Error-correcting Feedback Research Articles

Geometric Back-Projection Network for Point Cloud Classification

As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations. To achieve this, on the one hand, we enrich the geometric information of points in low-level 3D space explicitly. On the other hand, we apply CNN-based structures in high-level feature spaces to learn local geometric context implicitly. Specifically, we leverage an idea of error-correcting feedback structure to capture the local features of point clouds comprehensively. Furthermore, an attention module based on channel affinity assists the feature map to avoid possible redundancy by emphasizing its distinct channels. The performance on both synthetic and real-world point clouds datasets demonstrate the superiority and applicability of our network. Comparing with other state-of-the-art methods, our approach balances accuracy and efficiency.

Evolutionary design of regulatory control. II. Robust error-correcting feedback increases genetic and phenotypic variability

As systems become more robust against perturbations, they can compensate for greater sloppiness in the performance of their components. That robust compensation reduces the force of natural selection on the system’s components, leading to component decay. The paradoxical coupling of robustness and decay predicts that robust systems evolve cheaper, lower performing components, which accumulate greater mutational genetic variability and which have greater phenotypic stochasticity in trait expression. Previous work noted the paradox of robustness. However, no general theory for the evolutionary dynamics of system robustness and component decay has been developed. This article takes a first step by linking engineering control theory with the genetic theory of evolutionary dynamics. Control theory emphasizes error-correcting feedback as the single greatest principle in robust system design. Linking control theory to evolution leads to a theory for the evolutionary dynamics of error-correcting feedback, a unifying approach for the evolutionary analysis of robust systems. This article shows how increasingly robust systems accumulate more genetic variability and greater stochasticity of expression in their components. The theory predicts different levels of variability between different regulatory control architectures and different levels of variability between different components within a particular regulatory control system. The theory also shows that increasing robustness reduces the frequency of system failures associated with disease and, simultaneously, causes a strong increase in the heritability of disease. Thus, robust error correction in biological regulatory control may partly explain the puzzlingly high heritability of disease and, more generally, the surprisingly high heritability of fitness.

Evolutionary design of regulatory control. I. A robust control theory analysis of tradeoffs

The evolutionary design of regulatory control balances various tradeoffs in performance. Fast reaction to environmental change tends to favor plastic responsiveness at the expense of greater sensitivity to perturbations that degrade homeostatic control. Greater homeostatic stability against unpredictable disturbances tends to reduce performance in tracking environmental change. This article applies the classic principles of engineering control theory to the evolutionary design of regulatory systems. The engineering theory clarifies the conceptual aspects of evolutionary tradeoffs and provides analytic methods for developing specific predictions. On the conceptual side, this article clarifies the meanings of integral control, feedback, and design, concepts that have been discussed in a confusing way within the biological literature. On the analytic side, this article presents extensive methods and examples to study error-correcting feedback, which is perhaps the single greatest principle of design in both human-engineered and naturally designed systems. The broad framework and associated software code provide a comprehensive how-to guide for making models that focus on functional aspects of regulatory control and for making comparative predictions about regulatory design in response to various kinds of environmental challenge. The second article in this series analyzes how alternative regulatory designs influence the relative levels of genetic variability, stochasticity of trait expression, and heritability of disease.

When encoding fails: instructions, feedback, and registration without learning.

Four experiments replicated and extended the registration-without-learning effect, in which there is little improvement in the ability to discriminate an old target (X) from a highly similar test item (Y) after the first few presentations of X, even though judgments of frequency continue to rise in an open-ended fashion. Forced-choice testing revealed the anomalous form of the learning curve for X-Y discrimination (faster and then slower than the exponential). Effects of several different learning instructions were compared, but these appeared to affect only the level of initial learning, and to do little to promote X-Y discrimination learning on later presentations. The opportunity for self-testing with feedback during study provided no benefits when responding was covert, but did when overt anticipation was required. The findings are discussed in relation to the roles of bottom-up and top-down processing in memory encoding, and to the importance of error-correcting feedback in further structural learning of materials, once the materials have become familiar.

Differential-current derived feedback in error-correcting audio amplifier applications

A dominant distortion mechanism in audio power amplifiers results from nonlinearity in the voltage and current transfer functions within the power output stage. A technique is presented that uses a magnetic differential sensor to sense this distortion and to apply a supplementary error-correcting feedback path within the amplifier, in addition to overall negative feedback. Differential-current derived feedback is explored and the technique is shown to be applicable to both voltage and current transfer errors. Analysis reveals the system alignment for obtaining a distortion null and these results are confirmed using nonlinear transient analysis. Basic system topologies are presented using voltage transfer and transconductance gain cells as an aid to complete amplifier synthesis. Comparisons are made with feedforward-feedback error correction where the current-dumping amplifier is reconfigured using differential-current feedback. Generalisations are made to a multi-loop nest of amplifiers using individually aligned error correcting paths to achieve corresponding improvements in distortion reduction.

Fuzzy Adaptive Least Squares Applied to Structure-Activity and Structure-Toxicity Correlations.

A 1991 version of fuzzy adaptive least squares (FALS91) has been developed to analyze structure-activity rating data for generation of QSAR (quantitative structure-activity relationship) models. Improvements were made in this version especially to the error-correcting feedback adaptation of discriminant functions to avoid falling into a local optimum. This paper showed effective applications of FALS91 to structure-activity correlation of 29 antihypertensive arylacryloylpiperazine derivatives and structure-toxicity correlation of human acute toxicity of 504 organic chemicals as typical examples of small- and large-scale data sets, respectively. These examples demonstrated the high reliability of FALS91 in both recognition and leave-one-out prediction.