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Monte Carlo path tracing is a widely used method for generating realistic rendering results in multimedia applications, but often suffers from poor convergence and heavy sampling budget. Insufficient path samples may lead to noisy results. Some noises are hidden in textures, and the human visual system cannot detect them all. Just noticeable difference (JND) quantifies this limitation as a full-reference perception threshold. In rendering, the reference is unavailable and a surrogate is required. This paper proposed a perception-JND-driven path tracing method for reducing sampling budget. We tested and verified the surrogate JND thresholds derived from current rendering results. Then, we introduced difference pooling module and shading restart module to control perceptual convergence. Further, to improve accuracy, we developed the strategy for optimizing sampling steps. Experiments showed that the proposed method outperformed the state-of-the-art method at moderately low sampling levels, offering a lightweight and efficient solution to reducing sample budget while improving visual quality. [ABSTRACT FROM AUTHOR]

With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached. [ABSTRACT FROM AUTHOR]

The Marine Predators Algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean's predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators – i.e. Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability. [ABSTRACT FROM AUTHOR]

Sensors are the main components in Cyber-Physical Systems (CPS), which transmit large amounts of physical values and big data to computing platforms for processing. On the other hand, the embedded processors (as edge devices in fog computing) spend most of their time reading the sensor signals as compared with computing time. The impact of sensors on the performance of fog computing is very great, thus, the enhancement of the reading time of sensors will positively affect the performance of fog computing, and solves the CPS challenges such as delay, timed precision, temporal behavior, energy, and cost. In this paper, we propose an algorithm based on the 1st derivative of the sensor signal to generate an adaptive sampling frequency. The proposed algorithm uses an adaptive frequency to capture the sudden and rapid change in sensor signal in the steady state. Finally, we realize and tested it using the Ptolemy II Modeling Environment. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]

Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The efficient design optimization of electric machines for electric power steering (EPS) applications poses challenges in meeting demanding performance criteria, including high power density, efficiency, and low vibration. Traditional optimization approaches often fail to find a global solution or suffer from excessive computation time. In response to the limitations of traditional approaches, this paper introduces a novel methodology by incorporating a Gaussian process-based adaptive sampling technique into a surrogate-assisted optimization process using a metaheuristic algorithm. Validation on a 72-slot/8-pole interior permanent magnet (IPM) machine demonstrates the superiority of the proposed approach, showcasing improved exploitation–exploration balance, faster convergence, and enhanced repeatability compared to conventional optimization methods. The proposed design process is then applied to two surface PM (SPM) machine configurations with 9-slot/6-pole and 12-slot/10-pole combinations for EPS applications. The results indicate that the 12-slot/10-pole SPM design surpasses the alternative design in torque density, efficiency, cogging torque, torque ripple, and manufacturability. [ABSTRACT FROM AUTHOR]

Raw point clouds are usually sparse and incomplete, inevitably containing outliers or noise from 3D sensors. In this paper, an improved SA-Net based on an encoder-decoder structure is proposed to make it more robust in predicting complete point clouds. The encoder of the original SA-Net network is very sensitive to noise in the feature extraction process. Therefore, we use PointASNL as the encoder, which weights around the initial sampling points through the AS module (Adaptive Sampling Module) and adaptively adjusts the weight of the sampling points to effectively alleviate the bias effect of outliers. In order to fully mine the feature information of point clouds, it captures the neighborhood and long-distance dependencies of sampling points through the LNL module (Local-NonLocal Module), providing more accurate information for point cloud processing. Then, we use the encoder to extract local geometric features of the incomplete point cloud at different resolutions. Then, an attention mechanism is introduced to transfer the extracted features to a decoder. The decoder gradually refines the local features to achieve a more realistic effect. Experiments on the ShapeNet data set show that the improved point cloud completion network achieves the goal and reduces the average chamfer distance by 3.50% compared to SA-Net. [ABSTRACT FROM AUTHOR]

