Direct Inversion Research Articles

SAMControl: Controlling Pose and Object for Image Editing with Soft Attention Mask

To achieve content-consistent results in text-conditioned image editing, existing methods typically employ a reconstruction branch to capture the source image details via diffusion inversion and a generation branch to synthesize the target image based on the given textual prompt and the masked source image details. However, accurately segmenting source details is challenging with the current fixed-threshold mask strategy. Additionally, the inadequacies in the inversion process can lead to insufficient retention of source details. In this paper, we propose a method called SAMControl ( S oft A ttention M ask) to adaptively control the pose and object details for image editing. SAMControl dynamically learns flexible attention masks for different images at various diffusion steps. Furthermore, in the reconstruction branch, we utilize a direct inversion technique to ensure the fidelity of source details within SAM. Extensive qualitative and quantitative results demonstrate the effectiveness of the proposed method.

Novel fast full-wavefield modeling of air-coupled surface waves and its implications for non-contact pavement testing

We present a novel approach for deriving and modeling air-coupled surface waves with applications in non-contact non-destructive testing (NDT). It is based on the fast Fourier-Bessel series system in conjunction with the unconditionally stable dual-variable and position matrix method. Parametric studies, including sensitivity analysis, are conducted to assess the feasibility of using non-contact air-coupled measurements for pavement testing, focusing on Green's functions, time-domain waveforms, and experimental frequency-velocity spectra (FVS, i.e., the estimated Green's functions from acquired truncated wavefield). The predicted experimental FVS presented in this study are synthetic dispersion images, which are distinguished from the measured experimental FVS (i.e., measured dispersion images from multichannel analysis of surface wave (MASW) test). With the derived complete solution of air-coupled dynamic responses, we find that: (1) Striking similarities between the theoretical Green's functions of vertical displacement (on the pavement surface) and pressure (in the air), as well as in their corresponding experimental FVS. (2) The proposed accurate and efficient full-wave modeling of air-coupled surface waves avoids the need for good contact between geophones/accelerometers and pavement surface. This facilitates direct inversion of shear wave velocity profiles by fitting the predicted experimental FVS to the measured one. (3) Sensitivity analysis demonstrates no significant loss of information in the pressure measured in the air, supporting the feasibility of using non-contact measurement for non-destructive testing. These results suggest that non-contact air-coupled measurements hold great promise as a viable alternative to contact measurements in non-destructive testing.

Lifespan trajectories of negative and positive affect: A coordinated analysis of 14 longitudinal studies

Lifespan developmental theories suggest age-related shifts in motivation, cognition, emotion regulation, and stressor experience lead to changes in mean levels of negative and positive affect across the lifespan. The present research used coordinated data analysis to examine mean-level affective trajectories in 186,752 participants ranging from 11–104 years old across 14 longitudinal studies. Random-effects models were used to estimate meta-analytic effect sizes. On average, negative affect decreased until early older adulthood, and then remained stable throughout older adulthood. Meanwhile, positive affect remained stable across most of the younger and middle-aged adult lifespan, before starting its descent in later middle-aged adulthood and continuing to decline throughout older adulthood. Studies with older samples showed a clearer flattening effect of negative affect and steeper decline of positive affect in late-life relative to younger samples. These findings suggest that lifespan developmental affect trajectories are nuanced and not a direct inverse of each other.

A nonlinear inversion method for Young’s modulus and shear modulus based on the exact Zoeppritz equations

IntroductionYoung’s modulus and shear modulus are essential mechanical parameters for evaluating subsurface rocks, playing a pivotal role in the exploration and development of unconventional resources. Young’s modulus indicates the brittleness of the reservoir, while shear modulus determines the ease of fracturing rock layers.MethodsTraditional methods estimate these moduli through indirect calculations and approximate expressions, which are prone to cumulative errors and rely on multiple assumptions, reducing inversion accuracy. This paper presents a direct inversion method for acquiring of Young’s modulus and shear modulus using the exact Zoeppritz equations, integrated within a Bayesian framework for pre-stack inversion. The quantum particle swarm optimization (QPSO) algorithm is introduced to achieve a nonlinear solution to the objective function.ResultsTests on synthetic and actual field data demonstrate the feasibility and effectiveness of the proposed method, yielding more accurate inversion results compared to traditional methods.DiscussionThese findings provide valuable insights for predicting reservoir brittleness and characterizing reservoirs in unconventional shale gas exploration and development.

