Non-adaptive Approach Research Articles

Cryptovivipary: A rare phenomenon in monoecious species of Ficus L. (Moraceae)

Vivipary in Angiosperms refers to the precocious germination of offspring while they persist on the mother plant. True vivipary and pseudovivipary represent distinctive and adaptive strategies in plants. This phenomenon has never been documented in the genus Ficus (Moraceae family) before. The family Moraceae showcases a remarkable variety of floral structures, methods of pollination, and reproductive mechanisms. The present study is the first to document cryptovivipary in three monoecious fig species. The occurrence of this rare phenomenon could be plants' non-adaptive approach, as well as the influence of the microenvironment surrounding the seed of the species. Based on the observations made in this study, cryptovivipary in these three species appeared to be promoted by high levels of moisture, along with the microenvironment within the syconium that is encountered after seed maturation. Vivipary was documented predominantly during the heavy rainfall period in the monsoon season, spanning from late May to early August 2022, with July being the peak period. Nonetheless, it is important to acknowledge the limitations of this study in that it was not possible to investigate the survival and outcomes of viviparous offspring in comparison to non-viviparous seedlings, primarily due to resource limitations during the experimental period. Subsequent studies on reproductive biology, followed by seed setting and the effect of microenvironment inside syconia could advance our understanding of evolutionary factors and the influence of vivipary on seed production and survival in natural settings.

AdaMO: Adaptive Meta-Optimization for cold-start recommendation

Meta-learning has been proven effective in tackling the cold-start problem of recommendation systems. Most work in this line adopts the meta-optimization idea that learns global knowledge to initialize the base recommender and adapts it for unseen recommendation tasks. Typically, the process is non-adaptive in the sense that both the initialization and model fitting are not task-specific. Such non-adaptive approaches do not work well for the strict cold-start problem, where historical interaction data of the new users is scarce or not available at all. In this paper, we propose a novel adaptive meta-optimization approach, called Adaptive Meta-Optimization (AdaMO), to address the problem. AdaMO is fully adaptive, namely, it simultaneously customizes the initialization and model fitting according to task-specific information. During the cold-start phase, it aggregates task-specific information with transferred knowledge from all historical tasks to tune the initialization of the base model. In the subsequent warm-up phase, AdaMO mines the adaptive transition information from the behavior patterns and parameter status to effectively fit the model using transitional learning. By making a sophisticated balance between shared knowledge and task-specific information, AdaMO outperforms the state-of-the-art methods in various cold-start scenarios.

Greedy versus optimal analysis of bounded size dictionary compression and on-the-fly distributed computing

Scalability and robustness are not an issue when compression is applied for massive data storage, in the context of distributed computing. Speeding up on-the-fly compression for data transmission is more controversial. In such case, a compression technique merging together an adaptive and a non-adaptive approach has to be considered. A practical implementation of LZW (Lempel, Ziv and Welch) compression, called LZC (C indicates the Unix command ’compress’), has this characteristic. The non-adaptive phases work with bounded size prefix dictionaries built by LZW factorizations during the adaptive ones. Both phases employ the greedy approach. We show a worst case analysis of the greedy approach with respect to the optimal solution decodable by the LZC decompressor and give worst case examples for which the solution cost produced by the distributed implementations reaches the Θ(d) theoretical upper bound to the optimal cost approximation factor, where d is the dictionary size. We prove that for the worst case examples of the totally adaptive version of LZW compression the approximation factor is Θ(d). Such analysis suggests that parallelization of on the fly compression is not suitable for highly disseminated data since the non-adaptive phases are too far from optimal. In order to improve the compression effectiveness, we suggest to apply LZMW (Lempel, Ziv, Miller and Wegman) factorization to LZC compression (LZCMW) during the adaptive phases. Such factorization was originally thought with a dictionary bounded by the least recently used strategy. We introduce LZCMW in order to have non-adaptive phases.

