The method has been tested at the building scale for the N–S Boulevard in Bucharest, Romania, called Magheru. At the urban scale, an incident database has been created, in which the case study for the building level can be mapped. The paper presented is part of a larger research work, which addresses decision making using the framework shown here. The main value of the paper is in proposing a conceptual framework to deconstruct the map for digital earthquake disaster impact analysis and representation. The originality of the concept consists in the representation of elements at different scales considered to be of different levels of importance in the urban tissue, according to the analysis to be performed on them.

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Bostenaru Dan, M., et I. Armas. « Hazard impact on settlements : the role of urban and structural morphology ». Natural Hazards and Earth System Sciences Discussions 3, n^o 5 (19 mai 2015) : 3287–321. http://dx.doi.org/10.5194/nhessd-3-3287-2015.

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Abstract. We aim to create an alternative to GIS representation of the impact of hazards on urban areas. To accomplish this, we revise the traditional map, so that it can cope with today's innovative ways of planning, namely strategic planning. As in the theory of fractals, we address the building dimension and the urban neighbourhood dimension as different geographic scales between which lessons for decisions can be learned through regression. The interaction between the two scales can be seen when looking for alternatives or the completion of a GIS analysis, or in chosing the landmarks, which, in the case of hazards, become strategic elements in strategic planning. A methodology to innovate mapping as a digital means for analysing and visualising the impact of hazards has been developed. This new method relies on concepts from various geography, urban planning, structural engineering and architecture approaches related to disaster management. The method has been tested at the building scale for the central N–S boulevard in Bucharest, Romania, comprising the protected urban zone 04 "Magheru". At the urban scale, an incident database has been created, in which the case study for the building level can be mapped. The paper presented is part of a larger research work, which addresses decision making using the framework shown here. The main value of the paper is in proposing a conceptual framework to deconstruct the map for digital disaster impact analysis and representation. This concept is highly original, because it considers the representation of elements at different scales to be of different importance in the urban tissue, according to the analysis to be performed on them.

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Molinski, Steven V., Zoltán Bozóky, Surtaj H. Iram et Saumel Ahmadi. « Biophysical Approaches Facilitate Computational Drug Discovery for ATP-Binding Cassette Proteins ». International Journal of Medicinal Chemistry 2017 (19 mars 2017) : 1–9. http://dx.doi.org/10.1155/2017/1529402.

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Although membrane proteins represent most therapeutically relevant drug targets, the availability of atomic resolution structures for this class of proteins has been limited. Structural characterization has been hampered by the biophysical nature of these polytopic transporters, receptors, and channels, and recent innovations to in vitro techniques aim to mitigate these challenges. One such class of membrane proteins, the ATP-binding cassette (ABC) superfamily, are broadly expressed throughout the human body, required for normal physiology and disease-causing when mutated, yet lacks sufficient structural representation in the Protein Data Bank. However, recent improvements to biophysical techniques (e.g., cryo-electron microscopy) have allowed for previously “hard-to-study” ABC proteins to be characterized at high resolution, providing insight into molecular mechanisms-of-action as well as revealing novel druggable sites for therapy design. These new advances provide ample opportunity for computational methods (e.g., virtual screening, molecular dynamics simulations, and structure-based drug design) to catalyze the discovery of novel small molecule therapeutics that can be easily translated from computer to bench and subsequently to the patient’s bedside. In this review, we explore the utility of recent advances in biophysical methods coupled with well-established in silico techniques towards drug development for diseases caused by dysfunctional ABC proteins.

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Delo, Giulia, Marco Civera, Erica Lenticchia, Gaetano Miraglia, Cecilia Surace et Rosario Ceravolo. « Interferometric Satellite Data in Structural Health Monitoring : An Application to the Effects of the Construction of a Subway Line in the Urban Area of Rome ». Applied Sciences 12, n^o 3 (5 février 2022) : 1658. http://dx.doi.org/10.3390/app12031658.

