Significant Errors Research Articles

Correction: Feistel, R.; Hellmuth, O. Irreversible Thermodynamics of Seawater Evaporation. J. Mar. Sci. Eng. 2024, 12, 166

A sign error has been found in Equation (32) of the paper [...]JW=−Dfln1+SMW1−SMS+ln⁡ψfxV,T,p[...]

Hebbian Descent: A Unified View on Log-Likelihood Learning.

This study discusses the negative impact of the derivative of the activation functions in the output layer of artificial neural networks, in particular in continual learning. We propose Hebbian descent as a theoretical framework to overcome this limitation, which is implemented through an alternative loss function for gradient descent we refer to as Hebbian descent loss. This loss is effectively the generalized log-likelihood loss and corresponds to an alternative weight update rule for the output layer wherein the derivative of the activation function is disregarded. We show how this update avoids vanishing error signals during backpropagation in saturated regions of the activation functions, which is particularly helpful in training shallow neural networks and deep neural networks where saturating activation functions are only used in the output layer. In combination with centering, Hebbian descent leads to better continual learning capabilities. It provides a unifying perspective on Hebbian learning, gradient descent, and generalized linear models, for all of which we discuss the advantages and disadvantages. Given activation functions with strictly positive derivative (as often the case in practice), Hebbian descent inherits the convergence properties of regular gradient descent. While established pairings of loss and output layer activation function (e.g., mean squared error with linear or cross-entropy with sigmoid/softmax) are subsumed by Hebbian descent, we provide general insights for designing arbitrary loss activation function combinations that benefit from Hebbian descent. For shallow networks, we show that Hebbian descent outperforms Hebbian learning, has a performance similar to regular gradient descent, and has a much better performance than all other tested update rules in continual learning. In combination with centering, Hebbian descent implements a forgetting mechanism that prevents catastrophic interference notably better than the other tested update rules. When training deep neural networks, our experimental results suggest that Hebbian descent has better or similar performance as gradient descent.

Optimal Asymptotic Tracking Control for Nonzero-Sum Differential Game Systems with Unknown Drift Dynamics via Integral Reinforcement Learning

This paper employs an integral reinforcement learning (IRL) method to investigate the optimal tracking control problem (OTCP) for nonlinear nonzero-sum (NZS) differential game systems with unknown drift dynamics. Unlike existing methods, which can only bound the tracking error, the proposed approach ensures that the tracking error asymptotically converges to zero. This study begins by constructing an augmented system using the tracking error and reference signal, transforming the original OTCP into solving the coupled Hamilton–Jacobi (HJ) equation of the augmented system. Because the HJ equation contains unknown drift dynamics and cannot be directly solved, the IRL method is utilized to convert the HJ equation into an equivalent equation without unknown drift dynamics. To solve this equation, a critic neural network (NN) is employed to approximate the complex value function based on the tracking error and reference information data. For the unknown NN weights, the least squares (LS) method is used to design an estimation law, and the convergence of the weight estimation error is subsequently proven. The approximate solution of optimal control converges to the Nash equilibrium, and the tracking error asymptotically converges to zero in the closed system. Finally, we validate the effectiveness of the proposed method in this paper based on MATLAB using the ode45 method and least squares method to execute Algorithm 2.

State-of-Charge and State-of-Health Estimation in Li-Ion Batteries Using Cascade Electrochemical Model-Based Sliding-Mode Observers

This paper proposes a cascade approach based on a sliding mode observer (SMO) for estimating the state of charge (SoC) and state of health (SoH) of lithium-ion (Li-ion) batteries using a single particle model (SPM). After convergence, the observation error signal of the current node in the cascade observer is generated from the output injection signal of the previous node’s observer. The current node’s observer generates its output injection signal, leading to its convergence. This sequential process accurately determines the observed values of each node using only the battery’s current and voltage. Subsequently, the SoC and SoH are estimated using observations of lithium-ion concentrations on the surface and inside the battery anode. The accuracy of this approach is validated using Dynamic Stress Test (DST) and Federal Urban Driving Scheme (FUDS) experimental data. A comparative analysis with conventional SMO and Extended Kalman Filter (EKF) algorithms demonstrates the approach’s effectiveness and superior performance, confirming its practical applicability.

Spatially organized striatal neuromodulator release encodes trajectory errors.

