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The last few decades have led to the rise of research focused on propulsion and control systems for bio-inspired unmanned underwater vehicles (UUVs), which provide more maneuverable alternatives to traditional UUVs in underwater missions. Recent work has explored the use of time-series neural network surrogate models to predict thrust and power from vehicle design and fin kinematics. We expand upon this work, creating new forward neural network models that encapsulate the effects of the material stiffness of the fin on its kinematic performance, thrust, and power, and are able to interpolate to the full spectrum of kinematic gaits for each material. Notably, we demonstrate through testing of holdout data that our developed forward models capture the thrust and power associated with each set of parameters with high resolution, enabling highly accurate predictions of previously unseen gaits and thrust and FOM gains through proper materials and kinematics selection. As propulsive efficiency is of utmost importance for flapping-fin UUVs in order to extend their range and endurance for essential operations, a non-dimensional figure of merit (FOM), derived from measures of propulsive efficiency, is used to evaluate different fin designs and kinematics and allow for comparison with other bio-inspired platforms. We use the developed FOM to analyze optimal gaits and compare the performance between different fin materials. The forward model demonstrates the ability to capture the highest thrust and FOM with good precision, which enables us to improve thrust generation by 83.89% and efficiency by 137.58% with proper fin stiffness and kinematics selection, allowing us to improve material selection for bio-inspired fin design. [ABSTRACT FROM AUTHOR]

Landslides can cause significant infrastructure damage and thousands of deaths around the world. Colorado Springs has experienced numerous damaging landslides that have not been previously geophysically investigated due to its geological and climatic conditions as well as increased urban development into landslideprone areas. In 2015, heavy rainfall caused the Skyway and Broadmoor Bluffs landslides that destroyed 30 homes. In this study, these two landslides were studied using the electrical resistivity method to evaluate the nature of the landslides. Two-dimensional inverse models constrained by geological and borehole data indicate that both landslides occurred within the weathered portion of the Cretaceous Pierre Shale. The depth to the landslide failure surface ranged between 3 to 6 m and 6 to 8 m for the Skyway and the Broadmoor landslides, respectively. Both landslides have characteristics of a rotational-type landslide, but three models (two for Skyway, one for Broadmoor) do not have smooth failure surfaces. The Pierre Shale is characterized by significantly lower resistivity than the overlying clay. This suggests the landslide failure was promoted by high moisture content within the Pierre Shale. Therefore, the Skyway and Broadmoor areas need to be monitored for future landslides during highprecipitation events. Additional geophysical data are needed to fully determine the nature and extent of the landslides in Colorado Springs. Our results present the first subsurface models of the Skyway and Broadmoor landslides. City planners can use these models as critical data for mitigating future landslides, which can be used for future urban development. [ABSTRACT FROM AUTHOR]

An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning. [ABSTRACT FROM AUTHOR]

Inversions of interseismic geodetic surface velocities often cannot uniquely resolve the three‐dimensional slip‐rate distribution along closely spaced faults. Microseismic focal mechanisms reveal stress information at depth and may provide additional constraints for inversions that estimate slip rates. Here, we present a new inverse approach that utilizes both surface velocities and subsurface stressing‐rate tensors to constrain interseismic slip rates and activity of closely spaced faults. We assess the ability of the inverse approach to recover slip rate distributions from stressing‐rate tensors and surface velocities generated by two forward models: (a) a single strike‐slip fault model and (b) a complex southern San Andreas fault system (SAFS) model. The single fault model inversions reveal that a sparse array of regularly spaced stressing‐rate tensors can recover the forward model slip distribution better than surface velocity inversions alone. Because focal mechanism inversions currently provide normalized deviatoric stress tensors, we perform inversions for slip rate using full, deviatoric or normalized deviatoric forward‐model‐generated stressing‐rate tensors to assess the impact of removing stress magnitude from the constraining data. All the inversions, except for those that use normalized deviatoric stressing‐rate tensors, recover the forward model slip‐rate distribution well, even for the SAFS model. Jointly inverting stressing rate and velocity data best recovers the forward model slip‐rate distribution and may improve estimates of interseismic deep slip rates in regions of complex faulting, such as the southern SAFS; however, successful inversions of crustal data will require methods to estimate stressing‐rate magnitudes. Key Points: Joint inversions of velocity and stressing‐rate data can better estimate slip rates along complex faults than individual inversionsInverting data at multiple depths can better estimate fault locking depth than inverting data at a single depthApplication of the new method requires estimates of crustal deviatoric stressing‐rate tensors with magnitude [ABSTRACT FROM AUTHOR]