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In recent years, the use of interferometric satellite data for Structural Health Monitoring has experienced a strong development. The urban environment confirms its fragility to adverse natural events, made even more severe by climate change. Hence, the need to carry out continuous monitoring of structures and artefacts appears increasingly urgent. Furthermore, satellite data could considerably increase the feasibility of traditional Structural Health Monitoring (SHM) approaches. This study aims to explore this remote sensing approach, focusing on the representation techniques that can be adopted to highlight their advantages and provide an interpretation of the results. In particular, the study analyzes records from the urban area of Rome (Italy), subject to the construction of a new subway line. These data are exploited to create a velocity map to highlight the possible subsidence phenomenon induced by excavations. Then, the paper focuses on single buildings or building complexes through the entropy-energy representation. Beyond the different limitations caused by the input data, a correlation is identified between the results of the two representation techniques. Accordingly, the effects of excavation on the urban area are demonstrated, and the methodologies are validated.

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Qiu, Dehong, Qifeng Zhang et Shaohong Fang. « Reconstructing Software High-Level Architecture by Clustering Weighted Directed Class Graph ». International Journal of Software Engineering and Knowledge Engineering 25, n^o 04 (mai 2015) : 701–26. http://dx.doi.org/10.1142/s0218194015500072.

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Software architecture reconstruction plays an important role in software reuse, evolution and maintenance. Clustering is a promising technique for software architecture reconstruction. However, the representation of software, which serves as clustering input, and the clustering algorithm need to be improved in real applications. The representation should contain appropriate and adequate information of software. Furthermore, the clustering algorithm should be adapted to the particular demands of software architecture reconstruction well. In this paper, we first extract Weighted Directed Class Graph (WDCG) to represent object-oriented software. WDCG is a structural and quantitative representation of software, which contains not only the static information of software source code but also the dynamic information of software execution. Then we propose a WDCG-based Clustering Algorithm (WDCG-CA) to reconstruct high-level software architecture. WDCG-CA makes full use of the structural and quantitative information of WDCG, and avoids wrong compositions and arbitrary partitions successfully in the process of reconstructing software architecture. We introduce four metrics to evaluate the performance of WDCG-CA. The results of the comparative experiments show that WDCG-CA outperforms the comparative approaches in most cases in terms of the four metrics.

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Faez, Faezeh, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah et Hamid R. Rabiee. « SCGG : A deep structure-conditioned graph generative model ». PLOS ONE 17, n^o 11 (21 novembre 2022) : e0277887. http://dx.doi.org/10.1371/journal.pone.0277887.

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Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods able to consider different conditions during the generation procedure even increases their effectiveness by empowering them to generate new graph samples that meet the desired criteria. This paper presents a conditional deep graph generation method called SCGG that considers a particular type of structural conditions. Specifically, our proposed SCGG model takes an initial subgraph and autoregressively generates new nodes and their corresponding edges on top of the given conditioning substructure. The architecture of SCGG consists of a graph representation learning network and an autoregressive generative model, which is trained end-to-end. More precisely, the graph representation learning network is designed to compute continuous representations for each node in a graph, which are not only affected by the features of adjacent nodes, but also by the ones of farther nodes. This network is primarily responsible for providing the generation procedure with the structural condition, while the autoregressive generative model mainly maintains the generation history. Using this model, we can address graph completion, a rampant and inherently difficult problem of recovering missing nodes and their associated edges of partially observed graphs. The computational complexity of the SCGG method is shown to be linear in the number of graph nodes. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method compared with state-of-the-art baselines.

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Guo, Zhijiang, Yan Zhang, Zhiyang Teng et Wei Lu. « Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning ». Transactions of the Association for Computational Linguistics 7 (novembre 2019) : 297–312. http://dx.doi.org/10.1162/tacl_a_00269.

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We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Network (DCGCN). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMR-to-text generation and syntax-based neural machine translation.

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Novikova, T. S., et A. A. Tsyplakov. « Social policy development based on a combination of agent-oriented and inter-industrial approaches ». Journal of the New Economic Association 52, n^o 4 (2021) : 12–36. http://dx.doi.org/10.31737/2221-2264-2021-52-4-1.