Efficient and Robust Ab Initio Self-Consistent Field Acceleration Algorithm Based on a Semiempirical Model Hamiltonian.

A novel doubly iterative self-consistent field (SCF) approach using a semiempirical model Hamiltonian (denoted as the SMH algorithm) is proposed to accelerate the Hartree-Fock (HF) and density functional theory (DFT) calculations. This method first constructs the Fock matrix exactly in each SCF macroiteration, followed by a few SCF microiterations, where the Fock matrix is incrementally updated using an inexpensive semiempirical approximation. This leads to an improved wave function in each SCF macroiteration with minimal additional cost, and therefore a reduced number of exact Fock builds is required for SCF convergence. The SMH algorithm can be combined with conventional SCF convergence techniques such as level shifting, direct inversion in the iterative subspace (DIIS), and energy-DIIS (EDIIS). When integrated with DIIS, SMH enhances the convergence of simple organic molecules by approximately 10% compared to plain DIIS, with speedups of up to 60% for the more challenging transition metal systems compared to EDIIS + DIIS. Our results show that SMH is a reliable SCF accelerator that seldom deteriorates convergence and is highly robust.

An Efficient Algorithm for Constrained CASSCF(1,2) and CASSCF(3,2) Simulations as Relevant to Electron and Hole Transfer Problems.

We propose an efficient algorithm for the recently published electron/hole-transfer Dynamical-weighted State-averaged Constrained CASSCF (eDSC/hDSC) method studying charge transfer states and D1-D0 crossings for systems with odd numbers of electrons. By separating the constrained minimization problem into an unconstrained self-consistent-field (SCF) problem and a constrained nonself-consistent-field (nSCF) problem, as well as accelerating the direct inversion in the iterative subspace (DIIS) technique to solve the SCF problem, the overall computational cost is reduced by a factor of 8-20 compared with directly using sequential quadratic programming (SQP). This approach should be applicable for other constrained minimization problems, and in the immediate future, once gradients are available, the present eDSC/hDSC algorithm should allow for speedy nonadiabatic dynamics simulations.

Strain Partitioning in the Southeastern Tibetan Plateau From Kinematic Modeling of High‐Resolution Sentinel‐1 InSAR and GNSS

AbstractFault slip rates estimated from geodetic data are being integrated into seismic hazard models. The standard approach requires modeling velocities and relative (micro‐)plate motions, which is challenging for fault‐based models. We present a new approach to directly invert strain rates to solve for slip rates and distributed strain simultaneously. We generate velocity and strain rate fields over the southeastern Tibetan Plateau, utilizing Sentinel‐1 Interferometric Synthetic Aperture Radar data spanning 2014–2023. We derive slip rates using block modeling and by inverting strain rates. Our results show a partitioning between localized strain on faults and distributed deformation. The direct inversion of strain rates matches the geodetic data best when incorporating distributed moment sources, accounting for a similar proportion to on‐fault sources. The direct strain methodology also aligns best with the independent geological slip rates, especially near fault tips. As high‐resolution strain rate fields become increasingly available, we recommend direct inversion as the preferred practice.

Intelligent seismic AVO inversion method for brittleness index of shale oil reservoirs

The brittleness index (BI) is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development. Seismic amplitude variation with offset (AVO) inversion is commonly used to obtain the BI. Traditionally, velocity, density, and other parameters are firstly inverted, and the BI is then calculated, which often leads to accumulated errors. Moreover, due to the limited of well-log data in field work areas, AVO inversion typically faces the challenge of limited information, resulting in not high accuracy of BI derived by existing AVO inversion methods. To address these issues, we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients. Based on this, an intelligent AVO inversion method, which combines the advantages of traditional and intelligent approaches, for directly obtaining the BI is proposed. A TransU-net model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI. By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples, the challenge of limited samples is effectively addressed, and the direct inversion of the BI is stably achieved. Tests on model data and applications on field data demonstrate the feasibility, advancement, and practicality of the proposed method.