Strong form mesh-free hp-adaptive solution of linear elasticity problem

We present an algorithm for hp-adaptive collocation-based mesh-free numerical analysis of partial differential equations. Our solution procedure follows a well-established iterative solve–estimate–mark–refine paradigm. The solve phase relies on the Radial Basis Function-generated Finite Differences (RBF-FD) using point clouds generated by advancing front node positioning algorithm that supports variable node density. In the estimate phase, we introduce an Implicit-Explicit (IMEX) error indicator, which assumes that the error relates to the difference between the implicitly obtained solution (from the solve phase) and a local explicit re-evaluation of the PDE at hand using a higher order approximation. Based on the IMEX error indicator, the modified Texas Three Step marking strategy is used to mark the computational nodes for h-, p- or hp-(de-)refinement. Finally, in the refine phase, nodes are repositioned and the order of the method is locally redefined using the variable order of the augmenting monomials according to the instructions from the mark phase. The performance of the introduced hp-adaptive method is first investigated on a two-dimensional Peak problem and further applied to two- and three-dimensional contact problems. We show that the proposed IMEX error indicator adequately captures the global behaviour of the error in all cases considered and that the proposed hp-adaptive solution procedure significantly outperforms the non-adaptive approach. The proposed hp-adaptive method stands for another important step towards a fully autonomous numerical method capable of solving complex problems in realistic geometries without the need for user intervention.

A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics

Dynamic models of structural and mechanical systems can be updated to match the measured data through a Bayesian inference process. However, the performance of classical (non-adaptive) Bayesian model updating approaches decreases significantly when the pre-assumed statistical characteristics of the model prediction error are violated. To overcome this issue, this paper presents an adaptive recursive variational Bayesian approach to estimate the statistical characteristics of the prediction error jointly with the unknown model parameters. This approach improves the accuracy and robustness of model updating by including the estimation of model prediction error. The performance of this approach is demonstrated using numerically simulated data obtained from a structural frame with material non-linearity under earthquake excitation. Results show that in the presence of non-stationary noise/error, the non-adaptive approach fails to estimate unknown model parameters, whereas the proposed approach can accurately estimate them.

A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks.

We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial network structure, i.e., non-adaptive approach, and performing partial node rank recalculation after node removal, i.e., semi-adaptive approach. To quantify the efficacy of each vaccination strategy, we used three epidemic spread indicators: the size of the largest connected component, the total number of infected at the end of the epidemic, and the maximum number of simultaneously infected individuals. We show that the best vaccination strategies in the non-adaptive and semi-adaptive approaches are different and that the best strategy also depends on the number of available vaccines. Furthermore, a partial recalculation of the node centrality increases the efficacy of the vaccination strategies by up to 80%.

An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19

This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIMA) and the use of machine learning techniques, such as artificial neural networks, are the most covered in the literature review. All these approaches evaluate the prediction error when comparing the generated models with the data that fed the model, but they do not compare these values with the actual data of electricity demand once these are obtained, in addition, these techniques present high error values when there are unexpected changes in the trend of electricity consumption. This work proposes a methodology to generate an adaptive model for unexpected changes in electricity demand through the use of optimization in conjunction with SARIMA time series. The proposed case study is the electricity consumption in Quito, Ecuador to predict the electricity demand in the years 2019 and 2020, which are particularly challenging due to atypical electricity consumption attributed to COVID-19. The results show that the proposed model is capable of following the trend of electricity demand, adapting itself to sudden changes and obtaining an average error of 2.5% which is lower than the average error of 5.43% when using a non-adaptive approach (more than 50% or error improvement).