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The article proposes methodological approach for assessing the consequences of state social policy based on an extended agent-based spatial model with a detailed representation of the reaction of agents to changes in social transfers and taxation as the instruments of government policy. Optimization of territorial social policy is ensured by maximizing the iso-elastic function of social welfare (FSW), based on the individual utility functions of households and taking into account the degree of rejection of social inequality. Structural changes resulting from public policy are analyzed by aggregating the decisions of microeconomic agents and calculating the inputoutput balance table, including through a visual representation in the fourth quadrant of redistribution processes when transfers and taxes change. The results of experimental calculations show that at each level of the coefficient of rejection of inequality for both transfers and taxes, local maxima arise that correspond to the optimal levels of transfers and taxes and form monotonically decreasing iso-optimal curves depending on an increase in either the share of transfers or the level of taxation. The proposed approach to the formalization of ideas about the ratio of efficiency and fairness in the construction of FSW provides an opportunity to choose optimal solutions to justify an agent-based social policy.

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LU, JIAN, JIAPENG TIAN, CHEN XU et YURU ZOU. « A DICTIONARY LEARNING APPROACH FOR FRACTAL IMAGE CODING ». Fractals 27, n^o 02 (mars 2019) : 1950020. http://dx.doi.org/10.1142/s0218348x19500208.

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In recent years, sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, this paper investigates incorporating a dictionary learning approach into fractal image coding, which leads to a new model containing three terms: a patch-based sparse representation prior over a learned dictionary, a quadratic term measuring the closeness of the underlying image to a fractal image, and a data-fidelity term capturing the statistics of Gaussian noise. After the dictionary is learned, the resulting optimization problem with fractal coding can be solved effectively. The new method can not only efficiently recover noisy images, but also admirably achieve fractal image noiseless coding/compression. Experimental results suggest that in terms of visual quality, peak-signal-to-noise ratio, structural similarity index and mean absolute error, the proposed method significantly outperforms the state-of-the-art methods.

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Barannik, Vladimir, Yuriy Ryabukha, Pavlo Gurzhiy, Vitaliy Tverdokhlib et Igor Shevchenko. « TRANSFORMANTS BIT REPRESENTATION ENCODING WITHIN VIDEO BIT RATE CONTROL ». Information systems and technologies security, n^o 1 (1) (2019) : 52–56. http://dx.doi.org/10.17721/ists.2019.1.52-56.

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The conceptual basements of constructing an effective encoding method within the bit rate control module of video traffic in the video data processing system at the source level are considered. The essence of using the proposed method in the course of the video stream bit rate controlling disclosed, namely, the principles of constructing the fragment of the frame code representation and approaches for determining the structural units of the individual video frame within which the control is performed. The method focuses on processing the bit representation of the DCT transformants, and at his processing stage transformant was considered as a structural component of the video stream frame at which the encoding is performed. At the same time, to ensure the video traffic bit rate controlling flexibility, decomposition is performed with respect to each of the transformants to the level of the plurality of bit planes. It is argued that the proposed approach is potentially capable to reducing the video stream bit rate in the worst conditions, that is, when component coding is performed. In addition, this principle of video stream fragmen code representation forming allows to control the level of error that can be made in the bit rate control process. However, in conditions where the bit representation of the transformant is encoded, the method is able to provide higher compression rates as a result of the fact that the values of the detection probability of binary series lengths and the values of detected lengths within the bit plane will be greater than in the case of component coding. This is explained by the structural features of the distribution of binary elements within each of the bit planes, which together form the transformer DCT. In particular, high-frequency transformer regions are most often formed by chains of zero elements. The solutions proposed in the development of the encoding method are able to provide sufficient flexibility to control the bit rate of the video stream, as well as the ability to quickly change the bit rate in a wide range of values

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MIAO, YUAN, ZHI-QIANG LIU, XUE HON TAO, ZHI QI SHEN et CHUN WEN LI. « SIMPLIFICATION, MERGING AND DIVISION OF FUZZY COGNITIVE MAPS ». International Journal of Computational Intelligence and Applications 02, n^o 02 (juin 2002) : 185–208. http://dx.doi.org/10.1142/s1469026802000555.