Goal-directed navigation requires animals to continuously evaluate their current direction and speed of travel relative to landmarks to discern whether they are approaching or deviating from their goal. Striatal dopamine and acetylcholine are powerful modulators of goal-directed behavior, but it is unclear whether and how neuromodulator dynamics at landmarks incorporate relative motion for effective behavioral guidance. Using optical measurements in mice, we demonstrate that cue-evoked striatal dopamine release encodes bi-directional 'trajectory errors' reflecting relationships between ongoing speed and direction of locomotion and visual flow relative to optimal goal trajectories. Striatum-wide micro-fiber array recordings resolved an anatomical gradient of trajectory error signaling across the anterior-posterior axis, distinct from trajectory error independent cue signals. Dynamic regression modeling revealed that positive and negative trajectory error encoding emerges early and late respectively during learning and over different time courses in the medial and lateral striatum, enabling region specific contributions to learning. Striatal acetylcholine release also encodes trajectory errors, but encoding is more spatially restricted, opposite polarity, and delayed relative to dopamine, supporting distinct roles in modulating striatal output and behavior. Dopamine trajectory error signaling and task performance were reproduced in a reinforcement learning model incorporating a conjunctive state space representation, suggesting a potential neural substrate for trajectory error generation. Our results establish region specific neuromodulator signals positioned to guide the speed and direction of locomotion to reach goals based on environmental landmarks during navigation.

The self-fulfilling prophecy in medicine

This article first describes the mechanism of any self-fulfilling prophecy through discussion of its four conditions: credibility, employment, employment sensitivity, and realization. Each condition is illustrated with examples specific to the medical context. The descriptive account ends with the definition of self-fulfilling prophecy and an expansion on collective self-fulfilling prophecies. Second, the normative account then discusses the moral relevance of self-fulfilling prophecies in medicine. A self-fulfilling prophecy is typically considered problematic when the prediction itself changes the predicted outcome to match the prediction (transformative self-fulfillment). I argue that also self-fulfilling prophecies that do not change the outcome but change the ways in which the outcome was realized (operative self-fulfillment), have significant ethical and epistemic ramifications. Because it is difficult to distinguish, retrospectively, between a transformative and an operative self-fulfilling prophecy, and thus between a false or true positive, it becomes equally difficult to catch mistakes. Moreover, since the prediction necessarily turns out true, there is never an error signal warning that a mistake might have been made. On the contrary, accuracy is seen as the standard for quality assurance. As such, self-fulfilling prophecies inhibit our ability to learn, inviting repetition and exacerbation of mistakes. With the rise of automated diagnostic and prognostic procedures and the increased use of machine learning and artificial intelligence for the development of predictive algorithms, attention to self-fulfilling feedback loops is especially warranted. This account of self-fulfilling prophecies is practically relevant for medical research and clinical practice. With it, researchers and practitioners can detect and analyze potential self-fulfilling mechanisms in any medical case and take responsibility for their ethical and epistemic implications.

A Case Study of Suspected Childhood Apraxia of Sign.

We provide a case report of "Zoe," a 4-year-old deaf child from a deaf signing family, who presented with a possible case of Childhood Apraxia of Sign (CASign). The description is based on reports from the child's speech-language pathologist, her Individualized Education Program report, and a clinician-created sign imitation task. Zoe's sign articulation errors in American Sign Language differed from those reported for typically developing deaf children and were parallel to the types of errors observed for hearing children with childhood apraxia of speech. Specifically, Zoe produced inconsistent errors across signs, substituted more complex handshapes for simple handshapes, made errors on both unmarked (common) and marked (less common) forms, produced articulatory distortions (rather than substitutions), and exhibited "groping" behaviors (a sequence of attempts to move her hands into the correct position). In addition, Zoe sometimes self-corrected her errors by manipulating her own hands, for example, using her left hand to move the thumb of her right hand into the correct position. Zoe's pattern of sign errors is consistent with an underlying deficit in motor planning and/or programming and may constitute the first reported case of CASign.

