Significant Errors Research Articles

Noradrenergic alpha-2a receptor stimulation enhances prediction error signaling and updating of attention sets in anterior cingulate cortex and striatum.

The noradrenergic system is believed to support behavioral flexibility. A possible source mediating improved flexibility are α2A adrenoceptors (α2AR) in prefrontal cortex (PFC) or the anterior cingulate cortex (ACC). We tested this hypothesis by stimulating α2ARs using Guanfacine during attentional set shifting in male nonhuman primates. We found that α2AR stimulation improved learning from errors and updating attention sets. Neural recordings in the ACC, dorsolateral PFC, and the striatum showed that α2AR stimulation selectively enhanced neural signaling of prediction errors in neurons of the ACC and the striatum, but not in dlPFC. This modulation was accompanied by enhanced encoding of attended target features and particularly apparent in putative fast-spiking interneurons, pointing to an interneuron mediated mechanism of α2AR action. These results reveal that α2A receptors are part of the causal chain of flexibly updating attention sets through an enhancement of outcomes and prediction error signaling in ACC and striatum.

External-cavity diode laser at 2.6 µm and its frequency stabilization with a scanning Fabry-Pérot cavity

We report on the construction of an external cavity diode laser at 2.6 µm wavelength and its frequency stabilization with an external scanning Fabry-Pérot cavity using Pound-Drever-Hall (PDH) frequency stabilization technique. We discuss the frequency noise characteristics of the laser using the PDH residual error signal analysis and the locking performance. We have achieved a 2.5 x 105 suppression factor in the frequency noise power spectral density at Fourier frequencies lower than 100 Hz when the laser is stabilized. The laser is employed to observe resonant excitation on the 5s5p3P0 →5s4d3D1 transition in cold 88Sr atoms trapped in a magneto-optical trap. We have observed an enhancement in the trapped atom number by a factor of 8.3 compared to the case when a single laser at 707 nm is employed for repumping.

Active noise control of refrigerator based on cascaded notch feedback algorithm

Refrigerators bring convenience to people, but the noise they produce can also affect people’s lives. Refrigerator noise is dominated by low-frequency noise, and the active noise control method is more effective for this kind of noise. However, the existing active noise control methods for refrigerators do not take into account factors such as the limited space and complex structure of the refrigerator compressor room, and external interference signals will be introduced to affect the noise reduction performance during the error signal collection. Given that, this paper proposes the cascade notch feedback algorithm, which can well reduce the influence of external interference signals to better achieve the refrigerator noise reduction. The algorithm consists of the cascaded notch filtering algorithm and the feedback algorithm. The cascade notch filtering algorithm is formed by cascading the adaptive notch filtering algorithm, which is used to deal with the main frequency and harmonic noise generated by the refrigerator. The feedback algorithm consists of a robust algorithm for dealing with external interference signals introduced during error signal collection. The simulation experiment proves that the algorithm has advantages in terms of noise reduction and computational cost compared with the adaptive notch filtering algorithm and the improved algorithm. The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.

Development of a Two-Step EOF Statistical Postprocessing Algorithm to Identify Patterns of Systematic Error and Variance within GEFSv12 Reforecasts

Abstract Analysis of seasonally varying signals of model error biases, as well as the diagnosis of space–time patterns of synoptic error, here is accomplished through the development of a statistical postprocessing algorithm based on time-extended empirical orthogonal functions (EEOFs) built on patterns of variance. This work analyzes GEFSv12 200-hPa geopotential height (Z200) model error against ERA5 reanalysis data. The mean-square error variance between GEFSv12 reforecast and ERA5 grows rapidly after day 7 of a forecast and continues to increase through the end of the 16-day forecast period. At lead time 16, the largest variance occurs in mid- to high-latitude oceanic storm tracks. Variance is highest during hemispheric winter when baroclinic energy is abundant. The seasonal cycle of error shows largest anomalies in the high latitudes during hemispheric winter. After running the titular two-step, space–time EEOF algorithm, a standardized spatial eigenspectrum shows that eigenvalues increase at longer lead times after decreasing in the medium range, demonstrating that the algorithm extracts large-scale signals of systematic error in the long range. Leading wintertime space–time eigenmode pairs include high-latitude blocking structures and Rossby wave trains. Results suggest that the large-scale systematic errors in GEFSv12 Z200 initiate in part from inaccurate representations of phase speeds of atmospheric waves in the polar jet.