Clinical Outcomes of Prostate SBRT Using Non-Adaptive MR-Guided Radiotherapy (MRgRT)

<h3>Purpose/Objective(s)</h3> Stereotactic body radiation therapy (SBRT) is widely used for localized prostate cancer and implementation of MR-guided radiotherapy (MRgRT) has the advantage of smaller target margins and improved sparing of organs at risk. Here we evaluate outcomes and time required to treat using non-adaptive MRgRT for prostate SBRT at our institution. <h3>Materials/Methods</h3> From 9/2019 to 10/2021, 77 consecutive patients were treated with MRgRT prostate SBRT. Patients included low (4%), favorable intermediate (40%), unfavorable intermediate (49%) and high risk (7%). Short-term ADT was used in 32% of patients. Our clinical pathway allowed maximum prostate gland volume of 60cc. Target volumes included prostate gland and proximal seminal vesicles with an isotropic 3mm margin. Treatment was prescribed to 36.25 Gy in 5 fractions every other day with urethral sparing to maximum dose of 35 Gy. SpaceOAR gel was used in 17% of patients Time on the Linac was recorded as beam on time (BOT) plus total treatment time (TTT) including gating. Analyzed outcomes included PSA response and patient reported outcomes scored by the American Urological Association (AUA) questionnaire and toxicity per CTCAE v5. Repeated-measure analysis and Bonferroni correction were used to test changes in PSA and toxicity. <h3>Results</h3> Median follow up was 9.5 months (range 1.1 – 23.1). Median pre-treatment PSA was 6.5 (range 2.2 – 19.6) and median AUA score was 6 (1 – 25) with a median prostate size of 32.6 cc (range 17 – 70). At first follow up, median change in PSA was -5.23 ng/mL (Delta range +0.02– -19.3ng/ml) with a median AUA of 10 (range 1 – 26). At second follow up, median change in PSA was -6.33 ng/mL (range +1.48– -19.31ng/mL) and median AUA of 7 (range 0 – 33). At third follow up, median change in PSA was -6.09ng/mL (range -1.84 – -14.9ng/mL) and median AUA was 6.5 (1 – 25). There was a significant decrease in PSA between pre-treatment and first through fourth follow up (p=0.003). The greatest difference was between pretreatment PSA and PSA at 4^thfollow-up (decrease in PSA by 5.9 ng/ml, p<.001). Toxicity as measured by CTCAE v5 decreased from 53% at first follow up to 23% at second follow up and 1% at third follow up. Common toxicities included grade 2 urethritis, tenesmus and one grade 3 proctitis. No grade ≥4 toxicity was seen. Our MR-Linac underwent an upgrade in 11/2020 allowing for faster treatment times. For patients treated prior to the upgrade, mean BOT was 4.89 min with a TTT of 12.50 min whereas for those treated after mean BOT was 4.52 min with a TTT of 10.68 min. <h3>Conclusion</h3> A benefit of MRgRT is the potential for treatment adaptation, however it comes at the cost of increased resource utilization and reduced clinical efficiency. Our experience with non-adaptive MRgRT prostate SBRT maintains clinical efficiency as well as efficacy. Taken together these results suggest a non-adaptive approach may allow more patients to access an effective and safe treatment, while also maximizing resource utilization.

Adaptive Sequential Experiments with Unknown Information Arrival Processes

Problem definition: Sequential experiments that are deployed in a broad range of practices are characterized by an exploration-exploitation trade-off that is well understood when in each time period feedback is received only on the action that was selected in that period. However, in many practical settings, additional information may become available between decision epochs. We study the performance that one may achieve when leveraging such auxiliary information and the design of algorithms that effectively do so without prior knowledge of the information arrival process. Methodology/results: Our formulation considers a broad class of distributions that are informative about rewards from actions and allows auxiliary observations from these distributions to arrive according to an arbitrary and a priori unknown process. When it is known how to map auxiliary observations to reward estimates, we characterize the best achievable performance as a function of the information arrival process. In terms of achieving optimal performance, we establish that upper confidence bound and Thompson sampling algorithms possess natural robustness with respect to the information arrival process, which uncovers a novel property of these popular algorithms. When the mappings connecting auxiliary observations and rewards are a priori unknown, we characterize a necessary and sufficient condition under which auxiliary information allows performance improvement and devise an adaptive policy (termed 2UCBs) that guarantees near optimality. We use a data set from a large media site to analyze the value that may be captured by leveraging auxiliary observations in the design of content recommendations. Managerial implications: Our study highlights the importance of utilizing auxiliary information in the design of sequential experiments and characterizes how salient features of the auxiliary information stream impact performance. Our study also emphasizes the risk in processing auxiliary information using nonadaptive approaches that are predicated on correct interpretation of this information, as opposed to deploying flexible, adaptive methods.

