Representation Of Configuration Research Articles

A puzzle questions form training for self-supervised skeleton-based action recognition

This paper proposed a novel pretext task to address the skeleton-based video representation for self-supervised action recognition tasks. Instead of exploiting only the whole body, various levels of the skeleton structure (e.g., upper body, lower body, left arm, left leg, right arm, right leg, and torso) are employed to extract essential coarser-grained characteristics. This involves computing statistical representations like motion, orientation, trajectory, and magnitude shift from unlabeled skeleton configurations. Then a learning model is built and trained to yield these statistical representations given the sequence configuration as the input. Our approach is question-driven, where each question acts as a puzzle piece contributing to a deeper understanding of the skeleton joint configuration. It's inspired by the ability of the cognitive system observed in individuals to hypothesize unseen actions. This is accomplished by posing pertinent questions and envisioning plausible scenarios to recognize the actions taking place. The answers to these devised questions are derived from the statistical representation of skeleton configurations. To this end, we made 44 questions designed to encompass the broadest overview to the finest detail. Our experiments on the NTU RGB-D, NW-UCLA, and PKU-MMD datasets demonstrate outstanding results in action recognition, proving the superiority of our approach in learning discriminative characteristics.

Elemental and configural representation of a conditioned context

A context can be conceptualized as a stable arrangement of elements or as the sum of single elements. Both configural and elemental representations play a role in associative processes. This study aimed to explore the respective contributions of these two representations of a context in the acquisition of conditioned anxiety in humans. Virtual reality (VR) can be an ecologically valid tool to investigate context-related mechanisms, yet the influence of the sense of presence within the virtual environment remains unclear. Forty-eight healthy individuals participated in a VR-based context conditioning wherein electric shocks (unconditioned stimulus, US) were unpredictably delivered in one virtual office (CTX+), but not in the other (CTX-). During the test phase, nine elements from each context were presented singularly. We found a cluster of participants, who exhibited heightened anticipation of the US for anxiety-related elements as compared to the other group. In contrast to their clear elemental representation, these individuals showed diminished discriminative responses between the two context’s configurations. Discriminative responses to the contexts were boosted in those individuals, who had a weaker elemental representation. Importantly, the individual sense of presence significantly influenced the conditioned responses. These findings align with the dual-representation view of context and provide insights into the role of presence in eliciting (conditioned) anxiety responses.

Thermodynamic Stability Prediction of Triple Transition‐Metal (Ti−Mo−V)3C2${\rm (Ti-Mo-V)}_3{\rm C}_2$ MXenes via Cluster Correlation‐Based Machine Learning

AbstractThe representation of atomic configurations through cluster correlations, along with the cluster expansion approach, has long been used to predict formation energies and determine the thermodynamic stability of alloys. In this work, a comparison is conducted between the traditional cluster expansion method based on density functional theory and other potential machine learning models, including decision tree‐based ensembles and multi‐layer perceptron regression, to explore the alloying behavior of different elements in multi‐component alloys. Specifically, these models are applied to investigate the thermodynamic stability of triple transition‐metal MXenes, a multi‐component alloy in the largest family of 2D materials that are gaining attention for several outstanding properties. The findings reveal the triple transition‐metal ground‐state configurations in this system and demonstrate how the configuration of transition metal atoms (Ti, Mo, and V atoms) influences the formation energy of this alloy. Moreover, the performance of machine learning algorithms in predicting formation energies and identifying ground‐state structures is thoroughly discussed from various aspects.