Comparative Study of Denoising and Segmentation Techniques for Accurate Brain Tumor Detection in MRI

Background: Brain tumor incidence is on the rise each year, with more than 130 identified types. Precise segmentation models play a vital role in the diagnosis and treatment of brain tumors. This study specifically investigates the utilization of diffusion-based denoising techniques and thresholding methods for segmenting brain tumors from MRI images. Objective: The objective of this study is to examine and compare the efficacy of the Perona-malik and Weickert diffusion techniques in denoising brain MRI images. Additionally, the study aims to assess their performance in threshold-based segmentation of brain tumors. Moreover, it also aims to evaluate the compatibility, benefits, and limitations of the Perona-Malik and Weickert diffusion methods in the denoising of brain MRI images and the effect of denoising on segmentation. Methods: In this study, the Perona-Malik and Weickert diffusion methods are employed to denoise brain MRI images. The denoised images are then subjected to thresholding using both binary and fuzzy approaches, utilizing a triangular membership function. The performance of the diffusion techniques is evaluated using metrics, such as Mean Square Error and Peak Signal to Noise Ratio. Additionally, segmentation models are assessed using metrics such as Dice Similarity Coefficient, Jaccard Similarity Coefficient, and Structural Similarity Measurement Index. Results: The Perona-Malik and Weickert diffusion methods exhibit compatibility with various types of noise, each having its own set of advantages and limitations. The Weickert diffusion method excels in preserving image structure and texture during thresholding. Conclusion: The study provides evidence for the effectiveness of diffusion-based denoising techniques in segmenting brain tumors from MRI images. Specifically, the Weickert diffusion method outperforms in preserving essential image characteristics during thresholding. Additionally, fuzzy thresholding proves to be more successful in accurately segmenting brain tumors. These findings contribute to the advancement of precise models for brain tumor segmentation, ultimately enhancing the diagnosis and treatment of these tumors.

Exponential PID controller for effective load frequency regulation of electric power systems

Load frequency regulation (LFR) is an indispensable scheme in planning electrical power production to provide consumers with stable, reliable and uninterrupted power. In the face of complicated power system (PS) structures with increasing and intricate power demand, new controllers that offer not only good performance, but also easy commissioning in practice are required. To this end, this research introduces an exponential PID (EXP-PID) controller as a new control scheme to ameliorate the LFR performance of PSs. This controller is simple to design and has a nonlinear feature inherited from two tunable exponential functions, which are placed in front of the PID controller and act on the error signal and its time derivative individually. To achieve the utmost performance, the EXP-PID controller’s parameters are procured by a corrected variant of the snake optimizer (co-SO). To validate the proposed control scheme, various single-/multi-area single-/multi-source PSs favored in the area are considered as test benches. A thorough comparison with the state-of-the-art approaches is performed to disclose the true efficacy of our proposal. Among the rivals, co-SO tuned EXP-PID controller, despite its simplicity, is found to render credible and promising performance in mitigating frequency and tie-line power deviations effectively.

Cortical-subcortical interactions underlie processing of auditory predictions measured with 7T fMRI.

Perception integrates both sensory inputs and internal models of the environment. In the auditory domain, predictions play a critical role because of the temporal nature of sounds. However, the precise contribution of cortical and subcortical structures in these processes and their interaction remain unclear. It is also unclear whether these brain interactions are specific to abstract rules or if they also underlie the predictive coding of local features. We used high-field 7T functional magnetic resonance imaging to investigate interactions between cortical and subcortical areas during auditory predictive processing. Volunteers listened to tone sequences in an oddball paradigm where the predictability of the deviant was manipulated. Perturbations in periodicity were also introduced to test the specificity of the response. Results indicate that both cortical and subcortical auditory structures encode high-order predictive dynamics, with the effect of predictability being strongest in the auditory cortex. These predictive dynamics were best explained by modeling a top-down information flow, in contrast to unpredicted responses. No error signals were observed to deviations of periodicity, suggesting that these responses are specific to abstract rule violations. Our results support the idea that the high-order predictive dynamics observed in subcortical areas propagate from the auditory cortex.

A broadband active sound absorber with adjustable absorption coefficient and bandwidth.

Broadband adjustable sound absorbers are desired for controlling the acoustic conditions within enclosed spaces. Existing studies on acoustic absorbers, either passive or active, aim to maximize the sound absorption coefficients over an extended frequency band. By contrast, this paper introduces a tunable acoustic absorber, whose working frequency band and sound absorption characteristics can be defined by users for different applications. The approach leverages an error signal that can be synthesized using a standing wave separation technique. The error signal encodes different target reflection coefficients, leading to arbitrary absorption coefficients between 0 and 1. Experimental validation is conducted in a one-dimensional standing wave tube, demonstrating that the proposed active absorber achieves near-perfect absorption within the 150-1600 Hz frequency range, boasting an average absorption coefficient of 0.98. Adjustable absorption is demonstrated across three octave bands, aligning closely with theoretical predictions. Furthermore, when coupled with a shaping filter, the absorber exhibits spectrally tunable broadband absorption capabilities, selectively reflecting specific frequency bands while effectively absorbing others. These outcomes underscore the versatile tunability of the proposed active acoustic absorber, which is expected to pave the way for personalized regulating of the indoor acoustic environment.