Transient response improvement of predictive control under time domain constraints

In this paper, a novel predictive controller design approach within augmented structure base on convex optimization of an extra parameter is proposed in order to improve the transient response of discrete-time linear systems subject to input constraints. This methodology starts with the conception of an initial stabilizing predictive controller which ensures a fast closed loop response to the detriment of the other response specifications indexes (settling time, settling error, overshoot and control signal). Then, this controller is redesigned off-line using linear programming solver to meet a certain transient response specifications of the closed loop system determined before the design as time domain constraints. The resulting controlled output will track the reference as fast and as smooth as possible with a low energy consumption. Two examples are given to show the effectiveness of the developed controller.

Building and Breaking the Chain: A Model of Reward Prediction Error Integration and Segmentation of Memory.

Prediction errors drive reinforcement learning and organize episodic memory into distinct contexts, but do these effects interact? Here, we review the roles of midbrain dopamine, the locus coeruleus, and the hippocampus in event cognition to propose and simulate the theoretical influence of two prediction error signals in integrating versus segmenting events in memory. We suggest that signed reward prediction errors can build mental models of reward environments, increasing the contextual similarity (integration) of experiences with stronger, more stable reward expectations. On the other hand, unsigned reward prediction errors can signal a new model of the environment, generating a contextual shift (segmentation) between experiences that crossed them. We moreover predicted that these differences in contextual similarity give rise to distinct patterns of temporal-order memory. We combined these ideas in a computational model to account for a seemingly paradoxical pattern of temporal-order memory where greater representational distance helps order memory within context but impairs it across contexts. We found that simulating signed reward prediction error integration and unsigned reward prediction error segmentation differentially enabled the model to perform associative chaining, which involved reactivating items between two tested probes to assist with sequential retrieval. In summary, our simulations provide a unifying explanation for the varied ways that neuromodulatory systems may alter event cognition and memory.

Application of a Two-Step Space–Time EOF Statistical Postprocessing Algorithm to Mitigate Subseasonal 200-hPa Geopotential Height Forecast Error

Abstract The time-extended empirical orthogonal function (EEOF) patterns of error developed in a companion paper are applied in this study for model data postprocessing. Projecting forecast anomalies from the model seasonal cycle from GEFSv12 outputs onto these EEOF patterns reconstructs the error principal components (PCs) in a manner that can be applied in real time. When these reconstructed error anomalies are compared with the original GEFSv12 200-hPa geopotential height (Z200) reforecast error anomalies, the projected and validation error signals tend to align temporally. When distributed globally, projection and validation error anomalies have statistically significant positive Pearson correlation coefficients with each other at most grid points outside of the tropics. Categorical Heidke skill score (HSS) analysis reaffirms that by lead day 16 of a forecast, the algorithm skillfully predicts systematic errors for locations outside of the tropics. Hemispheric winter demonstrates that the highest magnitude HSS values are in the high latitudes, illustrating the underlying seasonality of Z200 behavior and predictability. On average, wintertime HSS values hover around 20%, with high-latitude locations exceeding mean values of 40%. Comparatively, summertime values generally exceed 10% improvement outside of the tropics. High-latitude storm-track regions, such as the North Atlantic, North Pacific, and South Pacific, contain the most Z200 variance in the globe and the highest HSS values. This algorithm will be applied to real-time forecasts and additional variables in future work, with the end goal of implementation into the NOAA-CPC subseasonal forecasts.

Sign language recognition using modified deep learning network and hybrid optimization: a hybrid optimizer (HO) based optimized CNNSa-LSTM approach.

Speech impairment limits a person's capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers' speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the Hippopotamus Optimization Algorithm (HOA) and the Pathfinder Algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.

Active Vibration Control Performance Comparison Based on Middle Pedestal Stiffness Using a Mobility Model and the Narrowband Fx-LMS Technique

Vibrations generated from equipment mounted on ships radiate into the water and affect covert operation capabilities. Accordingly, various studies are being conducted to reduce vibration transmitted from mounted equipment. In this study, a system consisting of mounting equipment, a 3-axis active mount, a middle pedestal, and a lower mount of the middle pedestal was modeled using a finite element analysis program, and a mobility model was constructed by calculating the frequency response function between the positions required for analysis. The error signal (primary path) obtained using the mobility model and the response at the operating point by the control force of the actuator (secondary path) are applied to the narrowband Fx-LMS algorithm for vibration control, and the control performance was compared. Through coupling analysis of the middle pedestal, the control influence according to the rigidity of the middle pedestal was analyzed. As a result of the control simulation, the time required for vibration control was controlled approximately 6 times faster in the model, with increased stiffness of the middle pedestal, and the vibration reduction performance was predicted to improve by a minimum of 0.9 dB and a maximum of 13.3 dB. Through this study, a simulation model that can provide a guide for the design of the middle pedestal of a ship was obtained, and it is expected that it can be utilized for a preliminary design review before manufacturing the middle pedestal of a ship.