Geometrical design method of Walker constellation in non-terrestrial network

To meet the requirement of mega low earth orbit (LEO) constellation design in non-terrestrial network (NTN), a geometric configuration design method of Walker constellation applicable to arbitrary latitude range coverage multiple and satellite quantity scale is proposed. First, a Walker configuration representation based on the streets of coverage (SoC) characteristic parameters is proposed. Then, satellite quantity required for coverage and the SoC parameters are estimated by calculating the width and adjacent spacing of SoC, which can improve the enumeration efficiency. Finally, by adjusting the phase and quantity of the satellite on the SoC, the constellation configuration with optimal comprehensive performances is selected from multiple groups of results. To demonstrate the effectiveness of the proposed design method, the performance comparisons among the existing mega LEO constellation systems are conducted. Compared with the existing configurations, the configuration calculated by the proposed method can efficiently reduce the satellite quantity required for coverage by about 20%, and can also satisfy the comprehensive demands including coverage performance, inter-satellite link, configuration redundancy and collision avoidance by reasonably selecting configuration parameters.

Machine learning based classification of vector field configurations

Magnetic materials at the nanoscale are important for science and technology. A key aspect for their research and advancement is the understanding of the emerging magnetization vector field configurations within samples and devices. A systematic parameter space exploration—varying for example material parameters, temperature, or sample geometry—leads to the creation of many thousands of field configurations that need to be sighted and classified. This task is usually carried out manually, for example by looking at a visual representation of the field configurations. We report that it is possible to automate this process using an unsupervised machine learning algorithm, greatly reducing the human effort. We use a combination of convolutional auto-encoder and density-based spatial clustering of applications with noise (DBSCAN) algorithm. To evaluate the method, we create the magnetic phase diagram of a FeGe disc as a function of changing external magnetic field using computer simulation to generate the configurations. We find that the classification algorithm is accurate, fast, requires little human intervention, and compares well against the published results in the literature on the same material geometry and range of external fields. Our study shows that machine learning can be a powerful tool in the research of magnetic materials by automating the classification of magnetization field configurations.

DISTRIBUTIVITY AND SYMMETRY IN SPATIAL CONFIGURATION OF RURAL HOMES. EL KANTARA (BISKRA) CASE STUDY

Several studies have addressed the mode of evolution of inhabited space and several methproposed to understand the interface between user and space and its impact on spatial configuration. In this field, space syntax provides a theory and a method for analyzing the correlation between spatial properties and the behavior of people in inhabited space and the modeling of space according to endogenous and exogenous influences. El Kantara, a case study, was predominant in pre-colonial and colonial times by the traditional vernacular type of architecture. After independence, the so-called modern construction took over. The objective is to shed light on constants and evolutions by a diachronic reading through the use of the spatial syntax method which is a set of techniques for the representation, quantification and interpretation of the spatial configuration of the built environment. Each house was analyzedaccording to its distributivity/non-distributivity corroborated by control visibility and symmetry/asymmetry corroborated by integration visibility recommended by space syntax and elaborated by depthmap. The results show that the genotype of El Kantara dwelling has undergone changes throughout its journey from the nineteenth century to the present day. The mainly constant is that it has always been a question of establishing a demarcation between the inner sphere and the outer sphere and that the spatial configuration takes many forms and revealed degrees ofrupture with respect to the outside.

Точность формирования пространственных репрезентаций динамических сцен в рабочей памяти

<p>The cognitive representation of the environment is formed using cognitive systems that process data on spatial representations of two types: egocentric, encoding the position of environmental objects relative to the observer, and allocentric, encoding the position of objects relative to each other, regardless of the position of the observer. Data on spatial representations were studied mainly in problems of memorization and reconstruction of static scenes. However, the task of processing information about dynamic scenes in everyday life has a higher ecological validity. We used HMD virtual reality technologies to study the accuracy of the formation of egocentric and allocentric spatial representations of static and dynamic scenes in working memory. The subjects were presented 8 three-dimensional virtual scenes of 4 objects each for 10 seconds in static and dynamic conditions for memorization and reconstruction. Identification accuracy (number of correctly reconstructed objects) and localization accuracy (accuracy of spatial scene reconstruction) were assessed. Localization accuracy was assessed in topological units, corresponding to the accuracy of the representation of the general configuration of objects in the scene (global topological information), and in metric units, corresponding to the accuracy of the representation of the spatial coordinates of each object (local metric information). The results showed that object identification accuracy was similar in static and dynamic conditions; the processes of encoding metric local information during the formation of both types of representations of dynamic scenes worsen compared to static ones; the accuracy of encoding topological global information remains stable compared to the static condition. We can conclude that the visual and spatial systems operate independently as part of a general cognitive system that processes data on spatial representations in time-limited working memory, as well as the redistribution of its resource in dynamic condition for supporting topological data of the holistic configuration of moving objects more, than metric data. The results highlight the importance of topological spatial characteristics of spatial representations for processes of early spatial perception, decision making, and action in the environment.</p>