A survey was conducted to investigate buried archaeological walls near the Ishtar temple in the ancient Babylon city using electrical resistivity tomography and ground‐penetrating radar methods. The survey includes applying 12 electrical resistivity tomography profiles using dipole–dipole array and a ground‐penetrating radar grid of 55 m × 50 m using 250 MHz antenna. Although the buried walls are consisting of mudbrick masonry and are embedded in a clayey environment, the electrical resistivity tomography method is still able to differentiate the tiny differences between the host materials and the buried walls, which show distinctive wall‐like features with resistivity values ranging between 9 and 15 Ω m. These features may reflect underground‐buried walls with a general width reaching about 2.5 m. The comparison of ground‐penetrating radar profiles and their corresponding electrical resistivity tomography profiles presents that the main architectures are coinciding well. The analysis of the geometry and composition of the walls around the Ishtar temple suggested that the wall‐like reflections on the ground‐penetrating radar slice at a depth between 140 and 150 cm (may be shallower) are underground‐buried walls. These wall‐like reflections show a special trend and orientation that indicate that they may be the remains of rooms belonging to two small houses or the remains of one big private house. [ABSTRACT FROM AUTHOR]

The paper considers the creation of a system for controlling the longitudinal motion of aircraft based on the principle of total energy control. The structure of the control system is presented, including a kernel independent of aircraft and a transition to commands for elevator and thrust deflection obtained from the principle of inverse models. The control system is implemented for models of three aircraft with different aerodynamic configurations. The quality of regulation was studied by numerical simulation. [ABSTRACT FROM AUTHOR]

Inverse problems involve making inference about unknown parameters of a physical process using observational data. This article investigates an important class of inverse problems—the estimation of the initial condition of a spatio-temporal advection-diffusion process using spatially sparse data streams. Three spatial sampling schemes are considered, including irregular, nonuniform and shifted uniform sampling. The irregular sampling scheme is the general scenario, while computationally efficient solutions are available in the spectral domain for nonuniform and shifted uniform sampling. For each sampling scheme, the inverse problem is formulated as a regularized convex optimization problem that minimizes the distance between forward model outputs and observations. The optimization problem is solved by the Alternating Direction Method of Multipliers algorithm, which also handles the situation when a linear inequality constraint (e.g., non-negativity) is imposed on the model output. Numerical examples are presented, code is made available on GitHub, and discussions are provided to generate some useful insights of the proposed inverse modeling approaches. [ABSTRACT FROM AUTHOR]

The design of miniaturized microwave components is largely based on computational models, primarily, full-wave electromagnetic (EM) simulations. The EM analysis is capable of giving an accurate account for cross-coupling effects, substrate and radiation losses, or interactions with environmental components (e.g., connectors). Unfortunately, direct execution of EM-based design tasks, such as parametric optimization or uncertainty quantification (UQ), may turn prohibitively expensive in computational terms. A workaround has been offered by surrogate-assisted procedures that capitalize on replacing expensive EM simulations by fast metamodels, notably data-driven ones. However, the construction of general-purpose metamodels is impeded by the curse of dimensionality as well as a limited capability of approximation techniques to represent highly nonlinear responses of microwave devices. This article proposes a novel technique that integrates the performance-driven modeling paradigm as well as variable-resolution EM simulations. The former focuses on the construction of the surrogate in the parameter space subset encompassing high-quality designs, which effectively addresses the dimensionality issues. The latter—realized through co-kriging—contributes to further computational savings by executing the majority of circuit evaluations at the level of coarse-discretization EM analysis. Verification experiments conducted for three microstrip components demonstrate the superiority of the proposed approach over existing performance-driven techniques, let alone conventional modeling procedures, both with respect to accuracy and computational cost of the surrogate construction. [ABSTRACT FROM AUTHOR]

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid–structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches. [ABSTRACT FROM AUTHOR]

Abstract Ocean anoxia was an important kill mechanism in the end‐Permian mass extinction and uranium isotope data are among the most powerful tools for quantifying the global extent and duration of ocean deoxygenation due to the dependence of uranium isotope fractionation on bottom‐water redox conditions. Although coherent stratigraphic variation in uranium isotope ratios (δ238U) and uranium concentrations ([U]) indicative of prolonged deoxygenation beginning coincident with the extinction is well established, the precise extent of anoxia and associated uncertainty have yet to be quantified. Uncertainty arises from both noise in the data and imprecise knowledge of key parameters within the uranium cycle. In this study, we use the Monte Carlo method to explore a range of scenarios and their implications for the uranium cycle across the Permian‐Triassic boundary and through the first 1.7 million years of the Triassic. We then compare model predictions against measured data using principal component analysis to identify model runs and associated parameter values most compatible with trends in the observed data. The best‐fitting models indicate a pronounced increase in the extent of seafloor anoxia across the Permian/Triassic transition, reaching 18% of the seafloor (95% CI: [11%, 47%]), lasting anywhere from 20 kyr to 1.2 Myr. There is an inverse relationship between the extent and duration of anoxia in the set of best‐fitting models. This initial pulse of pronounced anoxia is followed by a prolonged aftermath, which continues through the remainder of the study interval, of less extensive, yet still expanded, anoxia covering 7.8% of the seafloor (95% CI: [1.6%, 48.9%]). Both expanded and protracted anoxia are required to fit existing data, with no indication of full re‐oxygenation during the study interval.

