Heterogeneous Domains Research Articles

Multiscale solver for multi-component reaction–diffusion systems in heterogeneous media

Coupled nonlinear system of reaction–diffusion equations describing multi-component (species) interactions with heterogeneous coefficients is considered. Finite volume method based approximation for the space is used to construct semi-discrete form for the computation of numerical solutions. Two techniques for time approximations, namely, a fully implicit (FI) and a semi-implicit (SI) schemes are examined. The fully implicit scheme is constructed using Newton’s method and leads to the coupled system of equations on each nonlinear and time iterations which is computationally rather expensive. In order to minimize the latter hurdle, an efficient and fast multiscale solver is proposed for reaction–diffusion systems in heterogeneous media. To construct fast solver, we apply a semi-implicit scheme that leads to an uncoupled system for each individual component. Problems in heterogeneous domains require a very fine grid for accurate solutions of large systems of equations at each time step iteration. Here we present a multiscale model reduction technique to reduce the size of the discrete system. Multiscale solver is based on the uncoupled operator of the problem and constructed by the use of Generalized Multiscale Finite Element Method (GMsFEM). In GMsFEM we use a diffusion part of the operator and construct multiscale basis functions by solving spectral problems in each local domain associated with the coarse grid nodes. We collect multiscale basis functions to construct a projection/prolongation matrix and generate reduced order model on the coarse grid for fast solution. Moreover, the prolongation operator is used to reconstruct a fine-scale solution and accurate approximation of the reaction part of the problem which then leads to a very accurate and computationally effective multiscale solver. We provide numerical results for two species competition test problems in two-dimensional domain with heterogeneous inclusions. Our computed solutions predict strong competition between the two species and demonstrate that diffusion can dictate the dominance of one species over the other in heterogeneous environments. We investigate the influence of number of the multiscale basis functions to the method accuracy and ability to work with different values of the diffusion coefficients.

Joint Semantic Transfer Network for IoT Intrusion Detection

In this article, we propose a joint semantic transfer network (JSTN) toward effective intrusion detection (ID) for large-scale scarcely labeled Internet of Things (IoT) domain. As a multisource heterogeneous domain adaptation (MS-HDA) method, the JSTN integrates a knowledge-rich network intrusion (NI) domain and another small-scale IoT intrusion (II) domain as source domains and preserves intrinsic semantic properties to assist target II domain ID. The JSTN jointly transfers the following three semantics to learn a domain-invariant and discriminative feature representation. The scenario semantic endows source NI and II domains with characteristics from each other to ease the knowledge transfer process via a confused domain discriminator and categorical distribution knowledge preservation. It also reduces the source–target discrepancy to make the shared feature space domain invariant. Meanwhile, the weighted implicit semantic transfer boosts discriminability via a fine-grained knowledge preservation, which transfers the source categorical distribution to the target domain. The source–target divergence guides the importance weighting during knowledge preservation to reflect the degree of knowledge learning. Additionally, the hierarchical explicit semantic alignment performs centroid-level and representative-level alignment with the help of a geometric similarity-aware pseudo-label refiner, which exploits the value of the unlabeled target II domain and explicitly aligns feature representations from a global and local perspective in a concentrated manner. Comprehensive experiments on various tasks verify the superiority of the JSTN against state-of-the-art comparing methods, on average a 10.3% of accuracy boost is achieved. The statistical soundness of each constituting component and the computational efficiency is also verified.

Effects of grain size and small-scale bedform architecture on CO2 saturation from buoyancy-driven flow

Small-scale (mm-dm scale) heterogeneity has been shown to significantly impact CO2 migration and trapping. To investigate how and why different aspects of small-scale heterogeneity affect the amount of capillary trapping during buoyancy-driven upward migration of CO2, we conducted modified invasion percolation simulations on heterogeneous domains. Realistic simulation domains are constructed by varying two important aspects of small-scale geologic heterogeneity: sedimentary bedform architecture and grain size contrast between the matrix and the laminae facies. Buoyancy-driven flow simulation runs cover 59 bedform architecture and 40 grain size contrast cases. Simulation results show that the domain effective CO2 saturation is strongly affected by both grain size and bedform architecture. At high grain size contrasts, bedforms with continuous ripple lamination at the cm scale tend to retain higher CO2 saturation than bedforms with discontinuous or cross lamination. In addition, the “extremely well sorted” grain sorting cases tend to have lower CO2 saturation than expected for cross-laminated domains. Finally, both a denser CO2 phase and greater interfacial tension increase CO2 saturation. Again, variation in fluid properties seems to have a greater effect on CO2 saturation for cross-laminated domains. This result suggests that differences in bedform architecture can impact how CO2 saturation values respond to other variables such as grain sorting and fluid properties.