Broadband and efficient terahertz helicity inversion by a reconfigurable reflective metasurface based on vanadium dioxide

We experimentally demonstrate the broadband and highly efficient inversion of the rotation direction of the terahertz circular polarization achieved by a dynamic metasurface with vanadium dioxide (VO2). This metasurface converts linear polarization into circular polarization. The rotational direction of the output circular polarization can be reversed based on the VO2 phase transition. The VO2 state can be dynamically switched by injecting direct current into an on-chip circuit. To improve the terahertz-wave utilization efficiency, the metasurface is operated at an incident angle of 45°, thereby eliminating the beam splitter typically used to separate the outgoing wave from the incident wave at normal incidence. To achieve broadband operation, we employ a dispersion-cancelation strategy that cancels the dispersive phase responses between orthogonal linear polarizations. This strategy was previously developed for static metasurfaces; we have applied it to the design of dynamic metasurfaces. At a center frequency of 0.99 THz, the experimental evaluation demonstrated helicity switching with conversion efficiencies of 92% and 87% for the insulating and metallic states of VO2, respectively. Dispersion cancelation is simultaneously achieved for the insulating and metallic states of VO2, resulting in a relative bandwidth of 0.26, which is 3.2 times broader than that of a previously developed efficient dynamic metasurface.

On reversal of wave-generated longshore currents at tidal frequencies on dissipative beaches contiguous to a mesotidal estuary, S.E coast of Nigeria

Reversals in wave-generated longshore currents in surf zones occur at different temporal and spatial scales. Along coastlines with tidal channel openings with well-developed mouth bars or ebb-tidal deltas, the reversal mechanism is often attributed to the mouth bar – induced refraction of the shoaling waves. This reversal mechanism is characterized by convergence of longshore currents from the adjoining surf zones at the mouth of the tidal channel. Simultaneous half-hourly monitoring of wave-generated longshore currents over 50 successive (daylight) semi-diurnal tidal cycles in beach surf zones adjoining the Qua Iboe River estuary, S.E coast of Nigeria showed the above reversal pattern during flooding stage only. The converse pattern, where the surf zone longshore currents diverged away from the mouth of the estuary, was observed during ebbing stage. Both surf zones showed flow direction inversion with respect to each other, with velocity vector correlation coefficient r > − 0.8 in over 80 % of the data set. Instances of comparable flow direction (<10 %) were also recorded. Tidal processes are implicated in the documented results. Direction-averaged longshore current velocities, typically in the 15–60 cm/s range, attained highest values in both surf zones at about spring tide phase. Also, tidal cycle-residual longshore current maximum and minimum velocities occurred close to spring and neap tide, respectively. Only 30 % of the residual velocities were eastward directed in the up-drift surf zone as against 80 % in the down-drift counterpart. Given the prevailing southwesterly waves, the present results negate the assertion that reversal in longshore current direction in this offset shoreline setting is exclusively a consequence of wave refraction by mouth bar morphology. The reversing pattern of the longshore currents over a tidal cycle is well explained by incorporating interacting effects of shoaling waves with tide-induced oscillations in water level as well as the estuarine flow.

Physics‐Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow

AbstractWe propose a physics‐informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD stands out as a direct inversion method, eliminating the need for repeated forward model simulations. The framework combines data‐driven and physics‐driven approaches by integrating monitoring data and domain knowledge into the inversion process. PICD utilizes a convolutional decoder to effectively approximate the spatial distribution of hydraulic heads, while Karhunen–Loève expansion (KLE) is employed to parameterize hydraulic conductivities. During the training process, the stochastic vector in KLE and the parameters of the convolutional decoder are adjusted simultaneously to minimize the data‐mismatch and the physical violation. The final optimized stochastic vectors correspond to the estimation of hydraulic conductivities, and the trained convolutional decoder can predict the evolution and distribution of hydraulic heads. Various scenarios of groundwater flow are examined and results demonstrate the framework's capability to accurately estimate heterogeneous hydraulic conductivities and to deliver satisfactory predictions of hydraulic heads, even with sparse measurements.

An improved DIIS method using a versatile residual matrix to accelerate SCF starting from a crude guess.

The minimization of the commutator of the Fock and density matrices as the error matrix in the direct inversion of the iterative subspace (CDIIS) developed by Pulay is a powerful self-consistent field (SCF) acceleration technique for the construction of optimum Fock matrix, if initiated with a fair initial guess. In this work, we present an alternative minimized error matrix to the commutator in the CDIIS, namely the residual or the gradient of the energy-functional for a Slater determinant subject to the orthonormality constraints among orbitals, representing the search for a newly improved Fock matrix in the direction of the residual in the direct inversion of the iterative subspace (RDIIS). Implemented in the computational chemistry package GAMESS, the RDIIS is compared with the standard CDIIS and the second order SCF orbital optimization (SOSCF) for tested molecules started with a crude guess. As a result, the RDIIS stably and efficiently performs the SCF convergence acceleration. Furthermore, the RDIIS is considerably independent on the subspace size with the concentrated linear coefficients accounting proportionally for the Fock matrices close to the current iteration.