Ocean anoxia was an important kill mechanism in the end‐Permian mass extinction and uranium isotope data are among the most powerful tools for quantifying the global extent and duration of ocean deoxygenation due to the dependence of uranium isotope fractionation on bottom‐water redox conditions. Although coherent stratigraphic variation in uranium isotope ratios (δ238U) and uranium concentrations ([U]) indicative of prolonged deoxygenation beginning coincident with the extinction is well established, the precise extent of anoxia and associated uncertainty have yet to be quantified. Uncertainty arises from both noise in the data and imprecise knowledge of key parameters within the uranium cycle. In this study, we use the Monte Carlo method to explore a range of scenarios and their implications for the uranium cycle across the Permian‐Triassic boundary and through the first 1.7 million years of the Triassic. We then compare model predictions against measured data using principal component analysis to identify model runs and associated parameter values most compatible with trends in the observed data. The best‐fitting models indicate a pronounced increase in the extent of seafloor anoxia across the Permian/Triassic transition, reaching 18% of the seafloor (95% CI: [11%, 47%]), lasting anywhere from 20 kyr to 1.2 Myr. There is an inverse relationship between the extent and duration of anoxia in the set of best‐fitting models. This initial pulse of pronounced anoxia is followed by a prolonged aftermath, which continues through the remainder of the study interval, of less extensive, yet still expanded, anoxia covering 7.8% of the seafloor (95% CI: [1.6%, 48.9%]). Both expanded and protracted anoxia are required to fit existing data, with no indication of full re‐oxygenation during the study interval. Plain Language Summary: Uranium content and isotope composition in marine limestone provide strong evidence for a rapid reduction of oxygen availability in seawater coincident with the end‐Permian mass extinction and lasting for hundreds of thousands of years. Visual comparison to forward model output for the uranium cycle has previously demonstrated that expanded marine anoxia is the best explanation for trends in the data but statistical best estimates of the extent and duration of anoxia as well as the uncertainty in those values have yet to be developed. Here, Monte Carlo simulations are used to predict uranium concentrations and isotope compositions for marine limestones under a wide range of scenarios. These simulations are then compared to published uranium data to identify the model runs that best fit the observed data. The results favor simulations with either short (∼25 kyr), intense intervals of anoxia (∼20% of the seafloor), or longer intervals (∼1 Myr) with less extensive anoxia (∼10%). Both situations are followed by a prolonged period of still‐expanded but less extensive anoxia that vary depending on the intensity and duration of the initial perturbation (∼1%–48%). Key Points: Uranium cycle models are run using the Monte Carlo method and compared to existing data to quantify the best‐fitting history of ocean anoxia and its associated uncertaintyA temporary increase in anoxia to 18% ± 4% of the seafloor followed by a million‐year‐long episode of 7% ± 4% seafloor anoxia explain trends within uranium isotope and concentration dataSeafloor anoxia persisted at levels far above modern for at least 1.7 Mya after the end‐Permian extinction event [ABSTRACT FROM AUTHOR]

Full-wave electromagnetic (EM) analysis is one of the most important tools in the design of modern microwave components and systems. EM simulation permits reliable evaluation of circuits at the presence of cross-coupling effects or substrate anisotropy, as well as for accounting for interactions with the immediate environment. However, repetitive analyses required by EM-driven procedures, such as parametric optimization or statistical analysis, may entail considerable computational expenditures, often prohibitive. Tackling the high-cost issue fostered the shift toward the incorporation of fast replacement models, including both physics-based surrogates and data-driven ones. While the latter is more popular and versatile, the construction of reliable approximation metamodels for microwave components is hindered by the curse of dimensionality and nonlinearity of system responses. The recent performance-driven modeling methodologies are capable of alleviating these difficulties by confining the surrogate domain to a vicinity of the optimum design manifold (i.e., the region that contains high-quality designs rather than the entire parameter space). Although setting up the model in a constrained domain requires small amounts of training data, domain definition itself requires a set of preoptimized reference designs, acquisition of which is an expensive endeavor. This work proposes a novel approach, which replaces the reference designs with a small set of random observables, thereby considerably reducing the overall cost of the model setup. Comprehensive verification involving several miniaturized microstrip structures demonstrates that our methodology is competitive to performance-driven frameworks both in terms of modeling accuracy and computational efficiency with an average savings of around 80%. [ABSTRACT FROM AUTHOR]

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid–structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches.