Integrated air quality modeling for urban policy: A novel approach with olympus-chimere

This paper presents a novel approach to air quality modeling for megacities, focusing on the Paris region for the year 2009. The simulation is conducted by coupling the transport, energy demand, and emission model OLYMPUS with the atmospheric chemical transport model CHIMERE. OLYMPUS's emission calculations are based on the representation of an urban configuration, the simulation of a synthetic population, and the calculation of transport demand and energy consumption in buildings using an activity-based statistical approach. Emissions derived from this simulation, along with biogenic and additional industrial emissions, were used as input data in the CHIMERE model to predict pollutant concentrations. CHIMERE outputs were compared with observations from the local AIRPARIF air quality monitoring network, and with a reference simulation conducted using a benchmark bottom-up emission inventory. The results indicate that the platform provides a comprehensive representation of emissions and the resulting air quality. They also highlight the excellent representation of the spatial and temporal distribution of urban pollutant concentrations in comparison with both model and observational data. OLYMPUS tends to emit more primary pollutants than the reference emission inventory in the dense urban center. However, this often improves model scores for NO2, and these deviations remain within the uncertainty margins set for emission inventories. The approach to emission production, rooted in the connection between urban configuration and individual behaviors, allows for the exploration of innovative air quality scenarios centered on energy consumption practices. The validation of our results furnishes the OLYMPUS-CHIMERE coupling with a dependable framework for addressing numerous research inquiries related to the impact of urban policies on air quality.

Adult age differences in event memory updating: The roles of prior-event retrieval and prediction.

Remembering past events can lead to predictions of what is to come and to experiencing prediction errors when things change. Previous research has shown enhanced memory updating for ongoing events that are inconsistent with predictions based on past experiences. According to the Event Memory Retrieval and Comparison (EMRC) Theory, such memory updating depends on the encoding of configural representations that bind retrieved features of the previous event, changed features, and the relationship between the two. We investigated potential age-related differences in these mechanisms by showing older and younger adults two movies of everyday activities. Activities in the second movie either repeated from the first movie or included changed endings. During the second movie, before activities ended, participants were instructed to predict the upcoming action based on the first movie. One week later, participants were instructed to recall activity endings from the second movie. For younger adults, having predicted endings consistent with the first movie before seeing changed endings was subsequently associated with better recall of these changed endings and recollection that activities had changed. Conversely, for older adults, making such predictions prior to changes was associated with intruding details from the first movie endings and was less strongly associated with change recollection. Consistent with EMRC, these findings suggest that retrieval of relevant experiences when events change can trigger prediction errors that prompt associative encoding of existing memories and current perceptions. These mechanisms were less efficient in older adults, which may account for their poorer event memory updating than younger adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prediction of potential energy profiles of molecular dynamic simulation by graph convolutional networks

A graph convolutional networks (GCN)-based machine learning (ML) model is constructed to predict physical properties of metallic materials from graph representation of atomic configuration of molecular dynamics (MD) simulation. The developed ML model is employed for the prediction of time variation of the potential energy of a solid–liquid biphasic system of nickel. The learned ML model gives a good prediction on the property of training data. Moreover, it is confirmed that the ML model has generalization performance sufficient to make adequate predictions on unknown graph structures despite the lack of information on interatomic distances in the graph representation. It is significant in this study to show that the graph representation can be a good notation for the prediction of various properties from MD simulations since there is no established notation for atomic configuration of MD simulation especially for large-scale system of metallic materials.