Fixed-time anti-disturbance control for nonlinear turbofan engine with impulsive prescribed performance

To analyze the robust control problem of turbofan engine within a large flight envelope, a fixed-time prescribed performance control scheme is proposed for the nonlinear turbofan engine in presence of unknown disturbances and possibly discontinuous reference signals. Firstly, the equilibrium manifold expansion (EME) modeling method is adopted for the turbofan engine to construct an affine nonlinear model. Then, considering the possibly discontinuous reference signals, a novel fixed-time prescribed performance function (FxTPPF) with impulsive characteristic is presented to successfully deal with the singularity problem caused by its discontinuity situation. In what follows, a fixed-time nonlinear disturbance observer (FxTNDO) is developed to realize fast estimation of disturbances within fixed time. According to the transformed error system and disturbance estimations, a fixed-time anti-disturbance composite controller is further developed. By borrowing Lyapunov stability theory, the boundedness of all error signals in the impulsive closed-loop system is proved. Finally, simulation results indicate that this control scheme can ensure rotor speeds effectively to track the desired commands within a predefined time after the appearance of a discontinuity and the tracking errors are limited to the performance bound all along.

Sparsity-aware complex-valued least mean kurtosis algorithms

Complex-valued least mean kurtosis (CLMK) algorithm and its augmented version (ACLMK) have recently become popular as workhorse tools in the processing of complex-valued signals due to their superior performances. Unfortunately, they are not yet suitable for sparse system identification problems. In this paper, combining the well-known sparsity-promoting strategies into the cost function based on the negated kurtosis of the error signal, we introduce a suit of sparsity-aware CLMK algorithms, named l0-norm CLMK (l0-CLMK), l0-ACLMK, zero-attraction CLMK (ZA-CLMK), ZA-ACLMK, reweighted ZA-CLMK (RZA-CLMK), and RZA-ACLMK. Simulation results on synthetic and real-world sparse system identification scenarios in the complex domain show that the proposed algorithms outperform the existing sparsity-aware algorithms in terms of convergence rate, tracking, and steady-state error.

High covertness camouflage covert underwater acoustic communication based on masking technique

In marine military operations, it is very important to ensure the safe transmission of underwater information, so it is urgent to improve the covertness of underwater communication. In order to meet the requirement of high covertness in underwater acoustic communication, according to the natural characteristics of superimposed interference of marine ambient noise, a high covertness camouflage covert underwater acoustic communication (CUWAC) method based on masking technique using ship-radiated noise as communication carrier is proposed in this paper. This method leverages innovative audio digital technology to embed information into the DCT domain of ship-radiated noise, creating a disguised signal that is hard to detect, thereby significantly enhancing stealth without compromising communication effectiveness. In order to improve the demodulation performance of this camouflage CUWAC, this paper proposes a variable step size p-norm-like LMP sparse adaptive filtering algorithm to offset the interference of the masking signal at the receiving end. This adaptive filtering algorithm is based on the improved modified Gaussian function to construct step size. Furthermore, it introduces the error signal autocorrelation function to adjust the step size and zero-attracting term dynamically. The proposed adaptive filtering algorithm obtains excellent anti-noise performance. Through a series of simulation experiments, this paper not only validates the feasibility of the CUWAC method but also demonstrates its stability and reliability under various marine environmental conditions, providing robust theoretical support for practical applications, proving its efficiency and reliability in complex marine environments.

Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses.

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio-visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio-visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio-visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.

Origin and properties of own error signals of the discrete wavelet transform algorithms

Origin and properties of own error signals of the discrete wavelet transform algorithms

Sensorimotor adaptation impedes perturbation detection in grasping.