Online adaptive radiotherapy of urinary bladder cancer with full re-optimization to the anatomy of the day: Initial experience and dosimetric benefits

Background and purposeOnline adaptive radiotherapy (oART) potentially reduces the dose to organs at risk (OARs) as the planning target volume (PTV) margins are reduced compared to a non-adaptive approach (non-ART). This study evaluates the feasibility and dosimetric impact of cone-beam computed tomography (CBCT)-guided oART of urinary bladder cancer for the first patients treated, using patient-specific margins. Materials and methodsSixteen consecutive patients with muscle-invasive bladder cancer received two or more (median = 23) fractions as oART, and remaining fractions as non-ART. The non-ART fractions were delivered with standard population-based margins, while reduced patient-specific margins based on intra-fractional variations extracted from 2-4 fractions were applied to the primary PTV (PTV-T) during the oART fractions. Target volume and coverage, and dose to OARs were compared between non-ART and oART plans, and the oART procedure time was recorded. ResultsIn total, 297/512 fractions were delivered as oART with full re-optimization to the anatomy of the day. The median (interquartile range, IQR) oART procedure time, measured from the end of CBCT generation to completion of plan review, and quality assurance was 13.9 (11.9;16.6) min. The median (IQR) volume reduction in PTV-T volume was 33.9 (24.2;45.0)%, comparing oART and non-ART plans, resulting in median (IQR) reductions in bowel bag V45Gy of 18.8 (12.7;27.9)% and rectum V50Gy of 70.7 (35.9;94.8)%. By re-optimizing the plan to the daily anatomy, full target coverage was achieved at all oART fractions. ConclusionsoART resulted in large reductions in treatment volumes and doses to OARs, compared to non-ART, while ensuring target coverage. This indicates potential reductions in gastrointestinal toxicity.

Adaptive quadrature rules for Galerkin BEM

The singular integrals in the Galerkin Boundary Element Method are usually treated using singularity removing transformations. The regularized integrals are approximated by tensor product Gaussian quadrature rules. Although these integrals are smooth, the convergence rate depends strongly on the type of singularity and the aspect ratio of the triangulation. Here an adaptive quadrature scheme is presented that ensures a convergence at a user-specified rate. The effectiveness of the resulting quadrature scheme is compared with the non-adaptive approach.

Structural Reliability Methods Applied in Analysis of Steel Elements Subjected to Fire

The accuracy and efficiency of an approach using surrogate models are investigated, in comparison with traditional structural reliability methods, in the analysis of two steel structures subjected to natural fire. For this purpose, an algorithm is used, coupled to a finite-element computational package, which includes the first-order reliability method (FORM), the approximate method most used in this type of problem, and Monte Carlo simulation (MCS), which is usually taken as a reference. On the other hand, the literature indicates surrogate models as a promising alternative to maintain, at acceptable levels, the computational cost in similar analyses. Therefore, two approaches using surrogate models are also applied to the algorithm, more specifically adaptive and nonadaptive artificial neural networks. The analyses led to differences of up to 21.83% between the failure probabilities obtained via FORM and via MCS. Furthermore, while the nonadaptive approach fails to achieve sufficient accuracy, the adaptive approach is confirmed to be a viable alternative, with differences, on average, close to 1.00% and computational times less than 1.60% of the time required by MCS.