Leptonic Decay Widths for the Composite System of Two Relativistic Fermions

The new relativistic semiclassical leptonic decay widths of vector mesons as the relativistic systems of two quarks with arbitrary masses interacting by means of the funnel-type potentials with Coulomb interaction are obtained. The behavior of the relativistic leptonic decay widths of vector mesons is investigated. Comparison of the behavior for new expression with its relativistic spinless analog is given. Consideration is conducted within the framework of completely covariant quasipotential approach in the Hamiltonian formulation of quantum field theory via a transition to the relativistic configurational representation in the case of two relativistic spin particles of arbitrary masses.

An efficient and random synthesis method for mass exchange networks with multi-component using a node-based vertical non-structural model

Mass exchange network (MEN) synthesis is an important technique to ensure efficient purification of industrial waste gas sources and water streams. However, most existing models for MEN synthesis are not capable of dealing with multi-component problems and achieving optimal designs. In this study, an innovative process integration strategy based on a node-based vertical non-structural model (NV-NSM) is proposed. The developed strategy allows for a more random representation of process configuration and aids in handling both single- and multi-component problems. The NV-NSM arranges several groups of main nodes in the process streams and the sub-nodes in their branches and results in design configurations by connecting any two sub-nodes on the rich and lean streams at the same main node number. When dealing with multi-component synthesis, the number of tray column is directly optimized to balance the mass transfer needs of different components. As part of the current investigation, the random walk algorithm with compulsive evolution (RWCE) is established to realize the simultaneous optimization of multiple variables, including the mass transfer loads, the number of trays, the stream split ratios, and the flowrates of lean streams. Furthermore, the fine-search strategy based on the RWCE is applied to improve global and local optimization performance. Finally, the proposed method is applied to four MEN cases featuring different exchanger specifications and considering both single and multiple components. The implementation and analysis result in more optimal solutions compared to those published in the literature, demonstrating the feasibility and effectiveness of the proposed method in analyzing and evaluating different MEN problems, including multi-component ones.

Psym: Efficient Symbolic Exploration of Distributed Systems

Verification of distributed systems using systematic exploration is daunting because of the many possible interleavings of messages and failures. When faced with this scalability challenge, existing approaches have traditionally mitigated state space explosion by avoiding exploration of redundant states (e.g., via state hashing) and redundant interleavings of transitions (e.g., via partial-order reductions). In this paper, we present an efficient symbolic exploration method that not only avoids redundancies in states and interleavings, but additionally avoids redundant computations that are performed during updates to states on transitions. Our symbolic explorer leverages a novel, fine-grained, canonical representation of distributed system configurations (states) to identify opportunities for avoiding such redundancies on-the-fly. The explorer also includes an interface that is compatible with abstractions for state-space reduction and with partial-order and other reductions for avoiding redundant interleavings. We implement our approach in the tool Psym and empirically demonstrate that it outperforms a state-of-the-art exploration tool, can successfully verify many common distributed protocols, and can scale to multiple real-world industrial case studies across

