Adaptive Stopping Research Articles

Management Insights

Management Insights

Trap Detection Based Decoding Algorithm for Tail-biting Convolutional Codes

该文分析了循环维特比算法(CVA)中存在的循环陷阱问题，并证明了传统基于CVA的咬尾卷积码译码算法中存在的不足，提出了一种高效率的咬尾卷积码译码算法。该算法通过检测两次不同迭代中获得的两条最大似然路径是否相同来判断是否有循环陷阱产生，并及时终止循环，减少冗余迭代；在没有循环陷阱产生的情况下，新算法比较当前迭代中最大似然路径和已经发现的最优咬尾路径是否相同来自适应终止迭代。文中对循环陷阱检测方案和自适应终止方案做了进一步优化，即利用路径的净增量而非路径本身作为检测量。实验结果表明新算法提高了译码效率，降低了译码复杂度。

A point-of-care clinical trial comparing insulin administered using a sliding scale versus a weight-based regimen

Background Clinical trials are widely considered the gold standard in comparative effectiveness research (CER) but the high cost and complexity of traditional trials and concerns about generalizability to broad patient populations and general clinical practice limit their appeal. Unsuccessful implementation of CER results limits the value of even the highest quality trials. Planning for a trial comparing two standard strategies of insulin administration for hospitalized patients led us to develop a new method for a clinical trial designed to be embedded directly into the clinical care setting thereby lowering the cost, increasing the pragmatic nature of the overall trial, strengthening implementation, and creating an integrated environment of research-based care.Purpose We describe a novel randomized clinical trial that uses the informatics and statistics infrastructure of the Veterans Affairs Healthcare System (VA) to illustrate one key component (called the point-of-care clinical trial – POC-CT) of a ‘learning healthcare system,’ and settles a clinical question of interest to the VA.Methods This study is an open-label, randomized trial comparing sliding scale regular insulin to a weight-based regimen for control of hyperglycemia, using the primary outcome length of stay, in non-ICU inpatients within the northeast region of the VA. All non-ICU patients who require in-hospital insulin therapy are eligible for the trial, and the VA’s automated systems will be used to assess eligibility and present the possibility of randomization to the clinician at the point of care. Clinicians will indicate their approval for informed consent to be obtained by study staff. Adaptive randomization will assign up to 3000 patients, preferentially to the currently ‘winning’ strategy, and all care will proceed according to usual practices. Based on a Bayesian stopping rule, the study has acceptable frequentist operating characteristics (Type I error 6%, power 86%) against a 12% reduction of median length of stay from 5 to 4.4 days. The adaptive stopping rule promotes implementation of a successful treatment strategy.Limitations Despite clinical equipoise, individual healthcare providers may have strong treatment preferences that jeopardize the success and implementation of the trial design, leading to low rates of randomization. Unblinded treatment assignment may bias results. In addition, generalization of clinical results to other healthcare systems may be limited by differences in patient population. Generalizability of the POC-CT method depends on the level of informatics and statistics infrastructure available to a healthcare system.Conclusions The methods proposed will demonstrate outcome-based evaluation of control of hyperglycemia in hospitalized veterans. By institutionalizing a process of statistically sound and efficient learning, and by integrating that learning with automatic implementation of best practice, the participating VA Healthcare Systems will accelerate improvements in the effectiveness of care.

Robustness and the Halting Problem for Multicellular Artificial Ontogeny

Most works in multicellular artificial ontogeny solve the halting problem by arbitrarily limiting the number of iterations of the developmental process. Hence, the trajectory of the developing organism in the phenotypic space is only required to come close to an accurate solution during a very short developmental period. Because of the well-known opportunism of evolution, there is indeed no reason for the organism to remain close to a good solution in other situations: if the development is continued after the limiting bound; if the environment is perturbed by some noise during the development; if the development takes place in different physical conditions. In order to increase the robustness of the solution against such hazards, a new stopping criterion for the developmental process is proposed, based on the stability of some internal energy of the organism during its development. Such adaptive stopping criterion biases evolution toward solutions in which robustness is an intrinsic property. Experimental results on different “French flag” problems demonstrate that enforcing stable developmental process makes it possible to produce solutions that not only accurately approximate the target shape, but also demonstrate near-perfect self-healing properties, as well as excellent generalization capabilities.

