Representation Of Interactions Research Articles

Comparing the interactions between particulate matter and cloud properties over two populated cities in Texas using WRF-Chem fine-resolution modeling

Accurate modeling of aerosol-cloud interactions is essential for reliable weather and air quality simulations, given their significant impact on precipitation patterns, cloud dynamics, and aerosol distributions. This study employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to examine the impact of enhanced meteorological simulations, achieved through advanced microphysics parameterization supported by data assimilation techniques, on air quality across Texas on August 19 and 20, 2022. We tested four distinct configurations: (1) the Morrison two-moment bulk microphysics scheme, (2) Morrison's with observation nudging, (3) the Spectral Bin Microphysics (SBM), and (4) SBM with observation nudging. While the SBM scheme is known for its detailed representation of aerosol-cloud interactions, our focus was on how improvements in meteorological accuracy translate to more precise air quality simulations. Our findings demonstrated a progressive improvement in simulation accuracy, starting with the Morrison's scheme and further enhanced by adopting the SBM scheme, complemented by incorporating observation nudging. Specifically, the combination of the SBM scheme and the nudging substantially enhanced the model's ability to capture convective precipitation events, as shown by better alignment with NEXRAD radar reflectivity, with R increasing from −0.21 to 0.82, IOA from 0.10 to 0.87, and NMB decreasing from 99% to 34% in Houston. The enhanced meteorology translated into more accurate PM2.5 concentration simulations, particularly through the more accurate representation of aerosol washout during precipitation events. In Houston, the SBM scheme with nudging improved the model's PM2.5 simulations significantly, with NMB decreasing from −20% to 5% and IOA improving from 0.43 to 0.61. In San Antonio, improvements were also notable, with NMB improved from −27% to −22%, R increased from 0.48 to 0.82, and IOA increased from 0.66 to 0.86. Our results underscore the crucial role of accurate meteorological simulations in refining our understanding of aerosol behaviors in relation to precipitation patterns, directly enhancing the reliability and effectiveness of air quality modeling.

On the contribution of local anisotropic creep to macroscopic irradiation-induced growth in zirconium alloy polycrystals

ABSTRACT Zirconium alloys used in nuclear reactor exhibit, under fast neutron flux, a macroscopic deformation even without applied stress called irradiation induced growth. Because of the polycrystalline nature of the material, the local growth of individual grains results in strain incompatibilities yielding to intergranular stresses and thus to local creep. In order to study and understand the influence of the local anisotropic creep on the macroscopic growth behaviour, an analytical and a numerical study have been undertaken, using Voigt and self-consistent estimates and also fast Fourier transform simulations. It is shown that the anisotropic local creep has a strong influence on the effective macroscopic growth strain of the polycrystal. Especially, when the deformation is difficult along the 〈 c 〉 axis, a growth enhancement effect is observed. This phenomenon is well explained in the frame of the Voigt estimate using a reduced fibre texture. Computations conducted using a texture representative of the industrial material provide a quantitative confirmation of this enhancement effect. This work demonstrates the significant contribution of the local anisotropic creep to the macroscopic in-reactor growth strain of zirconium alloys. Highlights The local creep anisotropy has a strong influence on macroscopic irradiation growth of polycrystalline zirconium tubes. The macroscopic axial growth can be significantly enhanced by the interactions between grains when local creep deformation is prevented along the 〈 c 〉 axis. A simplified representation of grain interactions (reduced fibre texture and Voigt estimate) offers a qualitative understanding of this phenomenon. Refined estimates of the effective growth strain are provided for realistic crystallographic texture using polycrystalline unit-cell FFT computations.

