Initial Iteration Research Articles

Iterative phase retrieval with a sensor mask.

As an important inverse imaging problem in diffraction optics, Fourier phase retrieval aims at estimating the latent image of the target object only from the magnitude of its Fourier measurement. Although in real applications alternating methods are widely-used for Fourier phase retrieval considering the constraints in the object and Fourier domains, they need a lot of initial guesses and iterations to achieve reasonable results. In this paper, we show that a proper sensor mask directly attached to the Fourier magnitude can improve the efficiency of the iterative phase retrieval algorithms, such as alternating direction method of multipliers (ADMM). Furthermore, we refer to the learning-based method to determine the sensor mask according to the Fourier measurement, and unrolled ADMM is used for phase retrieval. Numerical results show that our method outperforms other existing methods for the Fourier phase retrieval problem.

Emergency contracting and the delivery of elective care services across the English National Health Service and independent sector during COVID-19: a descriptive analysis

BackgroundFollowing a virtual standstill in the delivery of elective procedures in England, a national block contract between the NHS and the independent sector aimed to help restart surgical care. This...

FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations

Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and the previous studies to originate from the frequency-level artifacts in generated images. We find that ignoring the frequency-level artifacts can improve the detector's generalization across various GAN models, but it can reduce the model's performance for the trained GAN models. Thus, we design a framework to generalize the deepfake detector for both the known and unseen GAN models. Our framework generates the frequency-level perturbation maps to make the generated images indistinguishable from the real images. By updating the deepfake detector along with the training of the perturbation generator, our model is trained to detect the frequency-level artifacts at the initial iterations and consider the image-level irregularities at the last iterations. For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories. Numerous experiments validate the state-of-the-art performance of our deepfake detector.

Monotone iterations method for fractional diffusion equations

In recent years, there has been a growing interest on non-localmodels because of their relevance in many practical applications. Awidely studied class of non-local models involves fractional orderoperators. They usually describe anomalous diffusion. Inparticular, these equations provide a more faithful representationof the long-memory and nonlocal dependence of diffusion in fractaland porous media, heat flow in media with memory, dynamics ofprotein in cells etc.
 For $a\in (0, 1)$, we investigate the nonautonomous fractionaldiffusion equation:
 $D^a_{*,t} u - Au = f(x, t,u),$
 where$D^a_{*,t}$ is the Caputo fractional derivative and $A$ is auniformly elliptic operator with smooth coefficients depending onspace and time. We consider these equations together with initialand quasilinear boundary conditions.
 The solvability of such problems in H\"older spaces presupposesrigid restrictions on the given initial data. These compatibilityconditions have no physical meaning and, therefore, they can beavoided, if the solution is sought in larger spaces, for instance inweighted H\"older spaces.
 We give general existence and uniqueness result andprovide some examples of applications of the main theorem. The maintool is the monotone iterations method. Preliminary we developed thelinear theory with existence and comparison results. The principleuse of the positivity lemma is the construction of a monotonesequences for our problem. Initial iteration may be taken as eitheran upper solution or a lower solution. We provide some examples ofupper and lower solution for the case of linear equations andquasilinear boundary conditions. We notice that this approach canalso be extended to other problems and systems of fractionalequations as soon as we will be able to construct appropriate upperand lower solutions.

Creating and adapting an infection management care pathway in pediatric oncology.

While care pathways based upon clinical practice guidelines (CPGs) are important, little is known about optimal approaches to development and adaptation in pediatric oncology. Objectives were to develop care pathway templates for pediatric cancer supportive care that are based upon CPGs and to adapt an infection management care pathway for use at a single institution. Study phases were as follows: (1) creation of care pathway templates across multiple supportive care topics; (2) refinement of the infection management care pathway template by interviewing pediatric oncology clinicians at a single institution; and (3) adaptation of the infection management care pathway template for use at a different institution. Informed by seven CPGs, an initial iteration of the infection management care pathway template was created. This template was then refined based upon 20 interviews with pediatric oncology clinicians. Adaptation of the infection management care pathway template for use at a different institution required many changes to improve its clinical usability. Specificity and additional information not considered by the source CPGs were incorporated. We developed a process to create care pathway templates across multiple supportive care topics in pediatric oncology and to refine and adapt the infection management care pathway. While we found that the process was feasible, we also identified the need to substantially modify the care pathway during the adaptation process to consider scenarios not addressed by the source CPGs. Future work should measure implementation success.