Towards urbanistic geosituation delineation

Introduction. Modern cities are complex and rapidly expanding systems. For their more effective study, it is necessary to use methods of urban remote sensing, in particular, LiDAR survey. Processed LiDAR survey data, visualized in a 3D scene, model a certain urban configuration that represents a static picture of the relationships between objects, processes and phenomena in the urban environment. The representation of such configurations in the dynamic plane are urbanistic geosituations. The main research objective of the paper is to define the concept and present the essence of the urbanistic geosituation. Results. The urbanistic geosituation is a dynamic aspect of a certain state of the urban environment, in which there are objects, processes and phenomena that are in dialectical unity with this urban environment. The urbanistic geosituation can be represented as a separate area of the urban environment in a certain research context with a specific state that is currently not inherent in other areas. The article describes in detail the property of the structural heredity of geosituations, which can be traced during the growth of cities. New buildings and roads are laid out taking into account the existing layout, thus inheriting the structure of the original geosituations. On the example of the city of Washington using 2D and 3D maps, the article discusses the features of identifying inherited urbanistic geosituations using the general functionality of visual analysis. On the example of the city of Kharkiv are described urban problems that arise as a result of unplanned development and ignoring the structural heredity of urbanistic geosituations. Repeating geosituations with common properties and internal configurations are combined into different rank geosituational patterns, which are tracked on city maps with the naked eye. The higher the rank of the pattern, the more stable it is, and the larger territories it covers in terms of more generalized properties. The formation of geosituations patterns is successfully combined with the feature of collecting and storing LiDAR data, which are divided into many areas of the same size – tiles. An important property of urbanistic geosituations is their variability, which manifests itself in the city study in the context of the daily population concentration. Diverse internal urban processes and phenomena often lead to the emergence of urbanistic geosituations that characterize the temporary gravity centers of the population. To search, identify and analyze urbanistic geosituations, it is necessary to use two key components – global coverage maps and geographic information systems (GIS). The article describes a special web-GIS that combines these components and provides an environment for exploring urbanistic geosituations in a 3D scene. Three use-cases are also proposed for analyzing urban systems at the geosituational level: visibility analysis, buildings energy consumption estimation, and population estimation [11, 21]. Conclusions. The geosituational approach in urban research can significantly improve the urban environment study. The repeatability of urban geosituations and the small data sets that can be obtained using LiDAR surveys provide grounds for their effective analysis and visualization in GIS, as a result of which it is possible to extract urban geosystem properties that can be relevant for the entire city.

Reaction Coordinates for Conformational Transitions Using Linear Discriminant Analysis on Positions.

In this work, we demonstrate that Linear Discriminant Analysis (LDA) applied to atomic positions in two different states of a biomolecule produces a good reaction coordinate between those two states. Atomic coordinates of a macromolecule are a direct representation of a macromolecular configuration, and yet, they are not used in enhanced sampling studies due to a lack of rotational and translational invariance. We resolve this issue using the technique of our prior work, whereby a molecular configuration is considered a member of an equivalence class in size-and-shape space, which is the set of all configurations that can be translated and rotated to a single point within a reference multivariate Gaussian distribution characterizing a single molecular state. The reaction coordinates produced by LDA applied to positions are shown to be good reaction coordinates both in terms of characterizing the transition between two states of a system within a long molecular dynamics (MD) simulation and also ones that allow us to readily produce free energy estimates along that reaction coordinate using enhanced sampling MD techniques.

RenoDSS – A BIM-based building renovation decision support system

Increasing the building renovation rate is a key initiative to drive energy efficiency in the sector and contribute to the European Green Deal. Within this paper, the BIM-based building renovation decision support system RenoDSS is described. RenoDSS (i) automatically generates renovation scenarios based on user preferences and the IFC representation (including second level space boundaries data) of the current building configuration (baseline), (ii) calculates baseline and renovation scenario KPIs, and (iii) identifies the most suitable scenarios by comparing the scenarios’ energy, finance, and sustainability KPIs. Validation conducted at two pilot sites shows that RenoDSS reduces the time for (i) identifying potential renovation scenarios, (ii) calculating their energy and LCA/LCC KPIs, and (iii) producing an IFC file for each renovation scenario by around 80% compared to tool-supported manual means. RenoDSS is used by (i) renovation designers that want to optimize the thermal building hull in combination with PV, solar thermal, and HVAC, as well as (ii) decision makers who want to collaboratively identify the most promising renovation option out of the ones generated by RenoDSS.

Improving the validity of neuroimaging decoding tests of invariant and configural neural representation.