A Convergent Nonconforming Adaptive Finite Element Method with Quasi-Optimal Complexity

In this paper, we prove convergence and quasi-optimal complexity of a simple adaptive nonconforming finite element method. In each step of the algorithm, the iterative solution of the discrete system is controlled by an adaptive stopping criterion, and the local refinement is based on either a simple edge residual or a volume term, depending on an adaptive marking strategy. We prove that this marking strategy guarantees a strict reduction of the error, augmented by the volume term and an additional oscillation term, and quasi-optimal complexity of the generated sequence of meshes.

Adaptive Stopping Criterion for Fast Time Domain Characterization of Microwave Components

This letter proposes a novel adaptive stopping criterion that facilitates a fast time domain characterization of microwave components. By successively computing a rational macromodel based on time-limited transient responses of the microwave component under study, the proposed stopping criterion limits the number of time steps required to capture the frequency behavior up to a predefined accuracy level. Therefore, it is particularly useful for systems where the transient responses decay slowly.

Convergence and quasi-optimal complexity of a simple adaptive finite element method

We prove convergence and quasi-optimal complexity of an adaptive finite element algorithm on triangular meshes with standard mesh refinement. Our algorithm is based on an adaptive marking strategy. In each iteration, a simple edge estimator is compared to an oscillation term and the marking of cells for refinement is done according to the dominant contribution only. In addition, we introduce an adaptive stopping criterion for iterative solution which compares an estimator for the iteration error with the estimator for the discretization error.

Inversion of the Laplace Transform from the Real Axis Using an Adaptive Iterative Method

A new method for inverting the Laplace transform from the real axis is formulated. This method is based on a quadrature formula. We assume that the unknown function is continuous with (known) compact support. An adaptive iterative method and an adaptive stopping rule, which yield the convergence of the approximate solution to , are proposed in this paper.

An optimally convergent adaptive mixed finite element method

We prove convergence and optimal complexity of an adaptive mixed finite element algorithm, based on the lowest-order Raviart–Thomas finite element space. In each step of the algorithm, the local refinement is either performed using simple edge residuals or a data oscillation term, depending on an adaptive marking strategy. The inexact solution of the discrete system is controlled by an adaptive stopping criterion related to the estimator.

An extension of min/max flow framework

In this paper, the min/max flow scheme for image restoration is revised. The novelty consists of the following three parts. The first is to analyze the reason of the speckle generation and then to modify the original scheme. The second is to point out that the continued application of this scheme cannot result in an adaptive stopping of the curvature flow. This is followed by modifications of the original scheme through the introduction of the Gradient Vector Flow (GVF) field and the zero-crossing detector, so as to control the smoothing effect. Our experimental results with image restoration show that the proposed schemes can reach a steady state solution while preserving the essential structures of objects. The third is to extend the min/max flow scheme to deal with the boundary leaking problem, which is indeed an intrinsic shortcoming of the familiar geodesic active contour model. The min/max flow framework provides us with an effective way to approximate the optimal solution. From an implementation point of view, this extended scheme makes the speed function simpler and more flexible. The experimental results of segmentation and region tracking show that the boundary leaking problem can be effectively suppressed.

Artifact removal and texture-based rendering for visualization of 3D fetal ultrasound images

Convenient and non-invasive ultrasonography has become an essential tool for diagnosing fetal abnormalities. However, the noisy and blurry nature of sonographic data poses a challenge. To improve object visualization, we first develop a modified diffusion filter that utilizes the local standard deviation and edge of local-average-difference to define an adaptive edge stopping function in diffusion filtering. The proposed method overcomes the drawbacks of traditional diffusion filters and shows good results in comparative experiments. Moreover, we propose a novel light absorbing function to remove large regions of interface artifacts. An advanced imaging mode, called texture-based rendering, is employed to provide more realistic rendering. Experimental results show that the proposed methods enhance final image quality in 3D fetal sonograms.