The backscattered light captured by a multi-fiber optical probe in a form of spatially-resolved reflectance holds a significant information on the optical properties of an observed sample. The optical properties can be estimated by adopting an appropriate inverse light propagation model that maps the captured reflectance to one or more optical properties of the sample. Among the inverse models, regression models based on neural networks are increasingly gaining popularity in a number of different fields and related applications. The main advantage of such inverse models is their inherent computational efficiency, which allows for determination of optical properties in real time, even on performance-limited embedded systems. The quality of predictions attained with the inverse models largely depends on the information held by the captured spatially resolved reflectance. In this study, we assess the quality of predictions made by neural network-based inverse models from spatially-resolved reflectance captured by optical fiber probes with a different number of linearly-arranged optical fibers. [ABSTRACT FROM AUTHOR]

The lack of gas turbine field data, especially faulty engine data, and the complexity of fault embedding into gas turbines on test benches cause difficulties in representing healthy and faulty engines in diagnostic algorithms. Instead, different gas turbine models are often used. The available models fall into two main categories: physics-based and data-driven. Given the models’ importance and necessity, a variety of simulation tools were developed with different levels of complexity, fidelity, accuracy, and computer performance requirements. Physics-based models constitute a diagnostic approach known as Gas Path Analysis (GPA). To compute fault parameters within GPA, this paper proposes to employ a nonlinear data-driven model and the theory of inverse problems. This will drastically simplify gas turbine diagnosis. To choose the best approximation technique of such a novel model, the paper employs polynomials and neural networks. The necessary data were generated in the GasTurb software for turboshaft and turbofan engines. These input data for creating a nonlinear data-driven model of fault parameters cover a total range of operating conditions and of possible performance losses of engine components. Multiple configurations of a multilayer perceptron network and polynomials are evaluated to find the best data-driven model configurations. The best perceptron-based and polynomial models are then compared. The accuracy achieved by the most adequate model variation confirms the viability of simple and accurate models for estimating gas turbine health conditions.

Thermochronometry is widely used to track exhumation, the motion of rock towards Earth’s surface, and to gain fresh insights into geodynamic and geomorphic processes. Applications require models to reconstruct a rock’s cooling history as it is exhumed from higher temperatures at depth within the crust to cooler shallower levels and eventually Earth’s surface. Thermochronometric models are dependent on the predictable accumulation and the temperature-dependent loss of radiogenic daughter products measured in the laboratory. However, there are many geologically reasonable scenarios that will yield very similar thermochronometric ages. This similarity hinders finding the actual scenario, so instead an approximate model is sought. Finding suitable model parameters is a potentially ill-posed inverse problem that requires making decisions about how best to extract information from the data and how to combine data to leverage redundant information and reduce the impact of data noise. Often these decisions lead to differences in conclusions of studies and such discrepancies have led to heated debates. Here, we discuss debates centred on the use of a variety of modelling approaches and potential sources of biases that lead to differences in the predicted exhumation rate. We also provide some suggestions about future research paths that will help resolve these debates.

In the paper, a framework for computationally-efficient design optimization of compact rat-race couplers (RRCs) is discussed. A class of hybrid RRCs with variable operating conditions is investigated, whose size reduction is obtained by replacing ordinary transmission lines with compact microstrip resonant cells (CMRCs). Our approach employs a bottom-up design strategy leading to the development of compact RRCs through rapid design optimization of its building blocks and a subsequent fine tuning to account for parasitic cross-coupling effects. The fundamental component of the proposed method is an inverse CMRC surrogate model, covering a wide range of cell electrical parameters, and enabling a convenient adjustment of coupler bandwidth. Having the surrogate model established, it is possible to produce close-tooptimum CMRC dimensions at a negligible computational cost. The subsequent correction step requires only up to two electromagnetic simulations of the CMRC. The proposed method is demonstrated by designing an RRC for several operational bandwidths. Experimental results are also provided. [ABSTRACT FROM AUTHOR]