In large-scale data processing, graph analytics of complex interaction networks are indispensable. As the whole graph processing and analytics can be inefficient and usually impractical, graph sampling by keeping a portion of the original graph becomes a favorable approach. While prior work focused on fixed edge and node selection strategy based on predetermined criteria, without adaptive feedback to adjust the sampling process, this type of sampling algorithms has limited flexibility and estimation accuracy for complex graphs. In this paper, we propose an adaptive graph sampling framework, and design AdapES, an adaptive edge sampling algorithm based on this framework. Compared to non-adaptive sampling methods, our approach can continually monitor the difference between the current sampled subgraph and the original graph, and dynamically adjust the edge sampling probability based on this observed sampling difference. Guided by a preset sampling goal, this algorithm automatically adapts to the fluctuations in the random sampling process with high flexibility. The experimental evaluation in 11 datasets demonstrates that AdapES outperforms other algorithms for preserving various graph properties and statistics. [ABSTRACT FROM AUTHOR]

Adaptive information-sampling approaches enable efficient selection of mobile robots' waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot's energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm's information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point's informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map's accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable. [ABSTRACT FROM AUTHOR]

Stochastic variations of the operation conditions and the resultant variations of the aerodynamic performance in Low-Pressure Turbine (LPT) can often be found. This paper studies the aerodynamic performance impact of the uncertain variations of flow parameters, including inlet total pressure, inlet flow angle, and turbulence intensity for an LPT cascade. Flow simulations by solving the Reynolds-averaged Navier–Stokes equations, the SST turbulence model, and γ − R e ˜ θ t transition model equations are first carried out. Then, a Gaussian process (GP) based on an adaptive sampling technique is introduced. The accuracy of adaptive GP (ADGP) is proven to be high through a function experiment. Using ADGP, the uncertainty propagation models between the performance parameters, including total pressure-loss coefficient, outlet flow angle, Zweifel number, and the uncertain inlet flow parameters, are established. Finally, using the propagation models, uncertainty quantifications of the performance changes are conducted. The results demonstrate that the total pressure-loss coefficient and Zweifel number are sensitive to uncertainties, while the outlet flow angle is almost insensitive. Statistical analysis of the flow field by direct Monte Carlo simulation (MCS) shows that flow transition on the suction side and viscous shear stress are rather sensitive to uncertainties. Moreover, Sobol sensitivity analysis is carried out to specify the influence of each uncertainty on the performance changes in the turbine cascade. [ABSTRACT FROM AUTHOR]

In this paper, we focus on tackling the problem that one sparse base alone cannot represent the different content of the image well in the image reconstruction for compressed sensing, and the same sampling rate is difficult to ensure the precise reconstruction for the different content of the image. To address this challenge, this paper proposed a novel approach that utilized two sparse bases for the representation of image. Moreover, in order to achieve better reconstruction result, the adaptive sampling has been used in the sampling process. Firstly, DCT and a double-density dual-tree complex wavelet transform were utilized as two different sparse bases to represent the image alternatively in a smoothed projected Landweber reconstruction algorithm. Secondly, different sampling rates were adopted for the reconstruction of different image blocks after segmenting the entire image. Experimental results demonstrated that the images reconstructed with the two bases were largely superior to that reconstructed with a single base, and the PSNR could be improved further after using the adaptive sampling. [ABSTRACT FROM AUTHOR]

Supercritical water fluidized beds (SCWFBs) are promising and efficient reactors for the gasification of coal in supercritical water. The understanding and investigation of multi-phase flows as well as the gasification process usually rely on time-consuming experiments or numerical simulations, which prohibit fast and full exploration of the single and coupled effects of the operation and geometric parameters. To this end, this paper builds an efficient surrogate-assisted parameter analysis framework for the SCWFB reactor. Particularly, (1) it establishes a steady numerical simulation model of the SCWFB reactor for the subsequent analysis; and (2) it employs a Gaussian process surrogate modeling via efficient adaptive sampling to serve as an approximation for predicting the carbon conversion efficiency (CE) of the reactor. Based on this parameter analysis framework, this paper investigates the effects of five independent parameters (the mass flow rate of supercritical water, mass flow rate of the coal slurry, temperature of supercritical water, temperature of the outer wall and reactor length) and their interactions on the reaction performance in terms of the carbon conversion efficiency (CE). We found that the CE increases as a function of the temperature of supercritical water, the temperature of the outer wall and the reactor length; while it decreases as a function of the mass flow rate of supercritical water and the mass flow rate of the coal slurry. Additionally, the global sensitivity analysis demonstratesthat the influence of the temperature of the outer wall exerts a stronger effect than all the other factors on the CE, and the coupled interaction among parameters has a slight effect on the CE. This research provides useful guidance for scaled-up designs and optimization of the SCWFB reactor. [ABSTRACT FROM AUTHOR]