Detecting Epileptic Seizures Based on Parameter Estimation of a Neural Mass Model

This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where, in addition to nonlinear model parameters, the covariance matrix of measurement noise is estimated recursively in a near online manner. Two adaptive KF approaches are formulated based on the forgetting factor and the moving window covariance-matching techniques using residuals. Although the proposed adaptive methods are integrated with the unscented KF (UKF) for nonlinear model updating in this paper, they can be alternatively combined with other types of nonlinear KFs such as the extended KF (EKF) or the ensemble KF (EnKF). The performance of the proposed methods is investigated through two numerical applications and compared to that of a non-adaptive UKF and an existing dual adaptive filter. The first application considers a nonlinear steel pier where nonlinear material properties are selected as updating parameters. Significant improvements in parameter estimation results are observed when using adaptive filters compared to the non-adaptive approach. Furthermore, the covariance matrix of simulated measurement noise is estimated from the adaptive approaches with acceptable accuracy. Effects of different types of modeling errors are studied in the second numerical application of a nonlinear 3-story 3-bay steel frame structure. Similarly, more accurate and robust parameter estimations and response predictions are obtained from the adaptive approaches compared to the non-adaptive approach. The results verify the effectiveness and robustness of the proposed adaptive filters. The forgetting factor and moving window methods are shown to have a simpler tuning process compared to the dual adaptive method while providing similar performance.

Use of the Cross-Spectral Density Matrix for Enhanced Passive Ultrasound Imaging of Cavitation.

Passive ultrasound imaging is of great interest for cavitation monitoring. Spatiotemporal monitoring of cavitation bubbles in therapeutic applications is possible using an ultrasound imaging probe to passively receive the acoustic signals from the bubbles. Fourier-domain (FD) beamformers have been proposed to process the signals received into maps of the spatial localization of cavitation activity, with reduced computing times with respect to the time-domain approach, and to take advantage of frequency selectivity for cavitation regime characterization. The approaches proposed have been mainly nonadaptive, and these have suffered from low resolution and contrast, due to the many reconstruction artifacts. Inspired by the array-processing literature and in the context of passive ultrasound imaging of cavitation, we propose here a robust estimation of the second-order statistics of data through spatial covariance matrices in the FD or cross-spectral density matrices (CSMs). The benefits of such formalism are illustrated using advanced reconstruction algorithms, such as the robust Capon beamformer, the Pisarenko class beamformer, and the multiple signal classification approach. Through both simulations and experiments in a water tank, we demonstrate that enhanced localization of cavitation activity (i.e., improved resolution and contrast with respect to nonadaptive approaches) is compatible with the rapid and frequency-selective approaches of the FD. Robust estimation of the CSM and the derived adaptive beamformers paves the way to the development of powerful passive ultrasound imaging tools.

Real-world embodied AI through a morphologically adaptive quadruped robot

Robots are traditionally bound by a fixed morphology during their operational lifetime, which is limited to adapting only their control strategies. Here we present the first quadrupedal robot that can morphologically adapt to different environmental conditions in outdoor, unstructured environments. Our solution is rooted in embodied AI and comprises two components: (1) a robot that permits in situ morphological adaptation and (2) an adaptation algorithm that transitions between the most energy-efficient morphologies on the basis of the currently sensed terrain. We first build a model that describes how the robot morphology affects performance on selected terrains. We then test continuous adaptation on realistic outdoor terrain while allowing the robot to constantly update its model. We show that the robot exploits its training to effectively transition between different morphological configurations, exhibiting substantial performance improvements over a non-adaptive approach. The demonstrated benefits of real-world morphological adaptation demonstrate the potential for a new embodied way of incorporating adaptation into future robotic designs.

Illumination Adaptation in a Multi-Wavelength Opto-Electronic Patch Sensor.

In capturing high-quality photoplethysmographic signals, it is crucial to ensure that appropriate illumination intensities are used. The purpose of the study was to deliver controlled illumination intensities for a multi-wavelength opto-electronic patch sensor that has four separate arrays each consisting of four light-emitting diodes (LEDs), the wavelength of the light generated by each array being different. The study achieved the following: (1) a linear constant current source LED driver incorporating series negative feedback using an integrated operational amplifier circuit; (2) the fitting of a linear regression equation to provide rapid determination of the LEDs driver voltage; and (3) an algorithm for the automatic adjustment of the output voltage to ensure suitable LED illumination. The data from a single centrally-located photo detector, which is capable of capturing all four channels of back-light in a time-multiplexed manner, were used to monitor heart rate and blood oxygen saturation. This paper provides circuitry for driving the LEDs and describes an adaptive algorithm implemented on a microcontroller unit that monitors the quality of the photo detector signals received in order to control each of the individual currents being supplied to the LED arrays. The study demonstrated that the operation of the new circuitry in its ability to adapt LED illumination to the strength of the signal received and the performance of the adaptive system was compared with that of a non-adaptive approach.

