Synthetic Representation Research Articles

Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm

Reliability analysis of structures under earthquake loading represents a significant engineering challenge. This is due to the required and rather numerically involving non-linear dynamic analysis, the large computational burden when targeting small failure probabilities, and the synthetic earthquake model representation that may contain thousands of random variables. Subset Simulation is an efficient reliability analysis technique that can handle the challenge of a high-dimensional space with a reduced number of structural analysis calls compared to crude Monte Carlo Simulation. In this contribution, firstly, we investigate the conditions for which Subset Simulation performs efficiently. Thereafter we propose an enhancement to the existing Subset Simulation schemes that shows significant potentials for enhancing the strategy for the starting of the Markov Chain Monte Carlo simulations whenever a new level is reached in the Subset Simulation. Finally, the information gathered from the simulations is investigated to verify that Subset Simulation provides meaningful results from a physical point of view.

An open data-based model for generating a synthetic low-voltage grid to estimate hosting capacity

This study develops and applies an open data-based reference electricity grid analysis (REGAL) model designed to create a synthetic representation of a low-voltage (LV) grid for a country-size geographic area. The model enables large-scale grid simulation in which new loads, such as electric vehicle charging, can be added to estimate their impacts on the current LV grid. The modeling is carried out in three steps: (1) generation of a synthetic LV grid; (2) addition of residential loads, including electric vehicle charging; and (3) evaluating if the grid capacity is exceeded. The grid is generated by selecting transformers and cables so that the system can fulfill the current demand while meeting national regulations and standards for distribution grids, all at the lowest total cost. This paper presents the results of calibration and validation against real-world data for the predicted electricity demands and synthetic grid generated by the model. Different calibration values were explored, and the accuracy of the estimations of grid capacities was calibrated using proprietary real-world data from grid operators. For a region with multiple grid cells, an average deviation from real-world data of ±10 % was achieved. For an average area of 1 km2, the error was 44.5 %, which means that the model is not suitable for analysis on this geographic level. However, the level of accuracy is deemed sufficient for initial estimations of hosting capacity for larger geographic areas, such as a region or a country, thereby enabling estimations of hosting capacity in new areas that lack publicly accessible grid capacities.

Nationwide synthetic human mobility dataset construction from limited travel surveys and open data

AbstractIn recent years, the explosion of extensive geolocated datasets related to human mobility has presented an opportunity to unravel the mechanism behind daily mobility patterns on an individual and population level; this analysis is essential for solving social matters, such as traffic forecasting, disease spreading, urban planning, and pollution. However, the release of such data is limited owing to the privacy concerns of users from whom data were collected. To overcome this challenge, an innovative approach has been introduced for generating synthetic human mobility, termed as the “Pseudo‐PFLOW” dataset. Our approach leverages open statistical data and a limited travel survey to create a comprehensive synthetic representation of human mobility. The Pseudo‐PFLOW generator comprises three agent models that follow seven fundamental daily activities and captures the spatiotemporal pattern in daily travel behaviors of individuals. The Pseudo‐PFLOW dataset covers the entire population in Japan, approximately 130 million people across 47 prefectures, and has been compared with the existing ground truth dataset. Our generated dataset successfully reconstructs key statistical properties, including hourly population distribution, trip volume, and trip coverage, with coefficient of determination values ranging from 0.5 to 0.98. This innovative approach enables researchers and policymakers to access valuable mobility data while addressing privacy concerns, offering new opportunities for informed decision‐making and analysis.

Examining Ethical Aspects of AI: Addressing Bias and Equity in the Discipline

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

Formation of fluvial xenolandscapes of Martian valleys and channels


 
 