AbstractWith the rapid development of the digital manufacturing industry, the nature of quality data has transformed from simple univariate or multivariate characteristics to big data comprising multimedia elements such as images and videos. The utilization of image data for automated monitoring and anomaly detection has gained significant attention in recent years, which also poses new and complex challenges. A critical challenge is the substantial demand for sensing and computation resources. When these resources are limited, only a fraction of the image data can be observed and analyzed. Hence, adaptive sampling becomes imperative to select the most informative pixels that effectively capture anomaly information. In this paper, we propose a novel recurrent neural network framework named Multi Task-Recurrent Attention Model (MT-RAM) which incorporates adaptive sampling for anomaly classification and localization in partially observable image data. MT-RAM emulates human-like perception by generating a sequence of glimpses to comprehend the image, with the location of each glimpse depending on the information gleaned from previous glimpses. Thorough numerical studies and case studies are conducted to evaluate the performance of MT-RAM in comparison to state-of-the-art adaptive-sampling-based anomaly detection methods. [ABSTRACT FROM AUTHOR]

Sequential sampling methods are often used to estimate functions describing models subjected to time‐intensive simulations or expensive experiments. These methods provide guidelines for point selection in the domain to capture maximum information about the function. However, in most sequential sampling methods, determining a new point is a time‐consuming process. In this paper, we propose a new method, named Sieve, to sequentially select points of an initially unknown function based on the definition of proper intervals. In contrast with existing methods, Sieve does not involve function estimation at each iteration. Therefore, it presents a greater computational efficiency for achieving a given accuracy in estimation. Sieve brings in tools from computational geometry to subdivide regions of the domain efficiently. Further, we validate our proposed method through numerical simulations and two case studies on the calibration of internal combustion engines and the optimal exploration of an unknown environment by a mobile robot. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINNs) have shown remarkable achievements in solving partial differential equations (PDEs). However, their performance is limited when encountering oscillatory part in the solutions of PDEs. Therefore, this paper proposes a multi-scale deep neural network with periodic activation function to achieve high-frequency to low-frequency conversion, which can capture the oscillation part of the solution of PDEs. Moreover, the use of adaptive sampling method can adaptively change the location and distribution of residual points, improving the performance of the network. Additionally, the gradient-enhanced strategy is also utilized to embed the gradient information of the PDEs into the loss function of the neural network, which further improves the accuracy of PINNs. Through the numerical experiments verification, it is found that our method is better than PINNs in terms of accuracy and efficiency. • A MscaleDNNs structure with periodic activation functions is proposed. • The proposed neural network has better stability and robustness. • The effectiveness of the method was verified through numerical experiments. [ABSTRACT FROM AUTHOR]

This paper introduces a fully automated greedy algorithm for the construction of parameterized behavioral models of electromagnetic structures, targeting at the same time uniform model stability and passivity. The proposed algorithm is able to determine a small set of parameter configurations for which an external solver provides on-the-fly the sampled scattering parameters of the structure over a predetermined frequency band. These samples are subjected to a multivariate rational/polynomial fitting process, which iteratively leads to a parameterized descriptor realization of the model. The main novel contribution in this paper is the adoption of a model-based approach for the adaptive augmentation of an initially small set of frequency responses, each corresponding to a randomly selected parameter configuration. In particular, the locations of the in-band passivity violations of intermediate macromodels constructed at each iteration are used as a proxy for the model-data error in those regions where input data are not available. This physics-based consistency check, which is enabled by recent developments in multivariate passivity characterization based on Skew-Hamiltonian–Hamiltonian (SHH) spectra, is combined with standard space exploration metrics to obtain a small-size and automatically determined distribution of points in the parameter space, leading to the construction of an accurate macromodel with a very limited number of external field solver runs. The embedded passivity check and enforcement process guarantees that either the final model is passive throughout the parameter space or the residual violations, if present, are negligible for practical purposes. Several examples validate the proposed approach for up to three concurrent parameters. [ABSTRACT FROM AUTHOR]