5G/GPON: A CONVERGENT ARCHITECTURE FOR WIRELESS AND OPTICAL SENSOR NETWORKS IN BOGOTÁ CITY

This paper presents the design of a convergent optical transport architecture for wireless and photonic sensor networks based on the optical access network deployed in Bogotá city. The proposed architecture is evaluated through the transport of services in subcarrier division multiplexing to enable the seamless transport of different standards such as WiFi, Bluetooth, Zigbee, SigFox, among others. The proposed design demonstrates high scalability given the distribution of the previously deployed network, enabling the incorporation of both optical sensors based on FBG and wireless sensors in locations with greater historical, economic, cultural and social relevance. The proposal presents an optimal response in terms of bit error rate (BER) and signal power in the multi-coverage zone for various interference parameters such as SS-RSRP, SS-RSRQ, and Throughput.

Digital stabilization of an I/Q modulator in the carrier suppressed single-sideband mode for atom interferometry

We present an all-digital method for stabilizing the phase biases in an electro-optic I/Q modulator for carrier-suppressed single-sideband (CS-SSB) modulation. Using programmable logic on the Red Pitaya STEMlab 125-14 platform, we digitally generate and demodulate an auxiliary radio-frequency tone whose beat with the optical carrier probes the I/Q modulator’s phase imbalances. We implement a multiple-input, multiple-output integral feedback controller that accounts for unavoidable cross-couplings in the phase biases to lock the error signals at exactly zero, where optical power fluctuations have no impact on phase stability. We demonstrate >23dB suppression of the optical carrier relative to the desired sideband at +3.4GHz over a period of 15 h and over temperature variations of 20°C.

Childhood neglect is associated with alterations in neural prediction error signaling and the response to novelty.

One in eight children experience early life stress (ELS), which increases risk for psychopathology. ELS, particularly neglect, has been associated with reduced responsivity to reward. However, little work has investigated the computational specifics of this disrupted reward response - particularly with respect to the neural response to Reward Prediction Errors (RPE) - a critical signal for successful instrumental learning - and the extent to which they are augmented to novel stimuli. The goal of the current study was to investigate the associations of abuse and neglect, and neural representation of RPE to novel and non-novel stimuli. One hundred and seventy-eight participants (aged 10-18, M = 14.9, s.d. = 2.38) engaged in the Novelty task while undergoing functional magnetic resonance imaging. In this task, participants learn to choose novel or non-novel stimuli to win monetary rewards varying from $0 to $0.30 per trial. Levels of abuse and neglect were measured using the Childhood Trauma Questionnaire. Adolescents exposed to high levels of neglect showed reduced RPE-modulated blood oxygenation level dependent response within medial and lateral frontal cortices particularly when exploring novel stimuli (p < 0.05, corrected for multiple comparisons) relative to adolescents exposed to lower levels of neglect. These data expand on previous work by indicating that neglect, but not abuse, is associated with impairments in neural RPE representation within medial and lateral frontal cortices. However, there was no association between neglect and behavioral impairments on the Novelty task, suggesting that these neural differences do not necessarily translate into behavioral differences within the context of the Novelty task.

Multi-Wavelength Narrow-Spacing Laser Frequency Stabilization Technology Based on Fabry-Perot Etalon

Classical frequency-stabilized lasers have achieved high-frequency stability and reproducibility; however, their extensive wavelength spacing limits their utility in various scenarios. This study introduces a novel frequency-stabilized laser scheme that integrates a Fabry-Perot etalon (FPE) with digital control technology and wavelength modulation techniques. The FPE, characterized by multiple transmission peaks at minimal frequency intervals, provides stable frequency references for different lasers, thereby enhancing the system’s flexibility and adaptability. An error signal is derived from the first-order differentiation of the FPE’s transmission curve. A 180° phase difference was observed in the feedback output signal when the laser’s central frequency diverged from the reference, determining that the direction of the frequency control was accordingly determined.Employing feedback control, the laser’s output frequency is stabilized at the transmission peak frequency of the FPE. Experimental results demonstrate that this stabilization scheme effectively locks the laser’s output wavelength to different transmission peak frequencies of the FPE, achieving 25 GHz wavelength spacing. The frequency stability is improved by two orders of magnitude on a second-level timescale, maintained within hundreds of kHz, equating to a frequency stability level of 10−10.