Evaluation and Validation of Microscale Atmospheric Modeling With Offline Weather Research and Forecasting Model to Parallelized Large-Eddy Simulation Model Forcing Conditions

Abstract As the rate of urbanization increases, local vegetation is being replaced with man-made materials, causing increasingly adverse impacts on the surface-atmosphere energy balance. These negative effects can be simulated by modeling the urban landscapes in question; however, the main challenges of modeling urban thermal environments are the scale and resolution at which to perform such tasks. Current modeling of urban thermal environments is typically limited to either mesoscale (1 –2000 km) or microscale (<1 km) phenomena. In the present work, an open-source framework for one-way upstream coupled multiscale urban thermal environment simulations is examined and validated. This coupled simulation can provide valuable insights into the flow behavior and energy transport between mesoscale and microscale interactions. The mesoscale to microscale boundary conditions are coupled together using simulated data from the advanced research weather research and forecasting model (WRF-ARW), a mesoscale numerical weather prediction software, and assimilating it into parallelized large-eddy simulation model (PALM), a computational fluid dynamics style (CFD-style) software designed for microscale atmospheric and oceanic flows. The multiscale urban thermal environment simulations are tested for grid sensitivity to variations in model input and control parameters, and then experimentally validated against distributed sensor measurements at the Georgia Institute of Technology (Georgia Tech) campus in Atlanta, GA. Validated microscale atmospheric models with heterogeneous domains can be used to project the thermal benefits of urban heat mitigation strategies (increase use of high-albedo surfaces, tree and vegetation cover, and smart growth practices) and advise building energy usage modeling and policies.

Heterogeneous Domain Adaptation With Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity.

Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging when the source and target domains are in heterogeneous feature spaces, known as heterogeneous domain adaptation (HDA). While most HDA methods utilize mathematical optimization to map source and target data to a common space, they suffer from low transferability. Neural representations have proven to be more transferable; however, they are mainly designed for homogeneous environments. Drawing on the theory of domain adaptation, we propose a novel framework, Heterogeneous Adversarial Neural Domain Adaptation (HANDA), to effectively maximize the transferability in heterogeneous environments. HANDA conducts feature and distribution alignment in a unified neural network architecture and achieves domain invariance through adversarial kernel learning. Three experiments were conducted to evaluate the performance against the state-of-the-art HDA methods on major image and text e-commerce benchmarks. HANDA shows statistically significant improvement in predictive performance. The practical utility of HANDA was shown in real-world dark web online markets. HANDA is an important step towards successful domain adaptation in e-commerce applications.

Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning

Landslide mapping via pixel-wise classification of remote sensing imagery is essential for hazard prevention and risk assessment. Deep-learning-based change detection greatly aids landslide mapping by identifying the down-slope movement of soil, rock and other materials from bitemporal images, benefiting from the feature representation capabilities of convolutional neural networks. However, these networks rely on large amounts of pixel-level annotated data to achieve their promising performance and they normally exhibit weak generalization capability on heterogeneous image data from unseen domains. To address these issues, we propose a prototype-guided domain-aware progressive representation learning (PG-DPRL) method for cross-domain landslide mapping from large-scale remote sensing images based on the multitarget domain adaptation (MTDA) technique. PG-DPRL attempts to learn a shared landslide mapping network that performs well in multiple target domains with no additional effort for sample annotation. Specifically, PG-DPRL adopts a near-to-far adaptation strategy to gradually align the representation distributions of all target domains with the source domain, considering discrepancies between them. On this basis, cross-domain prototype learning is exploited to generate reliable domain-specific pseudo-labels and aggregate representations across domains to learn a shared decision boundary. In each DPRL step, the prototype-guided adversarial learning (PGAL) algorithm is performed to achieve category-wise representation alignment and improve the discriminative capability of representations by introducing the Wasserstein distance metric and cross-domain prototype consistency (CPC) loss. Experiments on a global very-high-resolution landslide mapping (GVLM) dataset consisting of 17 heterogeneous domains from different landslide sites demonstrate the effectiveness and robustness of PG-DPRL. It considerably improves the transferability of landslide mapping networks and outperforms several state-of-the-art approaches in terms of total and average accuracy metrics among all target domains.