 Contemporary concepts of Martian Valley formation and the current state of their study are examined. Three-dimensional modeling was conducted using the examples of two regions on Mars, the Lani Chaos Region and Kasei Vallis, to determine and map the main morphological features typical of Martian valleys as a basis for identifying xenolandscapes. The xenaxiomatic author’s concept was applied as a means of investigating xenolandscape diversity on the surface of Earth-like planets, in this case, Mars. The xenaxiomatic concept is the result of incorporating and utilizing traditional knowledge of the differentiation and application of axioms of cognition. Specialized for xenoplanetological use, this integrative cognitive tool is a variant of an axiomatic approach. The main essence of this approach, in the case of distinguishing xenolandscapes, lies in developing a unified scheme for searching for corresponding xenolandscape elements that are axiomatically elementary fragments of the planet’s surface, followed by their classification and xenolandscape interpretation. Scientific awareness has been achieved by filling it with all available information from various natural sciences related to xenoplanetology and subsequent exploratory xenolandscape interpretation and synthetic cartographic representation of characteristic, typical local xenolandscape formations on Mars (part of the Kasei Vallis valley). These depicted objects are extra-rank, meaning they have not yet been ranked in terms of xenolandscape formations but are uniformly formed due to the synergy of influences and interactions of different forces, yet genetically stemming from the same xenolandscape-forming factors of material and energy-field xenonature. A mechanism for the formation of modern Martian valleys is proposed through the influx of heat flow and impact events. Exploratory xenolandscape studies, with their positions verified by this conducted research in terms of theoretical, methodological, and methodical content, have become an unexpected benchmark for the effectiveness of similar theoretical and methodological components of classical landscape science, with which the research capabilities of landscape science can and should be compared.
 
 

Robust 3D Tracking with Quality-Aware Shape Completion

3D single object tracking remains a challenging problem due to the sparsity and incompleteness of the point clouds. Existing algorithms attempt to address the challenges in two strategies. The first strategy is to learn dense geometric features based on the captured sparse point cloud. Nevertheless, it is quite a formidable task since the learned dense geometric features are with high uncertainty for depicting the shape of the target object. The other strategy is to aggregate the sparse geometric features of multiple templates to enrich the shape information, which is a routine solution in 2D tracking. However, aggregating the coarse shape representations can hardly yield a precise shape representation. Different from 2D pixels, 3D points of different frames can be directly fused by coordinate transform, i.e., shape completion. Considering that, we propose to construct a synthetic target representation composed of dense and complete point clouds depicting the target shape precisely by shape completion for robust 3D tracking. Specifically, we design a voxelized 3D tracking framework with shape completion, in which we propose a quality-aware shape completion mechanism to alleviate the adverse effect of noisy historical predictions. It enables us to effectively construct and leverage the synthetic target representation. Besides, we also develop a voxelized relation modeling module and box refinement module to improve tracking performance. Favorable performance against state-of-the-art algorithms on three benchmarks demonstrates the effectiveness and generalization ability of our method.

Diving-wave time-lapse delay for CO2 thin layer detection

SUMMARY We have derived an analytical approximate expression to estimate the delay in diving seismic waves due to thin layers of CO2. The expression is valid for high frequencies and can be used to estimate the delay in diving waves at seismic frequencies for large separations between the source and receiver (offset). The approximation may be used to assess CO2 detection limits using diving waves and to support survey planning for CO2 monitoring and full-waveform inversion (FWI) cycle skipping analysis. In this study, we analyse the diving-wave response to a thin layer of CO2 for band-limited data using acoustic finite-difference modelling, and compare the results against the analytical calculations. We find that the responses are offset-dependent and related to double- and single-leg interactions between the diving waves and the CO2. To test the methods, we created a synthetic representation of the 2010 subsurface conditions for the top CO2 layer at the Sleipner storage complex in the North Sea, by combining base and monitor post-stack seismic data with field velocity trends. Using the acoustic finite-difference method, we model pre-stack data that captures the complexity of field data and demonstrate the use of the diving-wave delay for CO2 migration monitoring and CO2 thin layer detection.

Synthetic Representation of Credit ETFs

Synthetic Representation of Credit ETFs

Simulation-Based Medical Education: A Boon for Medical Students? - An Integrative Review

Simulation is a synthetic representation of a real-world process with sufficient reliability to facilitate learning through contemplation and practice without the hazard, innate in a real-life experience. Nowadays, simulation is a useful accompaniment to medical education as pre-exposure to necessary clinical skills as exposure in the real clinical setting may be insufficient. Clinical skills and performance are considered core proficiency and are crucial to the professionals. This can enable the students to familiarize themselves with patient examination and hands-on- training by using models before coming across patients directly. Simulators are broadly classified into two broad categories: 1. High-fidelity Simulators and 2. Low-fidelity simulators. The fidelity of a simulator is decided by the extent to which it provides realism through characteristics. Simulation Based Medical Education (SBME) provides a safe environment for the students to acquire their psychomotor skill but is not necessarily better than other types of instruction as there is a high degree of variability between studies. SBME has been introduced in the health care field and now it is becoming one of the most popular teaching techniques for improving patient safety and care. It would be advantageous if it is included in medical curricula as it may proof boon for the young medicos. Keywords: Medical simulation; SBME; Simulators