Humans achieve skilled actions by continuously correcting for motor errors or perceptual misjudgments, a process called sensorimotor adaptation. This can occur with the actor both detecting (explicitly) and not detecting the error (implicitly). We investigated how the magnitude of a perturbation and the corresponding error signal each contribute to the detection of a size perturbation during interaction with real-world objects. Participants grasped cuboids of different lengths in a mirror-setup allowing us to present different sizes for seen and felt cuboids, respectively. Visuo-haptic size mismatches (perturbations) were introduced either abruptly or followed a sinusoidal schedule. These schedules dissociated the error signal from the visuo-haptic mismatch: Participants could fully adapt their grip and reduce the error when a perturbation was introduced abruptly and then stayed the same, but not with a constantly changing sinusoidal perturbation. We compared participants' performance in a two-alternative forced choice (2AFC) task where participants judged these mismatches, and modelled error-correction in grasping movements by looking at changes in maximum grip apertures, measured using motion tracking. We found similar mismatch-detection performance with sinusoidal perturbation schedules and the first trial after an abrupt change, but decreasing performance over further trials for the latter. This is consistent with the idea that reduced error signals following adaptation make it harder to detect perturbations. Error-correction parameters indicated stronger error-correction in abruptly introduced perturbations. However, we saw no correlation between error-correction and overall mismatch-detection performance. This emphasizes the distinct contributions of the perturbation magnitude and the error signal in helping participants detect sensory perturbations.

Controlling optical-cavity locking using reinforcement learning

This study applies an effective methodology based on Reinforcement Learning to a control system. Using the Pound–Drever–Hall locking scheme, we match the wavelength of a controlled laser to the length of a Fabry-Pérot cavity such that the cavity length is an exact integer multiple of the laser wavelength. Typically, long-term drift of the cavity length and laser wavelength exceeds the dynamic range of this control if only the laser’s piezoelectric transducer is actuated, so the same error signal also controls the temperature of the laser crystal. In this work, we instead implement this feedback control grounded on Q-Learning. Our system learns in real-time, eschewing reliance on historical data, and exhibits adaptability to system variations post-training. This adaptive quality ensures continuous updates to the learning agent. This innovative approach maintains lock for eight days on average.

Ultrasensitive Electrochemiluminescence Imaging Detection of Multiple miRNAs in Single Cells with a Closed Bipolar Electrode Array Chip.

In situ sensitive detection of multiple biomarkers in a single cell was highly necessary for understanding the pathogenesis mechanism and facilitating disease diagnosis. Herein, a bipolar electrode (BPE)-electrochemiluminescence (ECL) imaging chip was designed for ultrasensitive in situ detection of multiple miRNAs in single cells based on a dual-signal amplification strategy. A single cell was trapped and lysed within the microtrap of the cathode chamber and an HCR amplification process and nanoprobes (Fc/DNA/Fe3O4) were introduced, leading to a large number of electroactive molecules (Fc) being modified on the surface. Under a suitable potential, Fc+ in the cathodic chamber was reduced to Fc and L-012 was oxidized in the anodic chamber according to the electric neutrality principle of the bipolar electrode system, resulting in the ECL signal recorded by EMCCD. Ascribed to the dual-signal amplification, sensitive visual detection of miRNA-21 and miRNA-155 in single cells was achieved. For MCF-7 cells, miRNA-21 and miRNA-155 were calculated to be 4385 and 1932 copies/cell (median), respectively. For HeLa cells, miRNA-21 and miRNA-155 were calculated to be 1843 and 1012 copies/cell (median), respectively. The comprehensive evaluation of two kinds of miRNA could effectively eliminate error signals, and the detection precision was improved by 10%.

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

Multi-channel active noise control (ANC) systems can achieve effective attenuation of low-frequency noise in spatial areas and have been extensively studied. Recent works have focused on the application of multitask diffusion strategies in ANC systems based on wireless acoustic sensor and actuator networks (WASAN), which show the advantages of saving computational burden and enhancing flexibility. However, these works all adopt the mean-square error minimization as the optimization criterion, which leads to severe performance degradation in the presence of impulsive noise interference. Therefore, in this paper, we exploit the M-estimate function and the augmented weight vector of the control filter to formulate a robust minimization problem and propose a distributed augmented diffusion filtered-x least mean M-estimate (DADFxLMM) algorithm for multi-channel ANC systems. The proposed algorithm leverages the M-estimate function to overcome the influence of outliers in the error signal and achieves robustness against impulsive noise, and also eliminates the dependence on the symmetry of the acoustic paths by integrating the augmented weight vectors. Moreover, we replace the score function with the update probability of the weight vector to avoid the need for integration and Price’s theorem, and analyze the mean and mean-square performance of the proposed DADFxLMM algorithm under the contaminated Gaussian (CG) noise model. Simulation results under different system configurations demonstrate that the proposed algorithm outperforms the existing multi-channel ANC algorithms in the presence of impulsive noise interference.