Many-Objective Optimization Problems, or MaOPs, are complex optimization problems with more than three objective functions. Traditional Multi-Objective Evolutionary Algorithms (MOEAs) have shown poor scalability in solving this problem. Neuroevolution techniques can be used to overdraw that problem. The use of machine learning techniques to enhance optimization algorithms applied to MaOPs has drawn attention due to their capacity to add domain knowledge during the search process. One method of this kind is inverse surrogate modeling for fitness estimation, which uses machine learning models to enhance MOEAs, mapping the objective function values to the decision variables. Some inverse modeling methods in literature have performed well in various optimization problems. Despite the promising results, new strategies using the generated knowledge during the search can be further improved. The main goal of this work is to propose a framework that uses an inverse modeling approach coupled with an MOEA. The inverse surrogate modeling is used to sample solutions that are combined with the evolutionary search of the MOEA. The framework is evaluated in different scenarios, including multi-objective benchmark problems and real-world problems. The results show that the proposed framework had statistically better or equivalent results in 86% scenarios in the benchmark problems and 100% scenarios in the real-world problem. • Inverse surrogate framework for solving many-objective optimization problems. • Performance comparison with diverse metaheuristic algorithms. • Benchmark and real-world optimization problems are used for evaluation. • The framework is recommended in scenarios with restricted number of evaluations. [ABSTRACT FROM AUTHOR]

Spiral galaxies and their rotation curves have key characteristics of differentially spinning objects. Oblate spheroid shapes are a consequence of spin and reasonably describe galaxies, indicating that their matter is distributed in gravitationally interacting homeoidal shells. Here, previously published equations describing differentially spinning oblate spheroids with radially varying density are applied to 51 galaxies, mostly spirals. A constant volumetric density (r, kg m−3) is assumed for each thin homeoid in these formulae, after Newton, which is consistent with RCs being reported simply as a function of equatorial radius r. We construct parameter-free inverse models that uniquely specify mass inside any given r, and thus directly constrain r vs. r solely from velocity v (r) and galactic aspect ratios (assumed as 1:10 for spirals when data are unavailable). Except for their innermost zones, r is proven to be closely proportional to rn, where the statistical average of n for all 36 spirals studied is −1.80 ± 0.40. Our values for interior densities compare closely with independently measured baryon density in appropriate astronomical environments: for example, calculated r at galactic edges agrees with independently estimated r of intergalactic media (IGM). Our finding that central densities increase with galaxy size is consistent with behavior exhibited by diverse self-gravitating entities. Our calculated mass distributions are consistent with visible luminosity and require no non-baryonic component.

In this work, a novel model predictive control (MPC) scheme is introduced, by integrating direct and indirect neural control methodologies. The proposed approach makes use of a robust inverse radial basis function (RBF) model taking into account the applicability domain criterion, in order to provide a suitable initial starting point for the optimizer, thus helping to solve the optimization problem faster. The performance of the proposed controller is evaluated on the control of a highly nonlinear system with fast dynamics and compared with different control schemes. Results show that the proposed approach outperforms the rivaling schemes in terms of response; moreover, it solves the optimization problem in less than one sampling period, thus effectively rendering MPC-based controllers capable of handling systems with fast dynamics. [ABSTRACT FROM AUTHOR]

The technological differences between traditional robotics and soft robotics have an impact on all of the modeling tools generally in use, including direct kinematics and inverse models, Jacobians, and dynamics. Due to the lack of precise modeling and control methods for soft robots, the promising concepts of using such design for complex applications (medicine, assistance, domestic robotics, etc.) cannot be practically implemented. This paper presents a first unified software framework dedicated to modeling, simulation, and control of soft robots. The framework relies on continuum mechanics for modeling the robotic parts and boundary conditions like actuators and contacts using a unified representation based on Lagrange multipliers. It enables the digital robot to be simulated in its environment using adirectmodel. The model can also be inverted online using an optimization-based method which allows to control the physical robots in the task space. To demonstrate the effectiveness of the approach, we present various soft robots scenarios including ones where the robot is interacting with its environment. The software has been built on top of SOFA, an open-source framework for deformable online simulation and is available athttps://project.inria.fr/softrobot/. [ABSTRACT FROM PUBLISHER]

Autonomous exploration of motor skills is a key capability of learning robotic systems. Learning motor skills can be formulated as inverse modeling problem, which targets at finding an inverse model that maps desired outcomes in some task space, e.g., via points of a motion, to appropriate actions, e.g., motion control policy parameters. In this paper, autonomous exploration of motor skills is achieved by incrementally learning inverse models starting from an initial demonstration. The algorithm is referred to as skill babbling, features sample-efficient learning, and scales to high-dimensional action spaces. Skill babbling extends ideas of goal-directed exploration, which organizes exploration in the space of goals. The proposed approach provides a modular framework for autonomous skill exploration by separating the learning of the inverse model from the exploration mechanism and a model of achievable targets, i.e. the workspace. The effectiveness of skill babbling is demonstrated for a range of motor tasks comprising the autonomous bootstrapping of inverse kinematics and parameterized motion primitives. [ABSTRACT FROM AUTHOR]