Enhancing the synergy governance capability of urban communities based on Bayesian network: A case of Jinan city

The rapid pace of urbanization has brought about a plethora of challenges for urban communities, prompting the advocacy of collaborative governance as a means to tackle management issues effectively. However, fostering strong collaboration among multiple governance entities remains a significant hurdle in urban communities governance. This study addresses this challenge by exploring the complex structure of urban communities' collaborative governance systems, with a focus on enhancing collaborative governance capability. Drawing on Bayesian network (BN) theory and utilizing the case of Jinan, this article investigates the intrinsic mechanisms of achieving collaboration in urban communities. This study establishes a Bayesian network model for collaborative governance of urban communities, providing a visual representation of the intricate interactions among multiple governance entities. We employ a data-knowledge-driven approach for BN structure learning. This approach tackles issues such as the lack of cyclic dependency capability and the impact of small datasets on model accuracy. Furthermore, we analyze the collaborative mechanism through Bayesian reasoning. The findings underscore the pivotal entities and five key factors that significantly influence collaborative governance capability. Lastly, policy implications are drawn to enhance the collaborative governance capability of urban communities.

MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism.

Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they rarely consider multimodal features of drugs. Moreover, learning the interaction representations between drugs and targets needs further exploration. Here, we proposed a simple M ulti-modal G ating N etwork for DTI prediction, MGNDTI, based on multimodal representation learning and the gating mechanism. MGNDTI first learns the sequence representations of drugs and targets using different retentive networks. Next, it extracts molecular graph features of drugs through a graph convolutional network. Subsequently, it devises a multimodal gating network to obtain the joint representations of drugs and targets. Finally, it builds a fully connected network for computing the interaction probability. MGNDTI was benchmarked against seven state-of-the-art DTI prediction models (CPI-GNN, TransformerCPI, MolTrans, BACPI, CPGL, GIFDTI, and FOTF-CPI) using four data sets (i.e., Human, C. elegans, BioSNAP, and BindingDB) under four different experimental settings. Through evaluation with AUROC, AUPRC, accuracy, F1 score, and MCC, MGNDTI significantly outperformed the above seven methods. MGNDTI is a powerful tool for DTI prediction, showcasing its superior robustness and generalization ability on diverse data sets and different experimental settings. It is freely available at https://github.com/plhhnu/MGNDTI.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Stargazing Live!: Inspiring with semi-live astronomy data; teaching curriculum topics using smart education tools

Stargazing Live! aims to capture the imagination of learners with a combination of live and interactive planetarium lessons, real astronomical data, and lessons built around interactive knowledge representations. The lessons were created using a co-creation model and tackle concepts in the pre-university (astro)physics which students find difficult to grasp with traditional interventions. An evaluation study in 9 Dutch classrooms showed that learners are inspired and engaged by the planetarium lessons but are not always able to link the content to the classroom. Pre- and post-tests showed that the accompanying star properties activity significantly increased learners’ understanding of the causal relationships between mass and other properties (such as luminosity, gravity, and temperature) in a main sequence star.

Graph Neural Networks for Predicting Solubility in Diverse Solvents Using MolMerger Incorporating Solute-Solvent Interactions.

The prediction of solubility is a complex and challenging physicochemical problem that has tremendous implications for the chemical and pharmaceutical industry. Recent advancements in machine learning methods have provided a great scope for predicting the reliable solubility of a large number of molecular systems. However, most of these methods rely on using physical properties obtained from experiments and expensive quantum chemical calculations. Here, we developed a method that utilizes a graphical representation of solute-solvent interactions using "MolMerger," which captures the strongest polar interactions between molecules using Gasteiger charges and creates a graph incorporating the true nature of the system. Using these graphs as input, a neural network learns the correlation between the structural properties of a molecule in the form of node embedding and its physicochemical properties as the output. This approach has been used to calculate molecular solubility by predicting the Log solubility values of various organic molecules and pharmaceuticals in diverse sets of solvents.

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data. In this study, leveraging the source-reconstructed and anatomically-resolved EEG data as inputs, we sought to employ CNN as an interpretable model to uncover task-specific interactions between brain regions, rather than simply to utilize it as a black box decoder. To this end, our CNN model was specifically designed to learn pairwise interaction representations for 10 cortical regions from five-second inputs. By exclusively utilizing these features for decoding, our model was able to attain a median accuracy of 77.56% for within-participant and 65.14% for cross-participant classification. Through ablation analysis together with dissecting the features of the models and applying cluster analysis, we were able to discern the presence of alpha-band-dominated inter-hemisphere interactions, as well as alpha- and beta-band dominant interactions that were either hemisphere-specific or were characterized by a contrasting pattern between the right and left hemispheres. These interactions were more pronounced in parietal and central regions for within-participant decoding, but in parietal, central, and partly frontal regions for cross-participant decoding. These findings demonstrate that our CNN model can effectively utilize features known to be important in auditory attention tasks and suggest that the application of domain knowledge inspired CNNs on source-reconstructed EEG data can offer a novel computational framework for studying task-relevant brain interactions.

Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage.

Aneurysmal subarachnoid hemorrhage is a medical emergency of brain that has high mortality and poor prognosis. Causal effect estimation of treatment strategies on patient outcomes is crucial for aneurysmal subarachnoid hemorrhage treatment decision-making. However, most existing studies on treatment decision-making support of this disease are unable to simultaneously compare the potential outcomes of different treatments for a patient. Furthermore, these studies fail to harmoniously integrate the imaging data with non-imaging clinical data, both of which are useful in clinical scenarios. In this paper, we estimate the causal effect of various treatments on patients with aneurysmal subarachnoid hemorrhage by integrating plain CT with non-imaging clinical data, which is represented using structured tabular data. Specifically, we first propose a novel scheme that uses multi-modality confounders distillation architecture to predict the treatment outcome and treatment assignment simultaneously. With these distilled confounder features, we design an imaging and non-imaging interaction representation learning strategy to use the complementary information extracted from different modalities to balance the feature distribution of different treatment groups. We have conducted extensive experiments using a clinical dataset of 656 subarachnoid hemorrhage cases, which was collected from the Hospital Authority Data Collaboration Laboratory in Hong Kong. Our method shows consistent improvements on the evaluation metrics of treatment effect estimation, achieving state-of-the-art results over strong competitors. Code is released at https://github.com/med-air/TOP-aSAH.

Understanding geomorphodynamics in the Pergamon micro-region from a socio-ecological perspective

A systematic interdisciplinary approach based on the socio-ecological model of the Vienna school has been adopted to achieve a more nuanced and multifaceted understanding of the ancient metropolis of Pergamon (western Anatolia) and its micro-region. The city of Pergamon ranks among the ‘guiding fossils’ of urban culture in antiquity. We describe how the socio-ecological model is subject to adaptation and discussion to fit the needs and circumstances of archaeology. In focussing on geomorphodynamics, we use several approaches to conceptualise and model selected aspects of human-environment interactions, integrating data from physical geography, archaeology, building archaeology (Bauforschung) and ancient history. The model includes several dimensions of the social metabolism of Pergamon, first and foremost the carrying capacity of the environment and demographics, comprising population increase and labour as an active investment in nature. Geomorphodynamics are regarded as major ‘events’ in the model, related to the social metabolism (e.g. increased erosion/deposition in the micro-region in relation to urban sprawl). With the social-ecological model, it is possible – and becomes imperative – to include the perception and representation of human-environment interactions manifested in, for example, administrative patterns and religious practices or architecture and built infrastructure (such as terraces, riverbank stabilisation, substraction terraces and substraction bridges). Geomorphodynamics also involve various aspects of the perception of the environment, though these are not recorded in ancient texts on Pergamon known to date. Concurrently, the importance of the model in organising, structuring, and communicating interdisciplinary collaboration and discourse is highlighted.

RelChronVis: an interactive web application for visualizing the relative chronology of language changes

AbstractModels of the relative chronology of language changes resemble the structure of a graph. They consist of a set of changes that are in many cases related to each other. It follows that they can be visualized in a diagram, which opens up new possibilities for publishing historical linguistics data. In this paper, we introduce the RelChronVis application, a web application designed for visualizing language history. Based on detailed models of the relative chronology of Russian and Croatian sound changes, we show how interactive graph representations can help visualize language changes and the relationships between them. In addition to graphs, the application provides textual information about the included changes and relations as well as reconstructions of example lexemes. The RelChronVis application is primarily designed for doing research and for teaching. It allows the researcher to transparently compare different models and to access relevant data. At the same time, the RelChronVis application can be used to teach the history of the included languages as well as methods of historical linguistics. It provides the option of visualizing custom data and can easily be accommodated to novel discoveries. We believe that these features and uses make the RelChronVis application an immensely useful tool for publishing models of the relative chronology of language changes.