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Fuzzy Cognitive Map (FCM) is a powerful and flexible framework for knowledge representation and causal inference. However, in most real applications, it is difficult to design and analyze FCMs due to their structural complexity. Simplification, merging, and division are the important operations on the structure of FCMs. In this paper we present approaches to simplifying FCMs. These approaches show how to clean up a FCM, how to divide a complex FCM into basic FCMs, and how to extract the eigen structure of these basic FCMs. Two improved methods for merging FCMs from different human experts are also proposed in this paper. We discuss difficulties in merging FCMs and present possible solutions.

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Korshunov, Gennady I. « Two approaches for modelling of digital technological innovation in achieving defect-free electronics production ». Journal of Physics : Conference Series 2373, n^o 6 (1 décembre 2022) : 062012. http://dx.doi.org/10.1088/1742-6596/2373/6/062012.

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Abstract The issues of innovative development of high-tech industries are considered. The aim of this development is defect-free production. The achievement of the goal is due to the introduction of technological innovations within the framework of the concept of global technological changes, proposed by Carlotta Perez as a development of the theory of Kondratyev’s cycles. The article discusses the statistical and structural approaches for the implementation of technological innovation. A formalized representation of the formation and selection of functionally necessary and functionally sufficient structures is given. The above approaches are considered in relation to the processes of surface mounting of printed circuit boards. The elements of technology and the possibilities of their improvement in the context of digitalization of processes are considered.

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Romano, M., I. Ehrlich et N. Gebbeken. « Structural mechanic material damping in fabric reinforced composites : a review ». Archives of Materials Science and Engineering 1, n^o 88 (1 novembre 2017) : 12–41. http://dx.doi.org/10.5604/01.3001.0010.7747.

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Purpose: A review regarding the acting mechanisms of structural dynamic material damping in fabric reinforced composites is presented. Design/methodology/approach: Mechanical acting principles identified by different investigations are considered. Aspects of the determination and calculation of structural mechanical material properties of fabric reinforced composites are described. Approaches intending the description and classification of ondulations in fabrics reinforced single layers are demonstrated. Findings: The mesomechanic geometry of fabrics is not considered sufficiently by relatively simple homogenization approaches. Yet, it significantly affects its structural dynamic material properties, especially the dynamic ones. Research limitations/implications: In each case the different damping mechanisms act coupled and occur at the same time. Therefore a separation procedure is required in any case. Practical implications: Against the background of the comparison and remarks of the presented papers a reasonable further procedure is recommended. Thereby, FE-calculations with a parametrical variation of the mesomechanic geometry in order to identify kinematic correlations due to geometric constraints are suggested. Originality/value: The idea of the representation of the geometric conditions in terms of a degree of ondulation is described. Such a non-dimensional specific value representing the intensity of the ondulation would enable the comparability of the results of different kinds of investigations.

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Shinozuka, Masanobu, et George Deodatis. « Simulation of Stochastic Processes by Spectral Representation ». Applied Mechanics Reviews 44, n^o 4 (1 avril 1991) : 191–204. http://dx.doi.org/10.1115/1.3119501.

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The subject of this paper is the simulation of one-dimensional, uni-variate, stationary, Gaussian stochastic processes using the spectral representation method. Following this methodology, sample functions of the stochastic process can be generated with great computational efficiency using a cosine series formula. These sample functions accurately reflect the prescribed probabilistic characteristics of the stochastic process when the number N of the terms in the cosine series is large. The ensemble-averaged power spectral density or autocorrelation function approaches the corresponding target function as the sample size increases. In addition, the generated sample functions possess ergodic characteristics in the sense that the temporally-averaged mean value and the autocorrelation function are identical with the corresponding targets, when the averaging takes place over the fundamental period of the cosine series. The most important property of the simulated stochastic process is that it is asymptotically Gaussian as N → ∞. Another attractive feature of the method is that the cosine series formula can be numerically computed efficiently using the Fast Fourier Transform technique. The main area of application of this method is the Monte Carlo solution of stochastic problems in engineering mechanics and structural engineering. Specifically, the method has been applied to problems involving random loading (random vibration theory) and random material and geometric properties (response variability due to system stochasticity).