Real-time wireless sensor networks are an emerging technology for continuous environmental monitoring. But real-world deployments are constrained by resources, such as power, memory, and processing capabilities. In this paper, we discuss a set of techniques to maximize the lifetime of a system deployed in south India for detecting rain-fall induced landslides. In this system, the sensing subsystem consumes 77.5%, the communication subsystem consumes 22%, and the processing subsystem consumes 0.45% of total power consumption. Hence, to maximize the lifetime of the system, the sensing subsystem power consumption has to be reduced. The major challenge to address is the development of techniques that reduce the power consumption, while preserving the reliability of data collection and decision support by the system. This paper proposes a wavelet-based sampling algorithm for choosing the minimum sampling rate for ensuring the data reliability. The results from the wavelet sampling algorithm along with the domain knowledge have been used to develop context aware data collection models that enhance the lifetime of the system. Two such models named context aware data management (CAD) and context aware energy management (CAE) have been devised. The results show that the CAD model extends the lifetime by six times and the CAE model does so by 20 times when compared with the continuous data collection model, which is the existing approach. In this paper, we also developed mathematical modeling for CAD and CAE, which have been validated using real-time data collected in the past. [ABSTRACT FROM PUBLISHER]

An attention-aware patch-based deep-learning model for a blind 360-degree image quality assessment (360-IQA) is introduced in this paper. It employs spatial attention mechanisms to focus on spatially significant features, in addition to short skip connections to align them. A long skip connection is adopted to allow features from the earliest layers to be used at the final level. Patches are properly sampled on the sphere to correspond to the viewports displayed to the user using head-mounted displays. The sampling incorporates the relevance of patches by considering (i) the exploration behavior and (ii) a latitude-based selection. An adaptive strategy is applied to improve the pooling of local patch qualities to global image quality. This includes an outlier score rejection step relying on the standard deviation of the obtained scores to consider the agreement, as well as a saliency to weigh them based on their visual significance. Experiments on available 360-IQA databases show that our model outperforms the state of the art in terms of accuracy and generalization ability. This is valid for general deep-learning-based models, multichannel models, and natural scene statistic-based models. Furthermore, when compared to multichannel models, the computational complexity is significantly reduced. Finally, an extensive ablation study gives insights into the efficacy of each component of the proposed model. [ABSTRACT FROM AUTHOR]

This paper extends the recently proposed power-particle-based fluid simulation method with staggered discretization, GPU implementation, and adaptive sampling, largely enhancing the efficiency and usability of the method. In contrast to the original formulation which uses co-located pressures and velocities, in this paper, a staggered scheme is adapted to the Power Particles to benefit visual details and computing efficiency. Meanwhile, we propose a novel facet-based power diagrams construction algorithm suitable for parallelization and explore its GPU implementation, achieving an order of magnitude boost in performance over the existing code library. In addition, to utilize the potential of Power Particles to control individual cell volume, we apply adaptive particle sampling to improve the detail level with varying resolution. The proposed method can be entirely carried out on GPUs, and our extensive experiments validate our method both in terms of efficiency and visual quality. [ABSTRACT FROM AUTHOR]

Exchanges and interactions between water masses are typically concentrated across ocean fronts, requiring targeted sampling. As fronts are dynamic in their spatiotemporal extent, they can be hard to map with limited sampling resources. In this paper, we use horizontal temperature gradient as the defining feature of a front to adapt the path of an autonomous underwater vehicle to follow these regions of scientific interest. We present results from simulations and successful field operations in the Barents Sea, where the vehicle equipped with a microstructure sensor crossed the polar front while adapting its trajectory to the front position. Using in situ measurements, we estimate the temperature gradient across the polar front and cross it at different depths. We show that the vehicle is able to estimate and detect the temperature gradient maximum and to adjust its path to provide measurements of temperature, salinity and microstructure along its path, on both sides of the front. This is a step toward the integration of turbulence measurements on autonomous underwater vehicles, showing promise of more targeted sampling of turbulence in regions of large spatial gradients and interactions between water masses, such as fronts. The horizontal vehicle path complements the more traditional vertical profile measurements, providing a finer horizontal spatial resolution. The adaptive behavior of autonomous agents contributes to increased accuracy in targeted measurements as well as expanded resource utilization. [ABSTRACT FROM AUTHOR]