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery.

We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen's ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein-ligand complexes.

Spatial and Surface Correspondence Field for Interaction Transfer

In this paper, we introduce a new method for the task of interaction transfer. Given an example interaction between a source object and an agent, our method can automatically infer both surface and spatial relationships for the agent and target objects within the same category, yielding more accurate and valid transfers. Specifically, our method characterizes the example interaction using a combined spatial and surface representation. We correspond the agent points and object points related to the representation to the target object space using a learned spatial and surface correspondence field, which represents objects as deformed and rotated signed distance fields. With the corresponded points, an optimization is performed under the constraints of our spatial and surface interaction representation and additional regularization. Experiments conducted on human-chair and hand-mug interaction transfer tasks show that our approach can handle larger geometry and topology variations between source and target shapes, significantly outperforming state-of-the-art methods.

Social media’s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation

Most existing methods for detecting fraudulent news or other forms of disinformation primarily rely on user profiling or content analysis, determining whether a given article aligns with a user’s stylistic or content-related preferences. This research departs from conventional approaches by concentrating not only on the characteristics of shared content but also on user interactions and the resultant topologies of content propagation trees. In our study, we have introduced a deep learning model based on Graph Convolutional Neural Networks (GCNN) and enhanced with multi-head attention mechanisms. This model leverages a wide range of psycholinguistic attributes extracted from users’ posts, including sentiment, emotional content, linguistic features, personality traits, readability, and communication style. We further enrich these attributes with BERT embeddings to improve textual representation. Our research framework involves creating two distinct graph networks: the user interaction graph and the semantic propagation graph. These graphical representations are essential for visualizing dynamic interactions and patterns of information dissemination among users. For each user, we generate a set of carefully crafted features that capture their unique characteristics and behaviors. These user-specific features are then integrated into the User Interaction Graph, enabling a more nuanced understanding and representation of user interactions within our proposed model. The proposed framework has demonstrated superior performance on benchmark datasets, achieving accuracies and precisions of 91.07% and 91.24% on the FakeNewsNet dataset, and 94.20% and 94.92% on the FibVid dataset, respectively. The results obtained from the experimental evaluation have also revealed the effect of various parameters significant to profile disinformers, signifying a substantial improvement in the field of fake news detection and profiling.

More than Steepness: Introducing Slope Quantitatively with Interactive Dynamic Representations

We report on a long-term collaboration with ninth-grade mathematics teachers at a linguistically diverse high school in which we examined the intersection between mathematics and language, and how language and mathematical thinking can be developed together in a multilingual setting. We co-designed lessons that promoted conceptual understanding, increased students’ access to grade level mathematics and created opportunities for students who are classified as English learners to participate in discussions. Our content focus was linear and exponential rates of change. In this article, we focus on linear rate of change and its connection to slope. After identifying three distinct meanings for and uses of slope in a ninth-grade mathematics textbook: (a) slope as steepness, (b) slope as rate of change, and (c) slope as a property of collinearity (presented in this order) and observing student interactions around slope concepts, we considered whether it may be more beneficial for students, particularly emergent multilingual students, to develop a robust quantitative understanding of slope first, then introduce the other two meanings of slope as an application of rate of change. To achieve this, we created a series of lessons incorporating dynamic interactive technology and mathematical language routines. We share three of these lessons in this article and include a classroom vignette illustrating the classroom interactions that were achieved through using our first Desmos activity.