A dispersed polycrystalline phase boundary constructed in CaZrO3 modified KNN based ceramics with both excellent piezoelectric properties and thermal stability

In this paper, the ceramics with composition of (0.98-x)(K0.5Na0.5)(Nb0.96Sb0.04)O3-0.02(Bi0.5Na0.5)(Zr0.8Ti0.2)O3-xCaZrO3 (abbreviated as (0.98-x)KNNS-0.02BNZT-xCZ, x = 0, 0.01, 0.015, 0.02, 0.025, 0.03) were prepared by a traditional solid-state reaction method. The effect of the additional amount of CaZrO3 on the phase structure, microstructure, dispersion index, domain structure and piezoelectric properties of ceramics was systematically studied. Finally, the piezoelectric properties and thermal stability of ceramics could be controlled by adding different amounts of CaZrO3. The addition of CaZrO3 transferred the phase structure of the ceramics from orthogonal-tetragonal (O-T) coexistence phase to rhombohedral-orthogonal (R–O) coexistence phase, which could be demonstrated by XRD test, temperature-dependent Raman spectra and εr‐T plot analysis. And when x = 0.02, the ceramics possessed the best piezoelectric and dielectric properties (d33 = 253 pC/N, εr = 1185, tanδ = 0.044). Such excellent electrical properties could be originated from the heterogeneous domain structure and small-size nano-domains of the ceramics. Moreover, with the increase of CaZrO3 doping amount, the dispersion index of ceramics gradually increased from 1.404 to 1.871, which showed more obvious dispersion phase transition characteristics and improved the thermal stability of ceramics. Particularly, when x = 0.02, after annealing at a high temperature of 220 °C (close to its Curie temperature), the d33 tested at room temperature remained above 85% of that without annealing. The results indicated that (0.98-x)KNNS-0.02BNZT-xCZ ceramic was a promising lead-free piezoelectric ceramic system.

An efficient discontinuous Galerkin - mixed finite element model for variable density flow in fractured porous media

Modeling variable density flow (VDF) in fractured porous media is computationally challenging. The challenges are mainly related to: (i) the high permeability contrast between the matrix and the fractures and, (ii) the nonlinearity induced by density variations. Due to their local mass conservation property, cell-centered methods, such as finite volumes (FV), mixed finite elements (MFE) or Discontinuous Galerkin (DG) methods are well suited for modeling mass transfer in highly heterogeneous domains. When applied for fractured media, these methods require small grid cells next to fractures because of the cross-flow equilibrium assumption (i.e. the pressure and concentration in the fracture and in the adjacent matrix grid-cells are assumed the same). To avoid this constraint, an efficient model is developed in this work using advanced cell-centered numerical methods for VDF in fractured porous media with cross-flow equilibrium assumed only across the fractures. The new model uses the hybrid MFE method for the flow discretization in the matrix and in the fracture continua. Mass transport in the matrix is modeled using a monotonic upwind MFE scheme. Advection-dominated transport in fractures is discretized with the DG method, which is well adapted for hyperbolic equations. The new model ensures continuity of pressure, concentration, fluid mass flux, advective and dispersive contaminant fluxes at matrix-fracture interfaces as well as at the intersection of several fractures. The time discretization is performed using high-order adaptive time integration techniques via the method of lines (MOL). The developed model is first validated by comparison against a 2D-2D model for a test problem dealing with linear flow and transport in a 2D domain involving a “+”-shaped barrier/fracture network. Then, the MFE-DG model is used for the simulation of the Henry saltwater intrusion problem and the results are validated by comparison against the semi-analytical solution in the case of unfractured and fractured aquifers. Finally, the simulation of a transport problem with variable viscosity in a fractured heterogeneous domain points out the superiority of the new model in terms of accuracy and efficiency when compared to a standard finite element model.