Deep Learning–based Dynamic User Alignment in Social Networks

Academics and businesses are paying intense attention to social network alignment, which centers various social networks around their shared members. All studies to date treat the social network as static and ignore its innate dynamism. In reality, an individual's discriminative pattern is embedded in the dynamics of social networks, and this information may be used to improve social network alignment. This study finds that these dynamics can reveal more apparent patterns better suited to lining up the social web of things (SWoT). The correlation between the user structure and attributes for each social network must be maintained to combine the binary dynamics and make the original synthetic embedding representation. Finally, the initial embedding of each network is projected to a target subspace as part of the semi-supervised spatial transformation learning process. The Dynamic Social Network Alignment approach outperforms the current mainstream algorithm by 10% in this article's extensive series of trials using real-world datasets. The findings of this study show that this alignment of enormous networks addresses the volume, variety, velocity, and veracity (or 4Vs) of vast networks. To improve the efficacy and resilience of an adversarial network alignment, adversarial learning techniques can be applied. The results show that the model with structure, attribute, and time information performs the best, while the model without attribute information comes in second, the model without time information performs mediocrely, and the model without structure information performs the worst.

Visiting Heritage Sites in AR and VR

Advances in digitization technologies have made possible the digitization of entire archaeological sites through a combination of technologies, including aerial photogrammetry, terrestrial photogrammetry, and terrestrial laser scanning. At the same time, the evolution of computer algorithms for data processing and the increased processing power made possible the combination of data from multiple scans to create a synthetic representation of large-scale sites. Finally, post-processing techniques and the evolution of computer and mobile GPUs and game engines have made possible the exploitation of digitization outcomes to further scientific study and historical preservation. This route was opened by the gaming industry. In terms of research, the exploitation of these new assets in conjunction with new visual rendering technologies, such as virtual and augmented reality, can create new dimensions for education and leisure. In this paper, we explore the usage of large-scale digitization of a heritage site to create a unique virtual visiting experience that can be accessed offline in VR and AR and on-site when visiting the archaeological site.

Option pricing generators

We characterize a class of option pricing models by their algebraic structure. Option prices are monoids, that is operators endowed with the commutativity and associativity property and an identity element. If the price of the underlying asset is bounded, the operator corresponds to the concept of t-conorm, while if it is defined on the positive real line the operator is a pseudo-addition. These operators have the same no-arbitrage properties as the classical option pricing models, but are also associative. Each model in this class is characterized by a univariate increasing function that is defined the generator of the model. The generator encodes a synthetic representation of the probability structure of the underlying asset. We provide no arbitrage conditions for the generators and practical guidelines to construct them.

Dynamic horizon selection methodology for model predictive control in buildings

The interest in model predictive control (MPC) for buildings has grown in recent years due to the widespread implementation of dynamic electricity tariffs, energy flexibility and distributed energy resources. The MPC applied on buildings is a computational-based methodology used to optimize the performance of heating, ventilation and air conditioning systems (HVAC) by predicting the energy behavior and minimizing a specific cost function in a determined forecasting horizon. The forecasting horizon is one of the critical parameters in MPC design applied in buildings; it should be long enough to activate the buildings’ flexibility potential, but the computational resources grow exponentially with the horizon increase, which could difficult the real-time operation. Furthermore, long periods of non-occupancy, holidays or abrupt comfort-bound changes can significantly affect the optimal forecasting time horizon length. Unfortunately, very few studies have focused on ascertaining this key optimization process aspect. The contribution of this research paper is to demonstrate, through an innovative methodology, that the optimal horizon length can be dynamically updated according to the effects of building inertia. This methodology is validated by assessing the reduction of the economic costs of a space heating system based on a synthetic representation of an experimental building placed in Germany.