A methodological approach for enhancing visualization of country data representation in the presence of significant spatial disparity

In this article, we present a methodological approach to address spatial disparity in global data representation, introducing an algorithm called Flexible Mapping to Understand Spatial Analysis (FLEMUSA). We utilize world maps to depict various data points across countries, revealing substantial variation among them. However, conventional choropleth maps often fail to effectively represent regions with sparse data, obscuring valuable insights. To mitigate this issue, we propose interactive graphical methods in both two and three dimensions, implemented through open-source Python code accessible via Google Colab. Our approach includes several contributions such as excluding countries without data from the representation, scaling magnitudes within country borders, focusing on regional analysis, and using logarithmic scales for bubble maps proportional to country sizes. Additionally, we offer interactive 2D and 3D representations, rotatable 3D representations, and zoomable options, facilitating enhanced visualization of regional similarities amidst data heterogeneity. Through this algorithm, we aim to improve the clarity and interpretability of spatial data analysis, integrating solutions for extreme data overdispersion, all programmed with open-source code.-Utilization of world maps for visual representation of data across countries mitigating the overdispersion step by step.-Implementation of graphical methods, including interactive 2D and 3D maps, to address spatial disparity.-Provision of open-source code for customizable graphical representations, facilitating implementation in online journals as interactive code snippets.

Stochastic estimation of wake-induced loads in wind farms using conformalized graph neural networks

In the evolving landscape of sustainable energy, wind power has become a cornerstone in the transition towards renewable energy sources. With significant advancements in turbine technology and simulation methods, wind farms are now a cost-effective alternative to traditional power plants. Nevertheless, optimizing the performance and lifespan of wind turbines remains challenging, especially when it comes to predicting and managing the cumulative load on turbine structures. Wake effects, which result from the complex aerodynamic interplay between turbines, reduce energy efficiency and increase mechanical stress on turbine components. A precise assessment of wake-induced loads is therefore vital for estimating the Remaining Useful Lifetime (RUL) of turbines. To address these challenges, we introduce a novel approach using Graph Neural Networks (GNNs) in combination with Conformal Predictors to model wind farms and evaluate wake-induced loads while estimating uncertainty. GNNs are particularly adept at capturing the complex interactions in a wind farm due to their ability to model graph-structured data. This enables a more accurate representation of the aerodynamic interactions between turbines. Alongside the GNN framework, we employ Conformal Predictors for uncertainty estimation. Conformal Predictors provide statistically valid prediction sets based on past data and minimal assumptions, allowing us to estimate uncertainty bounds with improved confidence. The fusion of GNNs with Conformal Predictors offers a robust framework for predicting turbine loads, while reliably quantifying the uncertainty associated with these predictions.

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction.

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module, including this data-driven mechanism, is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance.

Joint low-rank tensor fusion and cross-modal attention for multimodal physiological signals based emotion recognition

Objective. Physiological signals based emotion recognition is a prominent research domain in the field of human-computer interaction. Previous studies predominantly focused on unimodal data, giving limited attention to the interplay among multiple modalities. Within the scope of multimodal emotion recognition, integrating the information from diverse modalities and leveraging the complementary information are the two essential issues to obtain the robust representations. Approach. Thus, we propose a intermediate fusion strategy for combining low-rank tensor fusion with the cross-modal attention to enhance the fusion of electroencephalogram, electrooculogram, electromyography, and galvanic skin response. Firstly, handcrafted features from distinct modalities are individually fed to corresponding feature extractors to obtain latent features. Subsequently, low-rank tensor is fused to integrate the information by the modality interaction representation. Finally, a cross-modal attention module is employed to explore the potential relationships between the distinct latent features and modality interaction representation, and recalibrate the weights of different modalities. And the resultant representation is adopted for emotion recognition. Main results. Furthermore, to validate the effectiveness of the proposed method, we execute subject-independent experiments within the DEAP dataset. The proposed method has achieved the accuracies of 73.82% and 74.55% for valence and arousal classification. Significance. The results of extensive experiments verify the outstanding performance of the proposed method.

Questioning the nature and origins of the "social agent" concept.

Spelke posits that the concept of "social agent," who performs object-directed actions to fulfill social goals, is the first noncore concept that infants acquire as they begin to learn their native language. We question this proposal on empirical grounds and theoretical grounds, and propose instead that the representation of object-mediated interactions may be supported by a dedicated prelinguistic mechanism.