Mining for equitable health: Assessing the impact of missing data in electronic health records

Electronic health records (EHR) are collected as a routine part of healthcare delivery, and have great potential to be utilized to improve patient health outcomes. They contain multiple years of health information to be leveraged for risk prediction, disease detection, and treatment evaluation. However, they do not have a consistent, standardized format across institutions, particularly in the United States, and can present significant analytical challenges– they contain multi-scale data from heterogeneous domains and include both structured and unstructured data. Data for individual patients are collected at irregular time intervals and with varying frequencies. In addition to the analytical challenges, EHR can reflect inequity– patients belonging to different groups will have differing amounts of data in their health records. Many of these issues can contribute to biased data collection. The consequence is that the data for under-served groups may be less informative partly due to more fragmented care, which can be viewed as a type of missing data problem. For EHR data in this complex form, there is currently no framework for introducing realistic missing values. There has also been little to no work in assessing the impact of missing data in EHR. In this work, we first introduce a terminology to define three levels of EHR data and then propose a novel framework for simulating realistic missing data scenarios in EHR to adequately assess their impact on predictive modeling. We incorporate the use of a medical knowledge graph to capture dependencies between medical events to create a more realistic missing data framework. In an intensive care unit setting, we found that missing data have greater negative impact on the performance of disease prediction models in groups that tend to have less access to healthcare, or seek less healthcare. We also found that the impact of missing data on disease prediction models is stronger when using the knowledge graph framework to introduce realistic missing values as opposed to random event removal.

Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms.

Semi-supervised heterogeneous domain adaptation (SsHeDA) aims to train a classifier for the target domain, in which only unlabeled and a small number of labeled data are available. This is done by leveraging knowledge acquired from a heterogeneous source domain. From algorithmic perspectives, several methods have been proposed to solve the SsHeDA problem; yet there is still no theoretical foundation to explain the nature of the SsHeDA problem or to guide new and better solutions. Motivated by compatibility condition in semi-supervised probably approximately correct (PAC) theory, we explain the SsHeDA problem by proving its generalization error - that is, why labeled heterogeneous source data and unlabeled target data help to reduce the target risk. Guided by our theory, we devise two algorithms as proof of concept. One, kernel heterogeneous domain alignment (KHDA), is a kernel-based algorithm; the other, joint mean embedding alignment (JMEA), is a neural network-based algorithm. When a dataset is small, KHDA's training time is less than JMEA's. When a dataset is large, JMEA is more accurate in the target domain. Comprehensive experiments with image/text classification tasks show KHDA to be the most accurate among all non-neural network baselines, and JMEA to be the most accurate among all baselines.

Linear Stochastic Analysis of the Partial Reversibility of Ensemble and Effective Dispersion in Heterogeneous Porous Media

AbstractMacrodispersion in heterogeneous formations is caused by spatial variability of the velocity field, making different parts of a plume experience different advective displacements. Differential advection interacts with diffusion, which hardly affects longitudinal ensemble dispersion (i.e., the spread of the ensemble‐averaged concentration) but determines effective dispersion, that is, the expected spread of individual plumes for point injections. The latter has been suggested as metric of solute mixing. Pure advection is fully reversible, whereas diffusion is completely irreversible. We quantify the partial reversibility of macrodispersion by analyzing the second central ensemble and effective spatial moments for advective‐diffusive transport in heterogeneous domains with flow reversal, applying linear stochastic theory to approximate the corresponding moments and comparing them to particle‐tracking random‐walk simulations in periodic media. Diffusion causes solute particles to deviate from their forward trajectories when flow is reversed. As long as advective memory dominates, both types of second central moments decrease during backward motion, then reach a minimum, and increase again. The reversibility is considerably bigger for ensemble than effective dispersion but the latter also shows partial reversibility, challenging its use as metric of mixing. The stronger diffusion is in comparison to advection, the less reversible dispersion becomes. After equally long times of forward and backward motion, the two types of second central moments differ, but to a much smaller extent than in pure forward motion. In realistic settings, the advective memory affects dispersion so strongly that the asymptotic regime is not reached before the plume center has returned to its origin.