Spiral galaxies are spinning, internally densified objects. The Virial Theorem explains galactic rotation curves via its linkage of the rotation rate to the gravitational self-potential (Ug) and the moment of inertia of oblate spheroids. We devise a new analytical solution that allows galactic mass and volumetric density (kg m−3) profiles to be extracted from velocity and its derivative as functions of equatorial radius. This inverse model of rotation curves is direct, unambiguous, and parameter-free. To probe galactic evolution, we combine energy minimization, angular momentum conservation, and the Virial Theorem. The characteristic flat shape of spiral galaxies results from an initial vertical collapse of a spinning, colossal molecular cloud, which reduces Ug while conserving angular momentum. Subsequent inward densification further lowers Ug, producing bulges, but conserving angular momentum requires mass loss, achieved by the outward movement of the distal parts of the spiral arms. Many of the evolutionary patterns of spiral galaxies are exhibited by the changing shapes of hurricanes during formation and dissipation. In contrast, elliptical galaxies evolve from a cloud with roughly random orbits into progressively rounder, internally denser objects, with angular momentum conserved by the development of vertically oriented jets. Galactic evolution is governed by the initial inventory of mass and angular momentum, resulting in separate paths for elliptical and spiral galaxies, as is codified in Hubble’s tuning fork diagram.

Due to increasing contribution of renewable energy sources to the overall energy production, future control of energy consumption should achieve the principle 'generation following demand'. Within this approach, also known as demand side management, consumers are prompted to alter demands through an incentive signal. This work addresses the use of variable price as such incentive signal to influence power consumption of private households in a controllable manner. The mathematical modelling is based on simulations of optimal power flow and includes several optimisation constraints and criteria, according to which both direct and inverse models are constructed. Therefore, the problem of power signals and price signals is considered as fully‐coupled. The analysis is performed for a model, representing typical conditions and scenarios of a residential network with household customers, including solar and wind power plants. The optimisation criteria are constructed with the emphasis on distribution system operators. The results of computations, performed with simplified and extended optimisation models, show a pronounced effect on the overall power consumption, reduction of the extreme values and minimisation of the peaks. Finally, further steps are proposed to extend the model to target various optimisation constraints. [ABSTRACT FROM AUTHOR]

• Inverse models-based evolutionary algorithms can generate as many solutions as required in interest regions at a low budget cost. • The number of predefined reference vectors remains constant resulting in a low computational efficiency. • The non-dominated solutions are utilized for interpolations in each subpopulation. • An adaptive interpolation range coefficient for adaptive adjustments of the interpolation region is to balance the exploitation and the exploration. • Hiring elite individuals in the whole population to enhance the solution convergence. Inverse models-based evolutionary algorithms can generate as many solutions as required in interest regions at a low budget cost. However, the objective value used for the inverse model in the original regularity-based algorithm using Gaussian process-based inverse models, (IM-MOEA), is linearly and separately sampled for each objective according to its maximum and minimum values so far searched. Consequently, some non-optimal even infeasible solutions may be sampled. Also, the number of the predefined reference vectors remains constant throughout the whole evolution. Consequently, it may result in a low computational efficiency for multi-objective optimization ones (MOOs) with nonuniform or disconnected Pareto fronts. In this respect, an improved regularity-based vector evolutionary algorithm for multi-objective optimizations is presented in the paper. The main component of the proposed algorithm is to obtain the training data for inverse models by interpolation in the objective space. The population is divided into subpopulations according to the number of objectives. The non-dominated solutions are utilized for interpolations in each subpopulation. The inverse models are finally used to generate offspring using data by interpolation. An adaptive interpolation range coefficient is proposed for adaptive adjustments of the interpolation region to balance the exploitation and the exploration searches. An elite strategy of hiring elite individuals in the whole population to the current subpopulation is employed to enhance the algorithm convergence. Experimental results on two test suites show the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Objectives. We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM). Approach. A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.33 S m -1 was defined. The vertices were then randomly perturbed radially up to 2%, 4%, 6%, 8% and 10% of the radius. For each head perturbation case, the forward signal was calculated for dipolar sources located at 2 cm, 4 cm, 6 cm and 8 cm from the origin (center of the sphere), and for a 324 sensor array located at 10 cm to 15 cm from the origin. Equivalent current dipole (ECD) source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was then analyzed in the spatial frequency domain, and the signal and ECD errors were quantified relative to the unperturbed case. Main results. In the noiseless and high signal-to-noise ratio (SNR) case of approximately ≥6 dB, inaccuracies in our spherical BEM head conductor models lead to increased signal and ECD inaccuracies when sensor arrays are placed closer to the head. This is true especially in the case of deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays allows for an improved ECD fit and outweighs the effects of head geometry inaccuracies. Significance. OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential., (Creative Commons Attribution license.)