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Höller, Daniel, Gregor Behnke, Pascal Bercher et Susanne Biundo. « Assessing the Expressivity of Planning Formalisms through the Comparison to Formal Languages ». Proceedings of the International Conference on Automated Planning and Scheduling 26 (30 mars 2016) : 158–65. http://dx.doi.org/10.1609/icaps.v26i1.13758.

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From a theoretical perspective, judging the expressivity of planning formalisms helps to understand the relationship of different representations and to infer theoretical properties. From a practical point of view, it is important to be able to choose the best formalism for a problem at hand, or to ponder the consequences of introducing new representation features. Most work on the expressivity is based either on compilation approaches, or on the computational complexity of the plan existence problem. Recently, we introduced a new notion of expressivity. It is based on comparing the structural complexity of the set of solutions to a planning problem by interpreting the set as a formal language and classifying it with respect to the Chomsky hierarchy. This is a more direct measure than the plan existence problem and enables also the comparison of formalisms that can not be compiled into each other. While existing work on that last approach focused on different hierarchical problem classes, this paper investigates STRIPS with and without conditional effects; though we also tighten some existing results on hierarchical formalisms. Our second contribution is a discussion on the language-based expressivity measure with respect to the other approaches.

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Birkin, Mark, et Alison Heppenstall. « Extending Spatial Interaction Models with Agents for Understanding Relationships in a Dynamic Retail Market ». Urban Studies Research 2011 (10 mars 2011) : 1–12. http://dx.doi.org/10.1155/2011/403969.

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For many years, effective model-based representations of the dynamics and evolution of urban spatial structure have proved elusive. While some progress has been made through the deployment of spatial interaction models, these approaches have been limited by the difficulty of representing behavioural mechanisms and processes. In this paper, it is demonstrated that evolutionary models grounded in the principles of spatial interaction are compatible with the more novel approaches of agent-based modelling. The incorporation of agents provides a much more flexible means for the representation of behavioural mechanisms. The paper illustrates the way in which three more complicated situations can be handled through the fusion of spatial interaction and agent modelling perspectives. These situations comprise discontinuous evolution (in which structural adjustment takes place in discrete steps, and not as a continuously smooth process); nonequilibrium dynamics (in which the underlying system parameters continue to evolve through time); the incorporation of new decision variables (which we illustrate through the addition of land rents into the model). The conclusion of the paper is that the combination of spatial interaction and agent-based modelling methods provides encouraging prospects for the social simulation of real urban systems.

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Karpik, Alexander P., Dmitry V. Lisitsky, Aleksey G. Osipov et Vyacheslav N. Savinykh. « GEOINFORMATIONAL-COGNITIVE REPRESENTATION OF TERRITORIAL RESOURCES ». Vestnik SSUGT (Siberian State University of Geosystems and Technologies) 25, n^o 4 (2020) : 120–29. http://dx.doi.org/10.33764/2411-1759-2020-25-4-120-129.

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The relevance of the study lies in considering the new content and features of a unified geospatial support for the economy and the life of society in the context of digital transformation. The aim of the work is to present the essence, fundamental points, new views and approaches to geospatial activities. Methods of formal logical analysis, linear programming, theory of matrix games with nature are used. A formal interpretation of the assessment and use of territorial resources as objects of a single geospatial activity is proposed. From the standpoint of the structural-functional approach, the essential characteristics of ensuring the life of society with geo-information and geosciences are considered. A formal-logical analysis of ideas about life in the surrounding geospace is given in order to optimize the use of its resources on the basis of an all-encompassing geospatial perspective. The concept of "geofragment" is introduced as an elementary unit of geospace, in which sectoral and / or natural processes take place and objects of different sectoral spaces interact. The role of geospatial knowledge is substantiated and an integrated approach to the processes of preparing spatial solutions for territory management based on a combination of geoinformation and geocognitive spaces is proposed. Geospatial activity in the territorial discourse is becoming an independent factor in the management of territories to ensure the life of society, based on the optimization of the distribution (redistribution) of territorial resources through the complex interaction of industries and clusters. This activity provides geodata, geo-information and geosciences for the diverse processes of interaction between industrial spaces and nature within the framework of a common physical geospace.