Adaptive Kalman filters for nonlinear finite element model updating

This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where, in addition to nonlinear model parameters, the covariance matrix of measurement noise is estimated recursively in a near online manner. Two adaptive KF approaches are formulated based on the forgetting factor and the moving window covariance-matching techniques using residuals. Although the proposed adaptive methods are integrated with the unscented KF (UKF) for nonlinear model updating in this paper, they can be alternatively combined with other types of nonlinear KFs such as the extended KF (EKF) or the ensemble KF (EnKF). The performance of the proposed methods is investigated through two numerical applications and compared to that of a non-adaptive UKF and an existing dual adaptive filter. The first application considers a nonlinear steel pier where nonlinear material properties are selected as updating parameters. Significant improvements in parameter estimation results are observed when using adaptive filters compared to the non-adaptive approach. Furthermore, the covariance matrix of simulated measurement noise is estimated from the adaptive approaches with acceptable accuracy. Effects of different types of modeling errors are studied in the second numerical application of a nonlinear 3-story 3-bay steel frame structure. Similarly, more accurate and robust parameter estimations and response predictions are obtained from the adaptive approaches compared to the non-adaptive approach. The results verify the effectiveness and robustness of the proposed adaptive filters. The forgetting factor and moving window methods are shown to have a simpler tuning process compared to the dual adaptive method while providing similar performance.

Visual Regulation of Differential-Drive Mobile Robots: A Nonadaptive Switching Approach

This article addresses the visual regulation problem of a differential-drive mobile robot with an arbitrarily installed monocular camera in the indoor environment. A three-stage controller is designed by using a novel nonadaptive switching approach, where the unknown image depth and the uncalibrated camera-to-robot translation parameters do not need to be estimated. Convergence of the error systems with the designed controller in each stage is analyzed. Moreover, the existence of the switching time instants from each stage is proved. The simulation results are presented to show the effectiveness of the proposed approach.

Electrocardiogram compression using the nonlinear iterative partial least squares algorithm: a comparison between adaptive and non-adaptive approach

Data compression is applicable in reducing amount of data to be stored and it can be applied in several data collecting processes, being generated by lossy or lossless compression algorithms. Due to its large amount of data, the use of compression is desirable in ECG signals. In this work, we present the accepted nonlinear iterative partial least squares (NIPALS) method as an option to ECG compression method, as recommended by Nicolosi (1999). In addition, we compare the results based in an adaptive and non-adaptive version of this method, by using the MIT arrhythmia database. As a help to obtain a better comparison, we have developed an abnormality indicator related to possible abnormalities in the waveform and a decision method that helps to choose between adaptive or non-adaptive approach. Results showed that the adaptive approach is better than the non-adaptive approach, for the NIPALS compression algorithm.

Electrocardiogram compression using the nonlinear iterative partial least squares algorithm: a comparison between adaptive and non-adaptive approach

Data compression is applicable in reducing amount of data to be stored and it can be applied in several data collecting processes, being generated by lossy or lossless compression algorithms. Due to its large amount of data, the use of compression is desirable in ECG signals. In this work, we present the accepted nonlinear iterative partial least squares (NIPALS) method as an option to ECG compression method, as recommended by Nicolosi (1999). In addition, we compare the results based in an adaptive and non-adaptive version of this method, by using the MIT arrhythmia database. As a help to obtain a better comparison, we have developed an abnormality indicator related to possible abnormalities in the waveform and a decision method that helps to choose between adaptive or non-adaptive approach. Results showed that the adaptive approach is better than the non-adaptive approach, for the NIPALS compression algorithm.