Large-scale mortality events in forests are increasing in frequency and intensity and can lead to both intermediate- and long-term changes in these systems. Specialist pests and pathogens are unique disturbances, as they commonly target individual species that are relatively prevalent in the community., Understanding the consequences of pathogen-caused mortality requires using sometimes limited available data to create statistical models that can forecast future community states., In the last two decades, ash dieback disease has swept through Europe causing widespread mortality of Fraxinus excelsior L. (European ash) across much of its distribution. In the UK, F. excelsior is an abundant and ecologically important species., Using demographic data from an 18 ha plot in Wytham Woods, Oxfordshire, we built models that forecast the response of this forest plot to the loss of F. excelsior. We combine integral projection models and individual-based models to link models of growth, survival and fecundity to population dynamics. We demonstrate likely responses in Wytham by comparing projections under different levels of F. excelsior mortality. To extrapolate results to other systems, we test hypotheses regarding the role of abundance, spatial structure and demographic differences between species in determining community response to disease disturbance., We show that the outcome of succession is determined largely by the differing demographic strategies and starting abundances of competing species. Spatial associations between species were shown to have little effect on community dynamics at the spatial scale of this plot., Synthesis. Host-specific pests and pathogens are an increasingly important type of disturbance. We have developed a framework that makes use of forest inventory data to forecast changes in the population dynamics of remaining species and the consequences for community structure. We use our framework to predict how a typical British woodland will respond to ash dieback disease and show how vital rates, spatial structure and abundance impact the community response to the loss of a key species. [ABSTRACT FROM AUTHOR]

The present paper implements a novelty semi-active structural control design on a two-story building, with the aim of reducing vibrations caused by transient type loads. The analyzed structure corresponds to an experimental prototype that was fully characterized and modeled according to the diaphragm hypothesis. The controller used was based on the action of a pair of real magnetorheological (MR) dampers whose operation is emulated by the phenomenological model. These mechanisms are governed by a numerical system that is based on non-linear autoregressive model with exogenous inputs (NARX)-type artificial neural networks, which have the ability to determine the necessary optimal control forces and the voltages required for the development of these forces through a prediction model and an inverse model, which are pioneers in this kind of systems. The results obtained show that the control design based on neural networks that was developed in the present study is a reliable and efficient, achieving reductions of up to 69% for the peak response value. [ABSTRACT FROM AUTHOR]

Abstract: We investigate the role of redundancy for exploratory learning of inverse functions, where an agent learns to achieve goals by performing actions and observing outcomes. We present an analysis of linear redundancy and investigate goal-directed exploration approaches, which are empirically successful, but hardly theorized except negative results for special cases, and prove convergence to the optimal solution. We show that the learning curves of such processes are intrinsically low-dimensional and S-shaped, which explains previous empirical findings, and finally compare our results to non-linear domains. [Copyright &y& Elsevier]

Highlights: [•] Poor-pitch singing may result from a poorly functioning inverse model of the auditory–vocal system. [•] Deficits of poor-pitch singing may be reduced for tasks that minimize use of inverse model, such as imitating yourself. [•] All singers in the present experiments imitated sung pitch more accurately when imitating recordings of themselves. [•] This advantage was enhanced for poor-pitch singers. [•] The self-advantage remained when self-recognition was reduced and when pitches were transposed. [ABSTRACT FROM AUTHOR]

Highlights: [•] Focusing on cortical areas is too restrictive to explain action understanding ability. [•] We propose that sub-cortical areas support action understanding ability. [•] Cortical and sub-cortical processes allow acquisition of action understanding ability. [Copyright &y& Elsevier]

Abstract: We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy parameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters. We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: (1) learning the inverse kinematics in a highly-redundant robotic arm, (2) learning omnidirectional locomotion with motor primitives in a quadruped robot, and (3) an arm learning to control a fishing rod with a flexible wire. We show that (1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; (2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly, as well as more efficient than different standard active motor babbling methods; (3) this architecture allows the robot to actively discover which parts of its task space it can learn to reach and which part it cannot. [Copyright &y& Elsevier]

Abstract: The topographic ambiguity and reference-dependency that has plagued EEG/ERP research throughout its history are largely attributable to volume conduction, which may be concisely described by a vector form of Ohm’s Law. This biophysical relationship is common to popular algorithms that infer neuronal generators via inverse solutions. It may be further simplified as Poisson’s source equation, which identifies underlying current generators from estimates of the second spatial derivative of the field potential (Laplacian transformation). Intracranial current source density (CSD) studies have dissected the “cortical dipole” into intracortical sources and sinks, corresponding to physiologically-meaningful patterns of neuronal activity at a sublaminar resolution, much of which is locally cancelled (i.e., closed field). By virtue of the macroscopic scale of the scalp-recorded EEG, a surface Laplacian reflects the radial projections of these underlying currents, representing a unique, unambiguous measure of neuronal activity at scalp. Although the surface Laplacian requires minimal assumptions compared to complex, model-sensitive inverses, the resulting waveform topographies faithfully summarize and simplify essential constraints that must be placed on putative generators of a scalp potential topography, even if they arise from deep or partially-closed fields. CSD methods thereby provide a global empirical and biophysical context for generator localization, spanning scales from intracortical to scalp recordings. [Copyright &y& Elsevier]