As one of the most useful methods in electromagnetic interference (EMI) diagnosis, near-field (NF) scanning is widely used in electromagnetic compatibility (EMC) evaluation of complex devices under test (DUTs). In this paper, a two-stage plane adaptive sampling algorithm is proposed to reduce the acquisition time in the process of NF scanning and to make reconstruction of the radiation source more efficient. The sampling method is based on the region self-growth algorithm and the Voronoi subdivision principle, significantly reducing the number of NF samples in the stage of solving the radiation source model through uniform and non-uniform two-stage sampling. Two experiments were conducted to verify the correctness and effectiveness by comparing with the traditional uniform sampling method. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in‐the‐loop; surrogates based on DNNs can effectively emulate diverse non‐scalar data of the types collected in fusion and laboratory‐astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. In this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high‐quality and wide‐ranging surrogate models to be trained using 2–3 times fewer simulations compare to space‐filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work. [ABSTRACT FROM AUTHOR]

Cluster‐based outcome‐dependent sampling (ODS) has the potential to yield efficiency gains when the outcome of interest is relatively rare, and resource constraints allow only a certain number of clusters to be visited for data collection. Previous research has shown that when the intended analysis is inverse‐probability weighted generalized estimating equations, and the number of clusters that can be sampled is fixed, optimal allocation of the (cluster‐level) sample size across strata defined by auxiliary variables readily available at the design stage has the potential to increase efficiency in the estimation of the parameter(s) of interest. In such a setting, the optimal allocation formulae depend on quantities that are unknown in practice, currently making such designs difficult to implement. In this paper, we consider a two‐wave adaptive sampling approach, in which data is collected from a first wave sample, and subsequently used to compute the optimal second wave stratum‐specific sample sizes. We consider two strategies for estimating the necessary components using the first wave data: an inverse‐probability weighting (IPW) approach and a multiple imputation (MI) approach. In a comprehensive simulation study, we show that the adaptive sampling approach performs well, and that the MI approach yields designs that are very near‐optimal, regardless of the covariate type. The IPW approach, on the other hand, has mixed results. Finally, we illustrate the proposed adaptive sampling procedures with data on maternal characteristics and birth outcomes among women enrolled in the Safer Deliveries program in Zanzibar, Tanzania. [ABSTRACT FROM AUTHOR]

High precision geometric measurement of free-form surfaces has become the key to high-performance manufacturing in the manufacturing industry. By designing a reasonable sampling plan, the economic measurement of free-form surfaces can be realized. This paper proposes an adaptive hybrid sampling method for free-form surfaces based on geodesic distance. The free-form surfaces are divided into segments, and the sum of the geodesic distance of each surface segment is taken as the global fluctuation index of free-form surfaces. The number and location of the sampling points for each free-form surface segment are reasonably distributed. Compared with the common methods, this method can significantly reduce the reconstruction error under the same sampling points. This method overcomes the shortcomings of the current commonly used method of taking curvature as the local fluctuation index of free-form surfaces, and provides a new perspective for the adaptive sampling of free-form surfaces. [ABSTRACT FROM AUTHOR]