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods approximate only one material parameter while assuming homogeneous distributions for the others. We employ physics-informed neural networks (PINN) in linear elasticity problems to discover the space-dependent distribution of both elastic modulus (E) and Poisson's ratio (ν) simultaneously, using strain data, normal stress boundary conditions, and the governing physics. We validated our model on three examples. First, we experimentally loaded hydrogel samples with embedded stiff inclusions, representing tumorous tissue, and compared the approximations against ground truth determined through tensile tests. Next, using data from finite element simulation of a rectangular domain containing a stiff circular inclusion, the PINN model accurately localized the inclusion and estimated both E and ν. We observed that in a heterogeneous domain, assuming a homogeneous ν distribution increases estimation error for stiffness as well as the area of the stiff inclusion, which could have clinical importance when determining size and stiffness of tumorous tissue. Finally, our model accurately captured spatial distribution of mechanical properties and the tissue interfaces on data from another computational model, simulating uniaxial loading of a rectangular hydrogel sample containing a human brain slice with distinct gray matter and white matter regions and complex geometrical features. This elasticity imaging implementation has the potential to be used in clinical imaging scenarios to reliably discover the spatial distribution of mechanical parameters and identify material interfaces such as tumors. STATEMENT OF SIGNIFICANCE: Our work is the first implementation of physics-informed neural networks to reconstruct both material parameters - Young's modulus and Poisson's ratio - and stress distributions for isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using experimental measurements and synthetic data generated using finite element modeling. Our method can be implemented in clinical elasticity imaging scenarios to improve diagnosis of tumors and for mechanical characterization of biomaterials and biological tissues in a minimally invasive manner.

Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation.

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain. Although UDA is typically jointly trained on data from both domains, accessing the labeled source domain data is often restricted, due to concerns over patient data privacy or intellectual property. To sidestep this, we propose "off-the-shelf (OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor trained in a source domain to a target domain, in the absence of source domain data in adaptation. Toward this goal, we aim to develop a novel batch-wise normalization (BN) statistics adaptation framework. In particular, we gradually adapt the domain-specific low-order BN statistics, e.g., mean and variance, through an exponential momentum decay strategy, while explicitly enforcing the consistency of the domain shareable high-order BN statistics, e.g., scaling and shifting factors, via our optimization objective. We also adaptively quantify the channel-wise transferability to gauge the importance of each channel, via both low-order statistics divergence and a scaling factor.Furthermore, we incorporate unsupervised self-entropy minimization into our framework to boost performance alongside a novel queued, memory-consistent self-training strategy to utilize the reliable pseudo label for stable and efficient unsupervised adaptation. We evaluated our OSUDA-based framework on both cross-modality and cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks. Our experimental results showed that our memory consistent OSUDA performs better than existing source-relaxed UDA methods and yields similar performance to UDA methods with source data.

«Ծայրամասային դոկտրինն» ու Մերձավոր Արևելքի «Հատվածայնացման տեսությունը» Իսրայելի ռազմավարական ընկալումների համատեքստում / The “Periphery Doctrine” and “Theory of Fragmentation” of the Middle East within the context of Israel’s strategic perceptions