The impact of heavy-duty vehicle electrification on large power grids: A synthetic Texas case study

The electrification of heavy-duty vehicles (HDEVs) is a nascent and rapidly emerging avenue for decarbonization of the transportation sector. In this paper, we examine the impacts of increased vehicle electrification on the power grid infrastructure, with particular focus on HDEVs. We utilize a synthetic representation of the 2000-bus Texas transmission grid, realistic representations of multiple distribution grids in Travis county, Texas, as well as transit data pertaining to HDEVs, and develop our own open-sourced publicly available simulation tool, to uncover the consequences of HDEV electrification, and expose the limitations imposed by existing electric grid infrastructure. Our analysis reveals that grid-wide voltage problems that are spatiotemporally correlated with the mobility of HDEVs may occur even at modest penetration levels. In fact, we find that as little as 11% of heavy duty vehicles in Texas charging simultaneously can lead to significant voltage violations on the transmission network that compromise grid reliability. Furthermore, we find that just a few dozen EVs charging simultaneously can lead to voltage violations at the distribution level.

Evaluation of automated radiostereometric image registration in total knee arthroplasty utilizing a synthetic-based and a CT-based volumetric model.

Radiostereometic analysis (RSA) is an accurate method for rigid body pose (position and orientation) in three-dimensional space. Traditionally, RSA is based on insertion of periprosthetic tantalum markers and manual implant contour selection which limit clinically application. We propose an automated image registration technique utilizing digitally reconstructed radiographs (DRR) of computed tomography (CT) volumetric bone models (autorsa-bone) as a substitute for tantalum markers. Furthermore, an automated synthetic volumetric representation of total knee arthroplasty implant models (autorsa-volume) to improve previous silhouette-projection methods (autorsa-surface). As reference, we investigated the accuracy of implanted tantalum markers (marker) or a conventional manually contour-based method (mbrsa) for the femur and tibia. The data are presented as mean (standard deviation). The autorsa-bone method displayed similar accuracy of -0.013 (0.075) mm compared to the gold standard method (marker) of -0.013 (0.085). The autorsa-volume with 0.034 (0.106) mm did not markedly improve the autorsa-surface with 0.002 (0.129) mm, and none of these reached the mbrsa method of -0.009 (0.094) mm. In conclusion, marker-free RSA is feasible with similar accuracy as gold standard utilizing DRR and CT obtained volumetric bone models. Furthermore, utilizing synthetic generated volumetric implant models could not improve the silhouette-based method. However, with a slight loss of accuracy the autorsa methods provide a feasible automated alternative to the semi-automated method.

Covariate-Assisted Sparse Tensor Completion

We aim to provably complete a sparse and highly missing tensor in the presence of covariate information along tensor modes. Our motivation comes from online advertising where users’ click-through-rates (CTR) on ads over various devices form a CTR tensor that has about 96% missing entries and has many zeros on nonmissing entries, which makes the standalone tensor completion method unsatisfactory. Beside the CTR tensor, additional ad features or user characteristics are often available. In this article, we propose Covariate-assisted Sparse Tensor Completion (COSTCO) to incorporate covariate information for the recovery of the sparse tensor. The key idea is to jointly extract latent components from both the tensor and the covariate matrix to learn a synthetic representation. Theoretically, we derive the error bound for the recovered tensor components and explicitly quantify the improvements on both the reveal probability condition and the tensor recovery accuracy due to covariates. Finally, we apply COSTCO to an advertisement dataset consisting of a CTR tensor and ad covariate matrix, leading to 23% accuracy improvement over the baseline. An important by-product is that ad latent components from COSTCO reveal interesting ad clusters, which are useful for better ad targeting. Supplementary materials for this article are available online.

Multiscale Modeling for Texture and Grain Topology of Polycrystalline Microstructures Under Uncertainty

The present work addresses multiscale modeling for grain topology of polycrystalline microstructures under the effects of the microstructural uncertainties. The special focus is on the titanium-7wt%-aluminum alloy (Ti-7Al), which is a candidate material for many aerospace systems owing to its outstanding mechanical performance in elevated temperatures. The electron backscatter diffraction samples of Ti-7Al in small-scales are used to reconstruct the alloy microstructures in larger domains. The reconstruction provides a statistically equivalent synthetic representation to the small-scale test samples while introducing epistemic uncertainty on microstructural features. Here, the grain topology of the polycrystalline microstructures is quantified using shape moment invariants. These invariant parameters can capture the shapes of physical objects with a numerical representation, and they are invariant to shape transformations. To quantify the effects of the uncertainties on the homogenized properties of the microstructures, a surrogate model is developed as a function of shape moment invariants with Gaussian process regression by utilizing the experiments and crystal plasticity simulations for training data.