Role of dopamine in reward expectation and predictability during execution of action sequences.

Reward-associated cues serve different functions depending on whether they precede or terminate action sequences. Cues that precede action sequences and signal opportunity for reward could serve as GO signals to initiate the sequence, whereas sequence termination cues could serve as response feedback by signaling reward delivery. Reward expectation during sequence execution depends on these cues and might condition whether behavior is habitual or goal-directed. However, it remains unknown how sequence initiation and termination cues differentially affect reward expectation and contribute to habit learning. Further, while mesolimbic dopamine plays a key role in cue-induced reward expectation and sequence learning, how dynamic changes in dopamine signals differ depending on the response strategy is unclear. Here, we determined how mesolimbic DA signals change over training during cue-mediated sequence learning, depending on the type of cue and the nature of behavioral control. We found sequence initiation and termination cues differentially affect reward expectation during action sequences, with the termination cue contributing to habit and automaticity. Distinct response strategies induced by sequence initiation and termination cues induced differential changes in mesolimbic DA signals that captured variations in reward expectation along sequence execution. Notably, habit-like behavior induced by the sequence termination cue was associated with a rapid shift in DA signals from reward retrieval to the cue. This habit-like behavior was reflected in behavioral inflexibility and attenuated DA reward prediction error signals. Finally, using optogenetics, we provide evidence that phasic DA activity elicited by the sequence termination cue is critical for the development of habit-like behavior.

Adaptive Position Control of Electrohydraulic Servo Systems with Parameter Uncertainty using Artificial Bee Colony Optimization Algorithm

In this paper, we present a robust adaptive backstepping-based controller for precise positioning of the spool valve in an Electro-Hydraulic Servo System (EHSS) under conditions of parameter fluctuations. Classical control strategies, such as PID and linear controllers, often struggle with the nonlinearities and parameter uncertainties inherent in EHSS, leading to poor tracking performance and instability. To overcome these limitations, we employ the Artificial Bee Colony (ABC) algorithm to optimize the controller parameters, minimizing both the tracking error and control signal. The proposed controller ensures uniform ultimate boundedness of the error and control signal by utilizing a Lyapunov-based stability criterion, which guarantees that errors do not exceed a predefined bound despite uncertainties and disturbances. Simulation results validate the robustness and effectiveness of the control scheme, even in the presence of parameter variations. Additionally, a comparative analysis with sliding mode control highlights the superior performance of the proposed method, particularly in providing smoother control signals and reducing chattering while ensuring stability.

Reward-driven cerebellar climbing fiber activity influences both neural and behavioral learning.

The cerebellum plays a key role in motor coordination and learning. In contrast with classical supervised learning models, recent work has revealed that CFs can signal reward-predictive information in some behaviors. This raises the question of whether CFs may also operate according to principles similar to those described by reinforcement learning models. To test how CFs operate during reward-guided behavior, and evaluate the role of reward-related CF activity in learning, we have measured CF responses in Purkinje cells of the lateral cerebellum during a Pavlovian task using 2-photon calcium imaging. Specifically, we have performed multi-stimulus experiments to determine whether CF activity meets the requirements of a reward prediction error (rPE) signal for transfer from an unexpected reward to a reward-predictive cue. We find that once CF activity is transferred to a conditioned stimulus, and there is no longer a response to reward, CFs cannot generate learned responses to a second conditioned stimulus that carries the same reward prediction. In addition, by expressing the inhibitory opsin GtACR2 in neurons of the inferior olive, and optically inhibiting these neurons across behavioral training at the time of unexpected reward, we find that the transfer of CF signals to the conditioned stimulus is impaired. Moreover, this optogenetic inhibition also impairs learning, resulting in a deficit in anticipatory lick timing. Together, these results indicate that CF signals can exhibit several characteristics in common with rPEs during reinforcement learning, and that the cerebellum can harness these learning signals to generate accurately timed motor behavior.