Analysis on stable imaging and inverse algorithm for artificial source EM data

Abstract The inversion of artificial source electromagnetic (EM) method data fundamentally involves constructing a mathematical relationship between observable data and geological structures. The aim of imaging and inversion is to construct a geophysical model that matches the observable results, thereby realizing the identification of subsurface targets. The results of EM data inversion, due to the simplicity of geophysical models, limit inversion computing efficiency. Moreover, complexity of actual geological structures, and lack of onsite observable data, are often hindered by non-uniqueness. The challenge in the interpretation of artificial source EM data is in enhancing both the precision and expeditiousness of the inversion process. It can be classified into three main types for EM data inversion: direct imaging inversion, deterministic inversion, and stochastic inversion. To enhance computational efficiency and reduce non-uniqueness in the results, effective inversion methods, prior geological information, geophysical data, and comprehensive analysis can help mitigate the issue of non-uniqueness in EM data inversion, thereby leading to more rational geophysical interpretation results. With the progress of technology such as computing centers and the development of artificial intelligence methods, future inversion techniques will become faster, more efficient, and more intelligent, and will be applied to the interpretation of artificial source EM data.

Latent variable model inversion for intervals. Application to tolerance intervals in class-modelling situations, and specification limits in process control

The paper deals with the inversion of intervals when a PLS (Partial Least Squares) model is used. However, instead of discretizing the interval, it is proved that the region resulting from the inversion of a PLS model is a convex set bounded by two parallel hyperplanes, each corresponding to the direct inversion of each endpoint of the given interval.When the domain of the input variables is a convex set, any feasible solution with predictions within the interval set in the response can be obtained as a convex combination of a point on each of the two hyperplanes. In this way, the new solutions preserve the internal structure of the input variables.This methodology can be of interest in several domains where the response under study is defined in terms of an interval of admissible values, such as specifications for a product in an industrial process, or tolerance intervals for computing compliant class-models.The inversion of the corresponding fitted model defines a region in the input space (predictor variables) whose predictions fall within the specified interval. Then, estimating and exploring this region will increase the information about the problem under study.

Direct inversion of the Longitudinal ray transform for 2D residual elastic strain fields

We examine the problem of Bragg-edge elastic strain tomography from energy resolved neutron transmission imaging. A new approach is developed for two-dimensional plane-stress and plane-strain systems whereby elastic strain can be reconstructed from its Longitudinal ray transform (LRT) as two parts of a Helmholtz decomposition based on the concept of an Airy stress potential. The solenoidal component of this decomposition is reconstructed using an inversion formula based on a tensor filtered back projection (FBP) algorithm whereas the potential part can be recovered using either Hooke’s law or a finite element model of the elastic system. The technique is demonstrated for two-dimensional plane-stress systems in both simulation, and on real experimental data. We also demonstrate that application of the standard scalar FBP algorithm to the LRT in these systems recovers the trace of the solenoidal component of strain and we provide physical meaning for this quantity in the case of 2D plane-stress and plane-strain systems.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.

Innovative Approach to Chiral Polyurethanes: Asymmetric Copolymerization with Isocyanates.

The introduction of nitrogen-containing functional groups to chiral polymer backbones enables the tailoring of physical properties and offers opportunities for further post-polymerization modification. However, the substrate scope of such polymers is extremely limited because monomers having nitrogen-containing groups can change coordination state with respect to the metal centers, thus decreasing the activity and enantioselectivity and even poisoning the catalyst completely. In this paper, we report our attempts to carry out the asymmetric copolymerization of meso-epoxide with highly reactive isocyanates. In particular, we found that biphenol-linked bimetallic Co(III) complexes with multiple chiral centers are very efficient in catalyzing this asymmetric copolymerization reaction, affording optically active polyurethanes with a completely alternating nature and a high enantioselectivity of up to 94% ee. Crucially, we identified that the steric hindrance at the phenolate ortho position of the ligand strongly influences the catalytic activity and product enantioselectivity. In addition, density functional theory calculations revealed that the highly sterically bulky substituents change the mechanism from bimetallic to monometallic, and result in the unexpected inversion of the chiral induction direction. Moreover, the high stereoregularity of the produced polyurethanes enhances their thermal stability. This study offers a versatile methodology for the synthesis of chiral polymers containing nitrogen functionalities.