Collaborative faculty development transforms evaluation at a school of osteopathic medicine: an exploratory grounded theory study

Background: Faculty development (FD) initiatives for medical educators must keep pace with educators’ expanding roles and responsibilities in the 21st century to effectively support and guide professional growth. Successful initiatives will be comprehensive and systematic, rather than episodic. Our research explores the impact of a collaborative, individualized, and focused FD program. The purpose of this pilot study is: (1) to describe the innovative design and implementation of the incipient FD program at University of the Incarnate Word School of Osteopathic Medicine (UIWSOM), San Antonio, Texas; and (2) to present insights from a preliminary process evaluation of the program’s initial launch to inform and facilitate broadscale implementation. Methods: We used a longitudinal, holistic approach to redesign the UIWSOM FD program to provide evidence-informed and experiential learning for faculty. We performed a process evaluation of the initial iteration of the FD program using an inductive qualitative research approach. We applied principles of constructivist grounded theory to analyze faculty’s responses collected during semi-structured interviews. Results: Three themes emerged from our analysis: communication, advocacy, and reciprocal learning. We found that effective communication, advocacy for faculty success, and reciprocal value between faculty and program developers undergirded the core concept of authentic engagement. Faculty’s perceptions of the quality of engagement of those implementing the program overshadowed the quality of the logistics. Conclusions: Our pilot study identified authentic engagement as critical to faculty’s positive experience of this new FD initiative. Practical implications for other health professions schools with similar FD initiatives include consideration of the relational aspects. Future studies should expand the process evaluation to determine key factors driving perceived program success for other skill domains and amongst clinical faculty, and include a long-range outcome evaluation of the fully implemented program.

Using organisational role theory and job analysis to identify and define the roles of an effective Australian general practice supervisor

ABSTRACT General practice (GP) supervisors – the key resource for training the future GP workforce – are often described as ‘occupying a role’ or enacting a series of roles. However, as much of the discourse uses a lay understanding of role, or merely hints at theory, a significant body of theoretical literature is underutilised. We reasoned that a more rigorous application of role theory might provide a conceptually clearer account of the GP-supervisor’s job. To this end, we describe the use of organisational role theory and job analysis to identify and define the roles of the Australian GP-supervisor. Our search of the academic literature identified 64 role titles, which we condensed to an initial iteration of core roles, using inclusion and exclusion criteria. We analysed GP training organisations’ documents to map embedded supervisory tasks against our list of roles to verify their presence, review our role titles and definitions, and look for additional roles that we may have missed. We used subject matter experts iteratively, to authenticate a final list of ten roles and their accompanying definitions, which can be used to support Australian GP-supervisors to perform effectively. We encourage those who support GP-supervisors in other countries and those who educate other health professionals in primary care settings to review the roles to judge whether they are transferable to their contexts.

With Clear Intention—An Ethical Responsibility Model for Robot Governance

There is much discussion about super artificial intelligence (AI) and autonomous machine learning (ML) systems, or learning machines (LM). Yet, the reality of thinking robotics still seems far on the horizon. It is one thing to define AI in light of human intelligence, citing the remoteness between ML and human intelligence, but another to understand issues of ethics, responsibility, and accountability in relation to the behavior of autonomous robotic systems within a human society. Due to the apparent gap between a society in which autonomous robots are a reality and present-day reality, many of the efforts placed on establishing robotic governance, and indeed, robot law fall outside the fields of valid scientific research. Work within this area has concentrated on manifestos, special interest groups and popular culture. This article takes a cognitive scientific perspective toward characterizing the nature of what true LMs would entail—i.e., intentionality and consciousness. It then proposes the Ethical Responsibility Model for Robot Governance (ER-RoboGov) as an initial platform or first iteration of a model for robot governance that takes the standpoint of LMs being conscious entities. The article utilizes past AI governance model research to map out the key factors of governance from the perspective of autonomous machine learning systems.

Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer

Most CycleGAN domain transfer architectures require a large amount of data belonging to domains on which the domain transfer task is to be applied. Nevertheless, in many real-world applications one of the domains is reduced, i.e., scarce. This means that it has much less training data available in comparison to the other domain, which is fully observable. In order to tackle the problem of using CycleGAN framework in such unfavorable application scenarios, we propose and invoke a novel Bootstrapped SSL CycleGAN architecture (BTS-SSL), where the mentioned problem is overcome using two strategies. Firstly, by using a relatively small percentage of available labelled training data from the reduced or scarce domain and a Semi-Supervised Learning (SSL) approach, we prevent overfitting of the discriminator belonging to the reduced domain, which would otherwise occur during initial training iterations due to the small amount of available training data in the scarce domain. Secondly, after initial learning guided by the described SSL strategy, additional bootstrapping (BTS) of the reduced data domain is performed by inserting artifically generated training examples into the training poll of the data discriminator belonging to the scarce domain. Bootstrapped samples are generated by the already trained neural network that performs transferring from the fully observable to the scarce domain. The described procedure is periodically repeated during the training process several times and results in significantly improved performance of the final model in comparison to the original unsupervised CycleGAN approach. The same also holds in comparison to the solutions that are exclusively based either on the described SSL, or on the bootstrapping strategy, i.e., when these are applied separately. Moreover, in the considered scarce scenarios it also shows competitive results in comparison to the fully supervised solution based on the pix2pix method. In that sense, it is directly applicable to many domain transfer tasks that are relying on the CycleGAN architecture.

Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling.

The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.

A hybrid Grasshopper Optimization Algorithm and Harris Hawks Optimizer for Combined Heat and Power Economic Dispatch problem

The Combined Heat and Power Economic Dispatch (CHPED) is a real-world optimization problem with several complex constraints that has been a topic of studies around energy systems and optimization processes. This paper attempts to conceptualize a potent algorithm by combining the Modified Grasshopper Optimization Algorithm (MGOA) and the Improved Harris Hawks Optimizer (IHHO) for attaining a better balance between the beginning stages of global search and the latter stages of global convergence. The proposed attempt is abbreviated as MGOA-IHHO. Firstly, the chaotic and Opposition-Based Learning (OBL) methods are invoked to generate the initial population. Second, the mathematical model of the conventional Grasshopper Optimization Algorithm (GOA) is modified using Sine–Cosine Acceleration Coefficients (SCAC) to simulate the global exploration at the initial iterations and graduating to the global convergence at the final stages of optimization. Hence, it is named MGOA. Finally, the adaptive search mechanism integrates the two improved search phases of HHO with a search phase of MGOA to improve the performance of the proposed optimization method. This mechanism investigates the best solution for the aging level of the individual during the optimal evaluation process for choosing an appropriate search phase in MGOA-IHHO. The intended effect of the proposed MGOA-IHHO method is verified with other nature-inspired methods on standard single-objective test functions including 23 benchmark problems, 30 test suits of IEEE Congress on Evolutionary Computation 2017 (CEC2017), and four CHPED problems. The statistical results ascertain that the proposed hybridized MGOA-IHHO is capable of providing promising results when compared with its variants and optimization algorithms introduced in the literature.

Imaging through scattering layers by exploiting the sum of gradient amplitude guided phase retrieval

One way to restore scattered optical imaging is retrieving its phase through the speckle autocorrelation. However, the commonly used methods have the disadvantages such as poor recovery quality, the need to select the initial value multiple times and insufficient noise immunity. A method named imaging through scattering layers by exploiting the sum of gradient amplitude guided phase retrieval is proposed in this paper. Sparsity enhancement operation is added to the solution obtained in each iteration and a value called the sum of gradient amplitudes is used to control the number of iterations. This method can effectively improve the quality of restored images. It can also effectively recover the object with any initial iteration value and have better noise resistance by a specific designed parameter in the iteration.

Metaheuristic method for searching quasi-optimal route based on the ant algorithm and annealing simulation