Many research questions in sensory neuroscience involve determining whether the neural representation of a stimulus property is invariant or specific to a particular stimulus context (e.g., Is object representation invariant to translation? Is the representation of a face feature specific to the context of other face features?). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging studies have used operational tests in which a target property is inferred from a significant test against the null hypothesis of the opposite property. For example, the popular cross-classification test concludes that representations are invariant or tolerant when the null hypothesis of specificity is rejected. A recently developed neurocomputational theory suggests two insights regarding such tests. First, tests against the null of context-specificity, and for the alternative of context-invariance, are prone to false positives due to the way in which the underlying neural representations are transformed into indirect measurements in neuroimaging studies. Second, jointly performing tests against the nulls of invariance and specificity allows one to reach more precise and valid conclusions about the underlying representations, particularly when the null of invariance is tested using the fine-grained information from classifier decision variables rather than only accuracies (i.e., using the decoding separability test). Here, we provide empirical and computational evidence supporting both of these theoretical insights. In our empirical study, we use encoding of orientation and spatial position in primary visual cortex as a case study, as previous research has established that these properties are encoded in a context-sensitive way. Using fMRI decoding, we show that the cross-classification test produces false-positive conclusions of invariance, but that more valid conclusions can be reached by jointly performing tests against the null of invariance. The results of two simulations further support both of these conclusions. We conclude that more valid inferences about invariance or specificity of neural representations can be reached by jointly testing against both hypotheses, and using neurocomputational theory to guide the interpretation of results.

Sign Language Recognition using Deep Neural Network

Abstract—Sign language recognition is an attractive research field with a wide range of applications including telesurgery techniques. Another important application of hand gesture recognition is the translation of sign language, which is a complicated structured form of hand gestures. In sign language, the fingers’ configuration, the hand’s orientation, and the hand’s relative position to the body are the basis of structured expressions. The importance of hand gesture recognition has increased due to the prevalence of touchless applications and the rapid growth of the hearing-impaired population. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence modeling. In this paper, a novel system is proposed for dynamic hand gesture recognition using multiple deep learning architectures for hand segmentation, local and global feature representations, and sequence feature globalization and recognition. The proposed system is evaluated on a very challenging dataset, which consists of dynamic hand gestures performed by subjects in an uncontrolled environment. The results show that the proposed system outperforms state-of- the-art approaches, demonstrating its effectiveness.

Interactive Dashboard with Visual Sensing and Fast Reactivity

These days, technology is growing rapidly, and the market has been introduced with lots of fascinating ways to interact with computers. The advancement of deep learning models and hardware technology also enables more applications with fancy features to be built. The importance of hand gesture recognition has increased due to the prevalence of touchless applications. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and a gesture sequence model. This paper proposed an interactive dashboard that could react to hand gestures. This is also an initiative of the Tunku Abdul Rahman University College (TAR UC) Smart Campus project. Deep learning models were investigated in this research and the optimal model was selected for the dashboard. In addition, 20BN Jester Dataset was used for the dashboard development. To set up a more user-friendly dashboard, the data communication stream between the captured input stream and commands among the devices were studied. As to achieve higher responsiveness from the dashboard, evaluation on data communication protocols which were used to pass the input data were included in the study.

A unified representation and retrieval of 3D grain configuration based on signed distance field

In this article, the design and development of a 3D grain configuration representation method with small data and high accuracy are presented, by which a unified grain database is established. There are two vital components in our proposed method: the traditional signed distance field (SDF) method is used for the unified representation of the 3D grain configuration; the proper generalized decomposition is employed to decompose the original SDF function to compress the data. With the proposed method, the reconstruction of 3D grain configuration is realized with small data and high accuracy, based on which the similarity measurement of different grains is proposed and the reuse of the 3D grain configurations is realized. The efficacy and accuracy of the proposed method are validated by reconstructing 3D grain configurations and the burnback curves of two different grain models. The similarities of the eight grains are calculated, which are accurately consistent with the burning surface area calculations results. The experiment results demonstrate that the proposed methods are feasible and efficient, and are significant for design case data and knowledge reuse.