Distinguishing the subtle differences among fine-grained images from subordinate concepts of a concept hierarchy is a challenging task. In this paper, we propose a Siamese transformer with hierarchical concept embedding (STrHCE), which contains two transformer subnetworks sharing all configurations, and each subnetwork is equipped with the hierarchical semantic information at different concept levels for fine-grained image embeddings. In particular, one subnetwork is for coarse-scale patches to learn the discriminative regions with the aid of the innate multi-head self-attention mechanism of the transformer. The other subnetwork is for finer-scale patches, which are adaptively sampled from the discriminative regions, to capture subtle yet discriminative visual cues and eliminate redundant information. STrHCE connects the two subnetworks through a score margin adjustor to enforce the most discriminative regions generating more confident predictions. Extensive experiments conducted on four commonly-used benchmark datasets, including CUB-200-2011, FGVC-Aircraft, Stanford Dogs, and NABirds, empirically demonstrate the superiority of the proposed STrHCE over state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Over the several decades, approximating functions in infinite dimensions from samples has gained increasing attention in computational science and engineering, especially in computational uncertainty quantification. This is primarily due to the relevance of functions that are solutions to parametric differential equations in various fields, e.g. chemistry, economics, engineering, and physics. While acquiring accurate and reliable approximations of such functions is inherently difficult, current benchmark methods exploit the fact that such functions often belong to certain classes of holomorphic functions to get algebraic convergence rates in infinite dimensions with respect to the number of (potentially adaptive) samples m. Our work focuses on providing theoretical approximation guarantees for the class of so-called (b , ε) -holomorphic functions, demonstrating that these algebraic rates are the best possible for Banach-valued functions in infinite dimensions. We establish lower bounds using a reduction to a discrete problem in combination with the theory of m-widths, Gelfand widths and Kolmogorov widths. We study two cases, known and unknown anisotropy, in which the relative importance of the variables is known and unknown, respectively. A key conclusion of our paper is that in the latter setting, approximation from finite samples is impossible without some inherent ordering of the variables, even if the samples are chosen adaptively. Finally, in both cases, we demonstrate near-optimal, non-adaptive (random) sampling and recovery strategies which achieve close to same rates as the lower bounds. [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive sampling strategy for a multi-vehicle sensor network to explore an underwater biological system known as the pelagic scattering layer, which is a region in the water column with a high density of marine organisms that reflect sound. Ever-changing ocean flow presents a challenge to multi-vehicle coordination in this process. The presence of an ocean flow field may disrupt inter-vehicle spacing so that the group loses communication, strays from a desired formation, or ends up with reduced area of coverage, especially for robotic drifting vehicles whose motion is largely influenced by the ambient flow field. However, those vehicles may also take advantage of the vertical variation of the flow to form a desired cohesive configuration since they can tune their vertical position via depth control. A motion-control algorithm, therefore, is a key element of the adaptive sampling strategy. The paper derives a decentralized coordination algorithm to stabilize a cohesive formation in a two-dimensional flow field with an initial unknown vertical distribution. The algorithm works with a distributed extended Kalman filter that generates a local estimate of the flow from pairwise range measurements between vehicles. Another component of the sampling strategy is the modeling of organism density dynamics using estimation of the density with local optical measurements, such as from onboard cameras. The density evolution is estimated using an ensemble Kalman filter and the results are fed into the motion-coordination algorithm. Numerical simulations illustrate the efficacy of this strategy and motivate ongoing and future efforts to extend the result to a three-dimensional geophysical flow. • A decentralized depth control algorithm guides multiple drifting vehicles to varying depths, ensuring cohesive, collision-free formation in shear flow. • Buoyancy control extends to unknown shear flow using extended Kalman filter for parameter estimation, enabling formation. • Ensemble Kalman filter estimates scattering layer density from depth predictions using operator and local measurements. [ABSTRACT FROM AUTHOR]

A highly automated vehicle (HAV) is a safety-critical system. Therefore, a verification and validation (V&V) process that rigorously evaluates the safety of HAVs is necessary before their release to the market. In this paper, we propose an interaction-aware safety evaluation framework for the HAV and apply it to the roundabout entering scenario. Instead of assuming that the primary other vehicles (POVs) take predetermined maneuvers, we model the POVs as game-theoretic agents. To capture a wide variety of interactions between the POVs and the vehicle under test (VUT), we use level- $k$ game theory and social value orientation (SVO) to characterize the interactive behaviors and train a diverse library of POVs using reinforcement learning. The game-theoretic library, together with initial conditions, form a rich testing space for the two-POV roundabout scenario. On the other hand, we propose an adaptive test case generation scheme based on adaptive sampling, stochastic optimization and upper confidence bound (UCB) algorithm to efficiently generate customized challenging cases for the VUT from the testing space. In simulations, the proposed testing space design captured a wide range of interactive patterns at the roundabout scenario. The proposed test case generation scheme was found to cover the failure modes of the VUT more effectively compared to other test case generation approaches. [ABSTRACT FROM AUTHOR]