Today, for intelligent computer systems of general and special purpose, the task of finding the optimal route is actual. Currently, there is a problem of lack of efficiency of methods for finding the quasi-optimal route. The object of the research is the process of solving optimization problems of finding a route. The subject of the research is a method for finding a quasi-optimal route based on metaheuristics. The current work increases the efficiency of searching for a quasi-optimal route using a metaheuristic method based on the ant algorithm. To achieve this goal, the work was created a method based on the ant algorithm and simulated annealing for the traveling salesman problem, was formulated the problem of the shortest path in the world of tiles, was developed a method based on the ant algorithm and simulated annealing for the problem of the shortest path in the world of tiles. Advantages of the proposed methods include the following. First, for calculating the probability of an ant moving from the current vertex to other vertices at the initial iterations, the random pheromone level plays the main role, which makes it possible to implement a random search, and at the final iterations, the normalized previous pheromone levelplays the main role, which makes it possible to implement directed search. This is ensured by the use of simulated annealing and increases the accuracy of finding a quasi-optimal route. Second, for calculating the change in the pheromone level at the initial iterations, the pheromone increment plays the main role, which ensures the breadth of the search, and at the final iterations, the previous pheromone level plays the main role, which ensures the convergence of the method. This is ensured by the use of simulated annealing and increases the accuracy of finding a quasi-optimal route. Third, the modification of the ant algorithm by calculating the length of the edges based on the Chebyshev distance, placing all ants in the initial vertex, checking for a dead-end, checking that the target vertex has been reached, and using Moore's neighborhood allows solving problems of the shortest path in the world of tiles. The performed numerical study made it possible to evaluate both methods (for the first method, the root-mean-square error was 0.04, and for the second method it was 0.03). The proposed methods make it possible to expand the area of application of metaheuristics based on the ant algorithm, which is confirmed by its adaptation for the specified optimization problems and contributes to an increase in the efficiency of intelligent computer systems for general and special purposes. The prospects for future research are the study of the proposed methods for a wide class of artificial intelligence problems.

Solution of nonlinear eigenvalues for the viscoelastic damped cylindrical shell considering the frequency dependence of viscoelastic materials

The dynamic model of the cylindrical shell partially attached with a viscoelastic free damping layer is established by a semi-analytical method, and the complex boundary conditions of the cylindrical shell are simulated by using the non-uniform distribution of spring groups. Two new methods for solving nonlinear eigenvalue problems of the viscoelastic damped cylindrical shell are proposed. One method is named as the Vector Iteration method based on Approximate Eigenvalues (VIAE), and the solution process can be described as: the iteration equation is obtained by the characteristic equation; the approximate eigenvalue and the initial eigenvector are used for the iteration calculation; in each iteration, the eigenvectors are normalized and the nonlinear eigenvalues are obtained by using the Rayleigh quotient. Another method is named as the Secant Method of Characteristic Polynomials (SMCP). In this method, the iteration equation of the eigenvalue is derived by the secant method. Furthermore, the approximate eigenvalues of the system are used as the initial iteration eigenvalues and the nonlinear eigenvalues of the system can be approximated through several iteration calculations. Finally, the proposed modeling method and algorithm are verified by the experiment of the actual cylindrical shell attached the ZN-1 viscoelastic material at the free end. The two new methods were also compared with the classical Eigenvector Increment Method (EIM). The comparison results show that the VIAE algorithm has the highest efficiency, and both the VIAE and SMCP algorithms have wider applicability because they do not need to perform eigenvector calculations.

What does it mean for an evaluation to be ‘valid’? A critical synthesis of evaluation literature

Validity is both a critical foundation for, and widely contested concept in the field of program evaluation. Given the wide and divergent treatment of the term, this article sought to enhance the field’s understanding and application of validity by systematically identifying, collating and integrating existing conceptions of validity. A critical interpretive synthesis approach was taken to combine the best elements of systematic literature searching with integrative review. We found that conceptions of validity could be roughly collated into three categories pertaining to the evaluation research, evaluative judgement and reporting, and beyond the evaluation proper. The result was a wide-ranging map of validity concepts against the various stages of the evaluation process. This made clear several gaps in the validity thinking, resulting in the development of comprehensive but not yet complete framework that weaves together related aspects identified into a cohesive whole. It is our hope that this initial iteration of a cohesive validity framework will be the starting point for a cooperative effort to improve professional evaluation practice.

An sEMG based adaptive method for human-exoskeleton collaboration in variable walking environments

In this paper, a novel long short memory network – adaptive robust iterative learning control (LSTM-ARILC) framework is proposed to achieve accurate continuous motion estimation and adaptive following control for the exoskeleton robot, which is applied to offer assistance to human walking in variable environments. The LSTM network is established to estimate the human lower limb motion through the processed surface Electromyography (sEMG) signals, and the human-exoskeleton coupling fuzzy dynamic model is further developed. Then the closed-loop ARILC controller is designed to compensate the estimated errors and realize the adaptive and robust following control, and its asymptotic stability is rigorously proved via Lyapunov theory. The performance of the proposed method is evaluated with numerical experiments and compared with adaptive PID controller and adaptive fuzzy sliding mode controller (SMC). The maximum following errors of ARILC after 6 iterations can be reduced by more than 99% compared to the initial iteration, and the controller is quite less sensitive to the environment changes than the other two controllers, which proves that the proposed control framework is much more robust and effective in variable walking environments.