Visualization of medical concepts represented using word embeddings: a scoping review

BackgroundAnalyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), to learn dense and low-dimensional word representations from large unlabeled corpora that capture the implicit semantics of words. Models like Word2Vec, GloVe or FastText have been broadly applied and reviewed in the bioinformatics and healthcare fields, most often to embed clinical notes or activity and diagnostic codes. Visualization of the learned embeddings has been used in a subset of these works, whether for exploratory or evaluation purposes. However, visualization practices tend to be heterogeneous, and lack overall guidelines.ObjectiveThis scoping review aims to describe the methods and strategies used to visualize medical concepts represented using word embedding methods. We aim to understand the objectives of the visualizations and their limits.MethodsThis scoping review summarizes different methods used to visualize word embeddings in healthcare. We followed the methodology proposed by Arksey and O’Malley (Int J Soc Res Methodol 8:19–32, 2005) and by Levac et al. (Implement Sci 5:69, 2010) to better analyze the data and provide a synthesis of the literature on the matter.ResultsWe first obtained 471 unique articles from a search conducted in PubMed, MedRxiv and arXiv databases. 30 of these were effectively reviewed, based on our inclusion and exclusion criteria. 23 articles were excluded in the full review stage, resulting in the analysis of 7 papers that fully correspond to our inclusion criteria. Included papers pursued a variety of objectives and used distinct methods to evaluate their embeddings and to visualize them. Visualization also served heterogeneous purposes, being alternatively used as a way to explore the embeddings, to evaluate them or to merely illustrate properties otherwise formally assessed.ConclusionsVisualization helps to explore embedding results (further dimensionality reduction, synthetic representation). However, it does not exhaust the information conveyed by the embeddings nor constitute a self-sustaining evaluation method of their pertinence.

Integration of Environmental Issues in Axiomatic Design to Pursue Sustainable Products

Product design is an activity that must be supported by information in order to allow designers to conceive solutions to real problems that do not introduce further issues, first of all, environmental concerns. Axiomatic design is an approach that provides the possibility to check whether a design solution is functionally valid and it can also be extended considering eco-design elements. In a synthetic representation of 1D and 2D arrays, it can inform designers about the level of sustainability of the product on which they have been working on since the first phase of design when they start to embody functionalities by introducing real components, and first assemblies appear. To achieve this task, the domains considered in the original formulation of Axiomatic Design have been revised, and a new domain has been introduced. This allows designers to take into account all the phases of product life and improve design solutions to avoid introducing structures, components, and functionalities that might be the cause of environmental problems. The paper describes such new mapping among domains and applies it to design a daily life device. The contents of the new data structure will be presented and discussed.

Comparison between Helical Axis and SARA Approaches for the Estimation of Functional Joint Axes on Multi-Body Modeling Data

Functional methods usually allow for a flexible and accurate representation of joint kinematics and are increasingly implemented both for clinical and biomechanics research purposes. This paper presents a quantitative comparison between two widely adopted methods for functional axis estimation, that is, the helical axis theory and the symmetrical axis of rotation approach (SARA). To this purpose, a multi-body model was developed to simulate the lower limb of a subject. This model was designed to reproduce different motion patterns, that is, by selecting the active degrees of freedom of the simulated ankle joint. Thanks to virtual markers attached to each segment, the multi-body model was used to generate simulated motion capture data that were then analyzed by instantaneous helical axes and SARA algorithms. To achieve a synthetic representation of joint kinematics, a mean helical axis and an average SARA functional axis were estimated, along with dispersion parameters and rms distance data that were used to quantitatively assess the performance of each method. The sensitivity of each algorithm to different combinations of range and speed of motion, scattering of marker clusters, sampling rate, and additive noise on markers’ trajectories, was finally evaluated.