Innovative Approach to Chiral Polyurethanes: Asymmetric Copolymerization with Isocyanates

AbstractThe introduction of nitrogen‐containing functional groups to chiral polymer backbones enables the tailoring of physical properties and offers opportunities for further post‐polymerization modification. However, the substrate scope of such polymers is extremely limited because monomers having nitrogen‐containing groups can change coordination state with respect to the metal centers, thus decreasing the activity and enantioselectivity and even poisoning the catalyst completely. In this paper, we report our attempts to carry out the asymmetric copolymerization of meso‐epoxide with highly reactive isocyanates. In particular, we found that biphenol‐linked bimetallic Co(III) complexes with multiple chiral centers are very efficient in catalyzing this asymmetric copolymerization reaction, affording optically active polyurethanes with a completely alternating nature and a high enantioselectivity of up to 94 % ee. Crucially, we identified that the steric hindrance at the phenolate ortho position of the ligand strongly influences the catalytic activity and product enantioselectivity. In addition, density functional theory calculations revealed that the highly sterically bulky substituents change the mechanism from bimetallic to monometallic, and result in the unexpected inversion of the chiral induction direction. Moreover, the high stereoregularity of the produced polyurethanes enhances their thermal stability, and they can be selectively decomposed into oxazolidinones. This study offers a versatile methodology for the synthesis of chiral polymers containing nitrogen functionalities.

The signal compensation method for the neutron flux in the measurement system using self-powered neutron detector.

Self-powered neutron detectors (SPNDs) have been utilized within in-core instrumentation to measure neutron flux for control and core flux mapping in nuclear reactors. To estimate neutron flux with SPNDs, a mathematical dynamic model correlating neutron flux and SPND material has been established. Estimation and signal compensation for neutron flux have primarily been developed using transfer-function-based methods or state-space-based methods. Particularly for the rhodium SPND, to compensate for its delayed response to incident neutron flux input, both groups of compensation methods have been widely applied. This Review details the signal compensation methods of neutron flux using transfer-function-based methods, such as those employing analog circuits, dynamic modeling of neutron flux, compensation of neutron flux, and direct inversion. In addition, signal compensation and estimation of neutron flux using state-space-based methods, such as the Kalman filter and H-infinity filter, are reviewed, along with basic calculations based on certain assumptions. Since there are differences in the characteristics of the two groups of methods for the same type of SPND, review comments are also included regarding the stability of compensation methods, based on results obtained from calculations using certain assumptions.

MATRICS: The implicit matrix-free Eulerian hydrodynamics solver

Context. There exists a zoo of different astrophysical fluid dynamics solvers, most of which are based on an explicit formulation and hence stability-limited to small time steps dictated by the Courant number expressing the local speed of sound. With this limitation, the modeling of low-Mach-number flows requires small time steps that introduce significant numerical diffusion, and a large amount of computational resources are needed. On the other hand, implicit methods are often developed to exclusively model the fully incompressible or 1D case since they require the construction and solution of one or more large (non)linear systems per time step, for which direct matrix inversion procedures become unacceptably slow in two or more dimensions. Aims. In this work, we present a globally implicit 3D axisymmetric Eulerian solver for the compressible Navier–Stokes equations including the energy equation using conservative formulation and a fully simultaneous approach. We use the second-order-in-time backward differentiation formula for temporal discretization as well as the κ scheme for spatial discretization. We implement different limiter functions to prohibit the occurrence of spurious oscillations in the vicinity of discontinuities. Our method resembles the well-known monotone upwind scheme for conservation laws (MUSCL). We briefly present efficient solution methods for the arising sparse and nonlinear system of equations. Methods. To deal with the nonlinearity of the Navier–Stokes equations we used a Newton iteration procedure in which the required Jacobian matrix-vector product was reconstructed with a first-order finite difference approximation to machine precision in a matrix-free way. The resulting linear system was solved either completely matrix-free with a combination of a sufficient Krylov solver and an approximate Jacobian preconditioner or semi-matrix-free with the incomplete lower upper factorization technique as a preconditioner. The latter was dependent on a standalone approximation of the Jacobian matrix, which was optionally calculated and needed solely for the purpose of preconditioning. Results. We show our method to be capable of damping sound waves and resolving shocks even at Courant numbers larger than one. Furthermore, we prove the method’s ability to solve boundary value problems like the cylindrical Taylor-Couette flow (TC), including viscosity, and to model transition flows. To show the latter, we recover predicted growth rates for the vertical shear instability, while choosing a time step orders of magnitude larger than the explicit one. Finally, we verify that our method is second order in space by simulating a simplistic, stationary solar wind.