October 7, 2023, saw the Israeli-Palestinian conflict step up on a new stage of combat operations. Consequently, with local, regional and global actors elucidating or clarifying their position on current military actions that hold the potential to escalate into a regional and large-scale war, importance is attached to the consideration of doctrines, concepts and theories that have become ideologies in the aspect of ensuring Israel’s own security, and its perceptions of regional security as a whole. In this sense, the article discusses the “Periphery Doctrine” (“Strategy of periphery countries”), and the essence of the “Theory of Fragmenta­tion”, stages of their transformation and Israel’s security perceptions stipulating those transformations. The aforementioned doctrine and the theory are considered within the context of Israel’s strategic security principles and its perceptions of the Near and Middle East as somewhat a heterogeneous cultural domain. Тhe axis drawn due to the Israeli-developed “New Doctrine of Periphery”, sometimes called the “Reverse periphery doctrine”, is also comprehensively analyzed, representing Israel’s desire to impose a blockade on the real periphery countries – Iran and Turkey – or to contain them. The article attaches particular interest to the consideration and comparative analysis of the differences between the classical Periphery Doctrine and the “New” one (not only in the sense of the number and motives of actors, the goals facing the new alliance or cooperation, methods of achieving them, but also within the framework of the fact of attaching special importance to their economic component along with common threats uniting all parties). The analysis allows us to sum up that Israel’s first, or “classical” doctrine “on concluding an alliance with periphery countries”, and the theory of fragmentation as a whole, were successful. Besides, owing to this strategy, the thesis on reconciliation with Israel and establishment of diplomatic relations with it was put on the agenda of the Arab States. In its turn, Israel’s “New Periphery Doctrine” is still at the formation stage. However, to some extent it comes from Israeli perceptions of not only military threats, but also economic and energy security.

Redoxtrons – An experimental system to study redox processes within the capillary fringe

AbstractSpatiotemporal characterisation of the soil redox status within the capillary fringe (CF) is a challenging task. Air‐filled porosities (ε), oxygen concentration (O2) and soil redox potential (EH) are interrelated soil variables within active biogeochemical domains such as the CF. We investigated the impact of water table (WT) rise and drainage in an undisturbed topsoil and subsoil sample taken from a Calcaric Gleysol for a period of 46 days. We merged 1D (EH and matric potential) and 2D (O2) systems to monitor at high spatiotemporal resolution redox dynamics within self‐constructed redoxtron housings and complemented the data set by a 3D pore network characterization using X‐ray microtomography (X‐ray μCT). Depletion of O2 was faster in the organic matter‐ and clay‐rich aggregated topsoil and the CF extended >10 cm above the artificial WT. The homogeneous and less‐aggregated subsoil extended only 4 cm above the WT as indicated by ε–O2–EH data during saturation. After drainage, 2D O2 imaging revealed a fast aeration towards the lower depths of the topsoil, which agrees with the connected ε derived by X‐ray μCT (εCT_conn) of 14.9% of the total porosity. However, small‐scaled anoxic domains with O2 saturation <5% were apparent even after lowering the WT (down to 0.25 cm2 in size) for 23 days. These domains remained a nucleus for reducing soil conditions (EH < −100 mV), which made it challenging to characterise the soil redox status in the CF. In contrast, the subsoil aeration reached O2 saturation after 8 days for the complete soil volume. Values of εCT_conn around zero in the subsoil highlighted that soil aeration was independent of this parameter suggesting that other variables such as microbial activity must be considered when predicting the soil redox status from ε alone. The use of redoxtrons in combination with localised redox‐measurements and image based pore space analysis resulted in a better 2D/3D characterisation of the pore system and related O2 transport properties. This allowed us to analyse the distribution and activity of microbiological niches highly associated with the spatiotemporal variable redox dynamics in soil environments.Highlights The time needed to turn from reducing to oxidising (period where all platinum electrodes feature EH > 300 mV) condition differ for two samples with contrasting soil structure. The subsoil with presumably low O2 consumption rates aerated considerably faster than the topsoil and exclusively by O2 diffusion through medium‐ and fine‐sized pores. To derive the soil redox status based upon the triplet ε–O2–EH is challenging at present in heterogeneous soil domains and larger soil volumes than 250 cm3. Undisturbed soil sampling along with 2D/3D redox measurement systems (e.g., redoxtrons) improve our understanding of redox dynamics within the capillary fringe.

Extraction of Validating Shapes from Very Large Knowledge Graphs

Knowledge Graphs (KGs) represent heterogeneous domain knowledge on the Web and within organizations. There exist shapes constraint languages to define validating shapes to ensure the quality of the data in KGs. Existing techniques to extract validating shapes often fail to extract complete shapes, are not scalable, and are prone to produce spurious shapes. To address these shortcomings, we propose the Quality Shapes Extraction (QSE) approach to extract validating shapes in very large graphs, for which we devise both an exact and an approximate solution. QSE provides information about the reliability of shape constraints by computing their confidence and support within a KG and in doing so allows to identify shapes that are most informative and less likely to be affected by incomplete or incorrect data. To the best of our knowledge, QSE is the first approach to extract a complete set of validating shapes from WikiData. Moreover, QSE provides a 12x reduction in extraction time compared to existing approaches, while managing to filter out up to 93% of the invalid and spurious shapes, resulting in a reduction of up to 2 orders of magnitude in the number of constraints presented to the user, e.g., from 11,916 to 809 on DBpedia.