Compensation Function Observer-Based Backstepping Sliding-Mode Control of Uncertain Electro-Hydraulic Servo System

Observer-based control is the most commonly used method in the control of electro-hydraulic servo system (EHSS) with uncertainties, but it suffers from the drawback of low accuracy under the influence of large external load forces and disturbances. To address this problem, this paper proposes a novel compensation function observer-based backstepping sliding-mode control (BSMC) approach to achieve high-accuracy tracking control. In particular, the model uncertainties, including nonlinearities, parameter perturbations and external disturbances are analyzed and treated together as a lumped disturbance. Then, a fourth-order compensation function observer (CFO) is constructed, which fully utilizes the system state information to accurately estimate the lumped disturbance. On this basis, the estimate of the lumped disturbance is incorporated into the design of a backstepping sliding-mode controller, allowing the control system to compensate for the disturbance effect. The stability of the closed-loop control system under the CFO and BSMC is rigorously proven through the use of the Lyapunov theory, which guarantees that all the tracking error signals converge exponentially to the origin. Comparative simulations are carried out to show the effectiveness and efficiency of the proposed approach, i.e., compared with PID and ESO-based BSMC methods, the tracking accuracy is respectively improved by 94.86% and 88.19% under the influence of large external load forces and disturbances.

Online decoupling of the time-varying longitudinal feedback loops for improved performance in Advanced Virgo Plus*

Abstract Gravitational Wave detectors require low-noise sensors combined with high-performance feedback loops to maximize the detector sensitivity in the low-frequency detection range. Some feedback loops in the detector are strongly coupled and their coupling varies over time, which is inherent to the optical configuration and the optical readout scheme used to obtain error signals for the feedback loops. This paper presents a control-based approach to deal with the time-varying interaction among three of the longitudinal degrees of freedom in the Advanced Virgo Plus detector, using the pre-existing infrastructure of the detector. An intuitive Multiple-Input Multiple-Output stability criterion is presented that illustrates how the time-varying coupling affects the stability of the feedback loops, as well as a method that identifies the coupling online and updates a decoupling matrix accordingly. Experiment results of the proposed method on the Advanced Virgo Plus detector illustrate continuous minimization of the coupling over time. Using the derived stability criterion, it is furthermore shown that the coupling is sufficiently minimized such that the interaction terms can be neglected in the control design, resulting in an improved controller performance.

Propofol-mediated loss of consciousness disrupts predictive routing and local field phase modulation of neural activity

Predictive coding is a fundamental function of the cortex. The predictive routing model proposes a neurophysiological implementation for predictive coding. Predictions are fed back from the deep-layer cortex via alpha/beta (8 to 30 Hz) oscillations. They inhibit the gamma (40 to 100 Hz) and spiking that feed sensory inputs forward. Unpredicted inputs arrive in circuits unprepared by alpha/beta, resulting in enhanced gamma and spiking. To test the predictive routing model and its role in consciousness, we collected data from intracranial recordings of macaque monkeys during passive presentation of auditory oddballs before and after propofol-mediated loss of consciousness (LOC). In line with the predictive routing model, alpha/beta oscillations in the awake state served to inhibit the processing of predictable stimuli. Propofol-mediated LOC eliminated alpha/beta modulation by a predictable stimulus in the sensory cortex and alpha/beta coherence between sensory and frontal areas. As a result, oddball stimuli evoked enhanced gamma power, late period (>200 ms from stimulus onset) spiking, and superficial layer sinks in the sensory cortex. LOC also resulted in diminished decodability of pattern-level prediction error signals in the higher-order cortex. Therefore, the auditory cortex was in a disinhibited state during propofol-mediated LOC. However, despite these enhanced feedforward responses in the auditory cortex, there was a loss of differential spiking to oddballs in the higher-order cortex. This may be a consequence of a loss of within-area and interareal spike-field coupling in the alpha/beta and gamma frequency bands. These results provide strong constraints for current theories of consciousness.

Design of Neural Network-Based Multi-Fault Tolerant Control System for Unmanned Aerial Vehicles

Actuator failure seriously threatens the flight safety of unmanned helicopters. Considering the problem of multiple faults such as actuator bias and failure in unmanned helicopters, a composite fault-tolerant flight control algorithm is proposed. For actuator bias fault, a nonlinear fault observer is designed to estimate it in real time; for actuator failure fault, a same-dimensional auxiliary system is constructed and processed by neural network technology. The composite fault-tolerant flight controller of the unmanned helicopter is designed by backstepping method, and the Lyapunov stability theory is used to prove that the error signals of the closed-loop system are bounded and convergent. Simulation results show that the proposed control algorithm can improve the fault tolerance of the unmanned helicopter when multiple actuator faults occur, ensuring its safe flight.