Selecting a Selection Procedure

Selection procedures are used in a variety of applications to select the best of a finite set of alternatives. “Best” is defined with respect to the largest mean, but the mean is inferred with statistical sampling, as in simulation optimization. There are a wide variety of procedures, which begs the question of which selection procedure to select. The main contribution of this paper is to identify, through extensive experimentation, the most effective selection procedures when samples are independent and normally distributed. We also (a) summarize the main structural approaches to deriving selection procedures, (b) formalize new sampling allocations and stopping rules, (c) identify strengths and weaknesses of the procedures, (d) identify some theoretical links between them, and (e) present an innovative empirical test bed with the most extensive numerical comparison of selection procedures to date. The most efficient and easiest to control procedures allocate samples with a Bayesian model for uncertainty about the means and use new adaptive stopping rules proposed here.

Inner solvers for interior point methods for large scale nonlinear programming

This paper deals with the solution of nonlinear programming problems arising from elliptic control problems by an interior point scheme. At each step of the scheme, we have to solve a large scale symmetric and indefinite system; inner iterative solvers, with an adaptive stopping rule, can be used in order to avoid unnecessary inner iterations, especially when the current outer iterate is far from the solution. In this work, we analyse the method of multipliers and the preconditioned conjugate gradient method as inner solvers for interior point schemes. We discuss the convergence of the whole approach, the implementation details and report the results of numerical experimentation on a set of large scale test problems arising from the discretization of elliptic control problems. A comparison with other interior point codes is also reported.

Stopping criteria for inner iterations in inexact potential reduction methods: a computational study

We focus on the use of adaptive stopping criteria in iterative methods for KKT systems that arise in Potential Reduction methods for quadratic programming. The aim of these criteria is to relate the accuracy in the solution of the KKT system to the quality of the current iterate, to get computational efficiency. We analyze a stopping criterion deriving from the convergence theory of inexact Potential Reduction methods and investigate the possibility of relaxing it in order to reduce as much as possible the overall computational cost. We also devise computational strategies to face a possible slowdown of convergence when an insufficient accuracy is required.

Cost-sensitive feature acquisition and classification

There are many sensing challenges for which one must balance the effectiveness of a given measurement with the associated sensing cost. For example, when performing a diagnosis a doctor must balance the cost and benefit of a given test (measurement), and the decision to stop sensing (stop performing tests) must account for the risk to the patient and doctor (malpractice) for a given diagnosis based on observed data. This motivates a cost-sensitive classification problem in which the features (sensing results) are not given a priori; the algorithm determines which features to acquire next, as well as when to stop sensing and make a classification decision based on previous observations (accounting for the costs of various types of errors, as well as the rewards of being correct). We formally define the cost-sensitive classification problem and solve it via a partially observable Markov decision process (POMDP). While the POMDP constitutes an intuitively appealing formulation, the intrinsic properties of classification tasks resist application of it to this problem. We circumvent the difficulties of the POMDP via a myopic approach, with an adaptive stopping criterion linked to the standard POMDP. The myopic algorithm is computationally feasible, easily handles continuous features, and seamlessly avoids repeated actions. Experiments with several benchmark data sets show that the proposed method yields state-of-the-art performance, and importantly our method uses only a small fraction of the features that are generally used in competitive approaches.