BiG-FSLF: A Cross Heterogeneous Domain Few-Shot Learning Framework Based on Bidirectional Generation for Hyperspectral Image Change Detection

BiG-FSLF: A Cross Heterogeneous Domain Few-Shot Learning Framework Based on Bidirectional Generation for Hyperspectral Image Change Detection

Heterogeneous domain adaptation for intracortical signal classification using domain consensus

Most domain adaptation studies have made efforts to solve the problem of homogeneous data shifts, which share the same feature and label space in both domains. However, for the heterogeneous scenarios, it still needs improvements in practical applications for intracortical brain-computer interface (iBCI) systems. To address this issue, we propose a domain adaptation framework for decoding reaching and grasping movements of iBCI systems in two heterogeneous scenarios, i.e., partial domain adaptation (PDA) and open set domain adaptation (OSDA). The proposed framework combined the neural activity vector (NAV) features and the domain consensus clustering (DCC) method for iBCI classification. For heterogeneous scenarios, the framework can map common classes according to the clusters with the highest domain consensus score at the semantic-level and distinguish private classes at the sample-level. We evaluated the framework on the data, which were collected from three array electrodes implanted in three regions of cerebral cortexes from one monkey across two sessions, each consisting of four continuous days. The proposed framework outperformed than four PDA and two OSDA methods, which effectively reduced the disparity of feature and label space in two heterogeneous scenarios. Consequently, our results demonstrated that the proposed framework is a new solution for decoder calibration in two heterogeneous scenarios, and it facilitates the generality of the iBCI system for clinical applications.

A hybrid pressure approximation in the control volume finite element method for multiphase flow and transport in heterogeneous porous media

We present a new hybrid pressure formulation applied to the control volume finite element (CVFE) method to model multiphase flow and transport in highly heterogeneous porous media. The formulation effectively captures sharp saturation changes in the presence of discontinuous material properties by employing a discontinuous pressure approximation at material interfaces. The heterogeneous porous medium is divided into sub-domains within which material properties are uniform or smoothly varying. By construction, the resultant control volume dual mesh is restricted within a sub-domain. The artificial mass leakage across material property boundaries observed in classical CVFE methods is therefore circumvented. The approach applies the robust continuous pressure approximation in the rest of the computational domain; the discontinuous approximation is applied only at the sub-domain boundaries. The discontinuous parameters necessary to achieve mass conservative solutions, locally and globally, are described. We demonstrate the accuracy and efficiency of the new approach by comparison with the classical continuous CVFE method on various examples of heterogeneous domains as well as establishing the convergence of the numerical method. The proposed hybrid formulation significantly outperforms the accuracy and efficiency of classical CVFE methods that use the same order of approximation for modeling multiphase flow in heterogeneous porous media.

An implicit FFT-based method for wave propagation in elastic heterogeneous media

An FFT-based algorithm is developed to simulate the propagation of elastic waves in heterogeneous d-dimensional rectangular shape domains. The method allows one to prescribe the displacement as a function of time in a subregion of the domain, emulating the application of Dirichlet boundary conditions on an outer face. Time discretization is performed using an unconditionally stable beta-Newmark approach. The implicit problem for obtaining the displacement at each time step is solved by transforming the equilibrium equations into Fourier space and solving the corresponding linear system with a preconditioned Krylov solver. The resulting method is validated against analytical solutions and compared with implicit and explicit finite element simulations and with an explicit FFT approach. The accuracy of the method is similar to or better than that of finite elements, and the numerical performance is clearly superior, allowing the use of much larger models. To illustrate the capabilities of the method, some numerical examples are presented, including the propagation of planar, circular, and spherical waves and the simulation of the propagation of a pulse in a polycrystalline medium.