Summary: Finite element (FE) model updating is essential to improve the reliability of physical model‐based approaches in structural engineering applications. The surrogate model is considered an alternative to time‐consuming iterative FE analyses in performing the updating procedure. This paper presents a Bayesian neural network (BNN) as the surrogate model for probabilistic FE model updating using the measured modal data. The BNN involves high computational efficiency by introducing the approximate Gaussian inference of the posterior distribution. In practice, the modal data are usually incomplete because of the measurement noise and limited sensors. The developed BNN exploits the nonlinear relationship between the selected parameters and incomplete modal data. As opposed to the traditional surrogate‐based approach, the proposed framework uses the modal data as inputs and structural parameters to be updated as outputs. It enables uncertainty quantification of the estimated structural parameters efficiently. In particular, an adaptive sampling strategy is established to shrink the searching space of optimal updating parameters based on the truncated Gaussian distribution. Numerical examples are conducted to demonstrate the effectiveness of the presented approach. Then it is applied to the laboratory and experimental structures using the measured data. Results indicate that the proposed framework is accurate and efficient for parameter uncertainty quantification in structural model updating. [ABSTRACT FROM AUTHOR]

The real-time requirements imposed on a feedback control system are often hard to be met, as the controller spends a disproportionately large amount of time waiting for a control cycle to reach its final state. When such a final state is established, multiple tasks have to be prioritized and launched altogether simultaneously, and the system is given an extremely short time window to generate its output. This huge gap between the wait and action times, perceived as a load unbalancing problem, hinders a control decision to be made in real time. To address this challenging problem, in this paper, we present a progressive output strategy that divides a control cycle into a few fine-grained control intervals, and the entire workload is scheduled across these control intervals. Dubbed as Progressive Output Strategy (PROS), this approach actively requests intermediate states be created between adjacent control cycles in an adaptive manner. Specifically, as the sensing information is arriving, a system that adopts PROS can generate a series of intermediate solutions that eventually converge to the final optimal control signal. This way, the controller will no longer waste its time idling while waiting for the arrival of all the data for one-shot decision-making. Rather the system actually cuts down the waiting time and is able to act on the intermediate data/states throughout the entire control cycle. Experimental results have confirmed that adopting the PROS in a feedback control loop can evenly distribute the workload over a control cycle, and thus, the time delay is reduced by as much as two orders of magnitude, which is essential to meet the most stringent timing requirements. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive sampling stochastic multigradient algorithm for solving stochastic multiobjective optimization problems. Instead of requiring additional storage or computation of full gradients, the proposed method reduces variance by adaptively controlling the sample size used. Without the convexity assumption on the objective functions, we obtain that the proposed algorithm converges to Pareto stationary points in almost surely. We then analyze the convergence rates of the proposed algorithm. Numerical experiments are presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A wide increase of 3D applications for using mobile phones and other electrical devices reveals the importance of 3D mesh representation. Since visualization and implementation of a coarse and simplified mesh are easier than analyzing a high-resolution mesh, the simplified mesh is preferred for processing. In this paper, a new 3D mesh simplification method is presented to simplify a mesh by anisotropic Nyquist-based adaptive sampling of each segmented region on the surface. Since the sampling step is completed for each segmented region individually, the algorithm can preserve the sharp features of each segment, precisely. The least number of samples is selected from each segment based on its details. Adjusting the sampling procedure according to the geometrical features of the mesh leads to accurately approximate the overall shape of the original model. In order to connect the selected samples, the original mesh connections are employed to better maintain the structure and shape of the input mesh. The improved quality of the results obtained by the proposed method demonstrates its ability in better preserving fine-scale features of different complex meshes in comparison with the previous studies. The simplified models can be efficiently reconstructed based on the selected samples of each region. [ABSTRACT FROM AUTHOR]

Block compressed sensing (BCS) is suitable for image sampling and compression in resource-constrained applications. Adaptive sampling methods can effectively improve the rate-distortion performance of BCS. However, adaptive sampling methods bring high computational complexity to the encoder, which loses the superiority of BCS. In this paper, we focus on improving the adaptive sampling performance at the cost of low computational complexity. Firstly, we analyze the additional computational complexity of the existing adaptive sampling methods for BCS. Secondly, the adaptive sampling problem of BCS is modeled as a distortion minimization problem. We present three distortion models to reveal the relationship between block sampling rate and block distortion and use a simple neural network to predict the model parameters from several measurements. Finally, a fast estimation method is proposed to allocate block sampling rates based on distortion minimization. The results demonstrate that the proposed estimation method of block sampling rates is effective. Two of the three proposed distortion models can make the proposed estimation method have better performance than the existing adaptive sampling methods of BCS. Compared with the calculation of BCS at the sampling rate of 0.1, the additional calculation of the proposed adaptive sampling method is less than 1.9%. [ABSTRACT FROM AUTHOR]