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Wu, Jingjing, Jianguo Jiang, Meibin Qi, Cuiqun Chen et Yimin Liu. « Improving Feature Discrimination for Object Tracking by Structural-similarity-based Metric Learning ». ACM Transactions on Multimedia Computing, Communications, and Applications 18, n^o 4 (30 novembre 2022) : 1–23. http://dx.doi.org/10.1145/3497746.

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Existing approaches usually form the tracking task as an appearance matching procedure. However, the discrimination ability of appearance features is insufficient in these trackers, which is caused by their weak feature supervision constraints and inadequate exploitation of spatial contexts. To tackle this issue, this article proposes a novel appearance matching tracking (AMT) method to strengthen the feature restraints and capture discriminative spatial representations. Specifically, we first utilize a triplet structural loss function, which improves the learning capability of features by applying a structural similarity constraint with a triplet metric format on the features. It leverages feature statistics to capture the complex interactions of visual parts. Second, we put forward an adaptive matching module that exploits the dual spatial enhancement module to reinforce target feature discrimination. This not only boosts the representation ability of spatial context but also realizes spatially dynamic feature selection by attending to target deformation information. Moreover, this model introduces a simple but effective matching unit to intuitively evaluate the relative appearance differences between the target and the proposals. In addition, with the obtained discriminative features, AMT is capable of providing precise localization for the target. Therefore, the impact of spatial suppression imposed by window functions can be alleviated, allowing for effective tracking of high-speed moving objects. Extensive experiments prove that AMT outperforms state-of-the-art methods on six public datasets and demonstrate the effectiveness of each component in AMT.

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Kudryashov, I. S. « Food-Porn : Philosophical and Psychoanalytic Approach to Contemporary Alimentary Practices ». Siberian Journal of Philosophy 19, n^o 4 (17 mai 2022) : 34–51. http://dx.doi.org/10.25205/2541-7517-2021-19-4-34-51.

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The paper deals with two contemporary alimentary phenomena (food porn and mukbang) in order to understand the basis for researching such phenomena in the social and anthropological sciences about food. As conceptual approaches, we choose the conception of hyperreality of J. Baudrillard and the modern version of psychoanalysis (structural psychoanalysis of J. Lacan). Based on these two approaches, we demonstrated one of the key complexities of the methodology of contemporary research on alimentary practices. It consists in the problematic choice between the search for basis in the global representation of society or in anthropological models. We also need to consider such phenomena not only in the perspective of meaning, but also in terms of pleasure.

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Garruss, Alexander S., Katherine M. Collins et George M. Church. « Deep representation learning improves prediction of LacI-mediated transcriptional repression ». Proceedings of the National Academy of Sciences 118, n^o 27 (29 juin 2021) : e2022838118. http://dx.doi.org/10.1073/pnas.2022838118.

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Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of the Escherichia coli LacI protein. We analyze structural and evolutionary aspects that relate to how the function of this protein is maintained, including an in-depth look at the C-terminal domain. We develop a deep neural network to predict transcriptional repression mediated by the lac repressor of Escherichia coli using experimental measurements of variant function. When measured across 10 separate training and validation splits using 5,009 single mutations of the lac repressor, our best-performing model achieved a median Pearson correlation of 0.79, exceeding any previous model. We demonstrate that deep representation learning approaches, first trained in an unsupervised manner across millions of diverse proteins, can be fine-tuned in a supervised fashion using lac repressor experimental datasets to more effectively predict a variant’s effect on repression. These findings suggest a deep representation learning model may improve the prediction of other important properties of proteins.

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