Numerical algorithm for nonlinear fractional equations with nonlocal boundary conditions based on a modified minimum residual method

Abstract In this paper, we will solve a nonlinear time-fractional equation with nonlocal boundary conditions. First, we construct a set of suitable base according to the correlation theory of reproducing kernel space and the nonlocal boundary conditions. Then we introduce F-derivative and Newton iterative to linearize the nonlinear terms of the equation. The appropriate initial iteration value is constructed by boundary conditions. Finally, we use the ɛ-approximate solution theory to solve the system of linear equations and give the corresponding theoretical derivation. In conclusion, several numerical examples illustrate the feasibility and effectiveness of the method.

Identification of Essential Steps in Outlet Forceps-Assisted Vaginal Delivery: A Delphi Study

ObjectiveTo achieve expert consensus using the Delphi methodology on the sub-steps considered essential in an outlet forceps-assisted vaginal delivery (FAVD). The purpose of this work is to help inform a framework for standardized training and objective assessment in the procedure. MethodsA Delphi survey was conducted with an international panel of experts in FAVD. Using an online platform, experts rated sub-steps of FAVD on a 5-point Likert scale to indicate whether they considered them essential. Responses were returned to the panel until consensus was reached (Cronbach α ≥ 0.80) with an intraclass correlation coefficient ≥0.75. All sub-steps with a rate of agreement ≥80% are proposed to be included in a future evaluation instrument. ResultsAfter the first iteration of the Delphi procedure, a response rate of 42% was reached (n = 21); the second iteration was only sent to those who had participated in the initial iteration, reaching a response rate of 100%. Of 42 sub-steps rated in the first round, 24 (57.1%) achieved consensus, 8 (19%) were rejected, and 10 (23.8%) were re-rated in the second round. After 2 iterations, 28 sub-steps were agreed upon by the experts to be essential in FAVD. ConclusionsA panel of experts identified a total of 28 sub-steps essential to FAVD. This list could inform the development of an objective assessment framework and evaluation tool for this procedure. Further research should focus on the standardization, applicability, validation, and introduction of this tool in medical training, with a focus on real-life, high-fidelity simulation and online educational tools.

Loss analysis of radial magnetic bearing using genetic algorithm and gradient based method

Radial magnetic bearing (RMB) works on magnetic levitation principle. Use of magnetic forces for supporting a shaft has been topic of interest of researchers. Shaft supported using magnetic force eliminates the use of lubricant and these bearing are free from rubbing action between the surfaces. Application of turbine rotor 60 mm diameter is taken into consideration and RMB of eight poles with NSNS pole configuration has been designed. Bearing is designed such that it is capable of generating electromagnetic force to levitate rotor with constant air gap 0.5 mm in dynamic condition. However these bearing encounter various energy losses. Loss optimization of eight poles RMB is carried out using variables like air gap, turbine speed, and laminating layer thickness over the constraints. Optimum results of bearing design variables obtained using Genetic Algorithm (GA) ensures minimum total loss in bearing. These optimized variables are used as initial iteration in reduced gradient based method. Results obtained in GA are validated using Reduced Gradient method.

A Novel Iterative Reweighted Method for Forest Height Inversion Using Multibaseline PolInSAR Data

Multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the advanced technologies of forest height inversion, as it provides rich observation information. In this letter, we propose a new iterative reweighted method for multibaseline PolInSAR joint inversion of forest height. First, we establish a better stochastic model and weight function to obtain more accurate parameter estimation considering the relationship between vertical wavenumber and forest height. Second, according to the proposed inversion criterion, the baseline observations with unsuitable interferometric geometry are regarded as gross errors and eliminated through reweighted iteration. Finally, we select airborne P-band synthetic aperture radar (SAR) data collected by the F-SAR system during the AfriSAR 2016 campaign for experimental validation. The experimental results show that using initial iteration values obtained by three optimal baseline selection methods, the accuracy of the proposed method achieves 4.47, 4.46, and 4.24 m, which is about 34.74%, 32.32%, and 33.23% higher than those of the existing multibaseline joint inversion method [root mean square error (RMSE) = 6.85, 6.59, and 6.35 m], respectively.