APPROXIMATION TO OPTIMAL STOPPING RULES FOR GUMBEL RANDOM VARIABLES WITH UNKNOWN LOCATION AND SCALE PARAMETERS

An optimal stopping rule is a rule that stops the sampling process at a sample size n that maximizes the expected reward. In this paper we will study the approximation to optimal stopping rule for Gumbel random variables, because the Gumbel-type distribution is the most commonly referred to in discussions of extreme values. Let $X_1, X_2,\cdots X_n,\cdots$ be independent, identically distributed Gumbel random variables with unknown location and scale parameters,$\alpha$ and $\beta$. If we define the reward sequence $Y_n = \max \{X_1,X_2,\cdots,X_n\}-cn$ for $c \gt 0$, the optimal stopping rule for $Y_n$ depends on the unknown location and scale parameters $\alpha$ and $\beta$. We propose an adaptive stopping rule that does not depend on the unknown location and scale parameters and show that the difference between the optimal expected reward and the expected reward using the proposed adaptive stopping rule vanishes as $c$ goes to zero. Also, we use simulation in statistics to verify the results.

APPROXIMATION TO OPTIMAL STOPPING RULES FOR GUMBEL RANDOM VARIABLES WITH UNKNOWN LOCATION AND SCALE PARAMETERS

An optimal stopping rule is a rule that stops the sampling process at a sample size n that maximizes the expected reward. In this paper we will study the approximation to optimal stopping rule for Gumbel random variables, because the Gumbel-type distribution is the most commonly referred to in discussions of extreme values. Let $X_1, X_2,\cdots X_n,\cdots$ be independent, identically distributed Gumbel random variables with unknown location and scale parameters,$\alpha$ and $\beta$. If we define the reward sequence $Y_n=\max\{X_1,X_2,\cdots,X_n\}-cn$ for $c>0$, the optimal stopping rule for $Y_n$ depends on the unknown location and scale parameters $\alpha$ and $\beta$. We propose an adaptive stopping rule that does not depend on the unknown location and scale parameters and show that the difference between the optimal expected reward and the expected reward using the proposed adaptive stopping rule vanishes as $c$ goes to zero. Also, we use simulation in statistics to verify the results.

APPROXIMATION TO OPTIMAL STOPPING RULES FOR WEIBULL RANDOM VARIABLES WITH UNKNOWN SCALE PARAMETER

Let $X_1, X_2, \cdots, X_n, \cdots$ be independent, identically distributed Weibull random variables with an unknown scale parameter $\alpha$. If we define the reward sequence $Y_n = \max \{ X_1,X_2,\cdots,X_n \} - cn$ for $c \gt 0$, the optimal stopping rule for $Y_n$ depends on the unknown scale parameter $\alpha$. In this paper we propose an adaptive stopping rule that does not depend on the unknown scale parameter $\alpha$ and show that the difference between the optimal expected reward and the expected reward using the proposed adaptive stopping rule vanishes as $c$ goes to zero.

Demosaicing by successive approximation

In this paper, we present a fast and high-performance algorithm for color filter array (CFA) demosaicing. CFA demosaicing is formulated as a problem of reconstructing correlated signals from their downsampled versions with an opposite phase. The major contributions of this work include (1) a new iterative demosaicing algorithm in the color difference domain and (2) a spatially adaptive stopping criterion for suppressing color misregistration and zipper artifacts in the demosaiced images. We have compared the proposed demosaicing algorithm with two current state-of-the-art techniques reported in the literature. Ours outperforms both of them on demosaicing performance and computational cost.

APPROXIMATION TO OPTIMAL STOPPING RULES FOR WEIBULL RANDOM VARIABLES WITH UNKNOWN SCALE PARAMETER

Let $X_1, X_2,\cdots, X_n,\cdots$ be independent, identically distributed Weibull random variables with an unknown scale parameter $\alpha$. If we define the reward sequence $Y_n=\max\{X_1,X_2,\cdots,X_n\}-cn$ for $c>0$, the optimal stopping rule for $Y_n$ depends on the unknown scale parameter $\alpha$. In this paper we propose an adaptive stopping rule that does not depend on the unknown scale parameter $\alpha$ and show that the difference between the optimal expected reward and the expected reward using the proposed adaptive stopping rule vanishes as $c$ goes to zero.