Abstract: Given a random vector , we address the question of linear separability of , that is, the task of finding a linear operator such that we have with statistically independent random vectors . As this requirement alone is already fulfilled trivially by being independent of the empty rest, we require that the components be not further decomposable. We show that if has finite covariance, such a representation is unique up to trivial indeterminacies. We propose an algorithm based on this proof and demonstrate its applicability. Related algorithms, however with fixed dimensionality of the subspaces, have already been successfully employed in biomedical applications, such as separation of fMRI recorded data. Based on the presented uniqueness result, it is now clear that also subspace dimensions can be determined in a unique and therefore meaningful fashion, which shows the advantages of independent subspace analysis in contrast to methods like principal component analysis. [Copyright &y& Elsevier]

Magnetoencephalography provides precise information about the temporal dynamics of brain activation and is an ideal tool for investigating rapid cognitive processing. However, in many cognitive paradigms visual stimuli are used, which evoke strong brain responses (typically 40-100 nAm in V1) that may impede the detection of weaker activations of interest. This is particularly a concern when beamformer algorithms are used for source analysis, due to artefacts such as 'leakage' of activation from the primary visual sources into other regions. We have previously shown (Quraan et al. ) that we can effectively reduce leakage patterns and detect weak hippocampal sources by subtracting the functional images derived from the experimental task and a control task with similar stimulus parameters. In this study we assess the performance of three different subtraction techniques. In the first technique we follow the same post-localization subtraction procedures as in our previous work. In the second and third techniques, we subtract the sensor data obtained from the experimental and control paradigms prior to source localization. Using simulated signals embedded in real data, we show that when beamformers are used, subtraction prior to source localization allows for the detection of weaker sources and higher localization accuracy. The improvement in localization accuracy exceeded 10 mm at low signal-to-noise ratios, and sources down to below 5 nAm were detected. We applied our techniques to empirical data acquired with two different paradigms designed to evoke hippocampal and frontal activations, and demonstrated our ability to detect robust activations in both regions with substantial improvements over image subtraction. We conclude that removal of the common-mode dominant sources through data subtraction prior to localization further improves the beamformer's ability to project the n-channel sensor-space data to reveal weak sources of interest and allows more accurate localization. [ABSTRACT FROM AUTHOR]

Abstract: The fast pace in the development of indoor sensors and communication technologies is allowing a great amount of sensor data to be utilized in various areas of indoor air applications, such as estimating indoor airflow patterns. The development of such an inverse model and the design of a sensor system to collect appropriate data are discussed in this study. Algebraic approaches, including singular value decomposition (SVD), are evaluated as methods to inversely estimate airflow patterns given limited sensor measurements. In lieu of actual sensor data, computational fluid dynamics data are used to evaluate the accuracy of the airflow patterns estimated by the inverse models developed in this study. It was found that the airflow patterns estimated by the linear inverse SVD model were as accurate as those estimated by the nonlinear inverse-multizone model. For the zones tested, sensor measurements along on the walls and near the inlet and outlet provided the greatest improvement in the accuracy of the estimated airflow patterns when compared with the results using measurements from other locations. [Copyright &y& Elsevier]

This paper posits that the brain evolved for the control of action rather than for the development of cognition per se. We note that the terms commonly used to describe brain-behavior relationships define, and in many ways limit, how we conceptualize and investigate them and may therefore constrain the questions we ask and the utility of the 'answers' we generate. Many constructs are so nonspecific and over-inclusive as to be scientifically meaningless. 'Executive function' is one such term in common usage. As the construct is increasingly focal in neuroscience research, defining it clearly is critical. We propose a definition that places executive function within a model of continuous sensorimotor interaction with the environment. We posit that control of behavior is the essence of 'executive function,' and we explore the evolutionary advantage conferred by being able to anticipate and control behavior with both implicit and explicit mechanisms. We focus on the cerebellum's critical role in these control processes. We then hypothesize about the ways in which procedural (skill) learning contributes to the acquisition of declarative (semantic) knowledge. We hypothesize how these systems might interact in the process of grounding knowledge in sensorimotor anticipation, thereby directly linking movement to thought and 'embodied cognition.' We close with a discussion of ways in which the cerebellum instructs frontal systems how to think ahead by providing anticipatory control mechanisms, and we briefly review this model's potential applications. [ABSTRACT FROM AUTHOR]