Complementary Coding Research Articles

Hippocampal Cognitive Function Based on Deep Learning

Abstract This research focuses on the study of agent behavior decision-making based on hippocampal cognitive functions, aiming to enhance the decision-making capabilities of agents in complex task environments by deeply exploring the crucial role of the hippocampus in learning, memory, and cognitive processes. By drawing inspiration from the biological structure and functional characteristics of the hippocampus, researchers are dedicated to designing and developing more intelligent and adaptive decision-making models to enhance agents' behavioral performance, problem-solving abilities, and adaptability to new situations. To achieve this goal, the research integrates advanced artificial intelligence technologies such as reinforcement learning and deep learning to simulate the complex functions of the hippocampus in memory encoding, storage, retrieval, and cognitive reasoning. This research not only contributes to advancing intelligent systems towards higher levels of intelligence and personalization but also plays a significant role in improving the interaction between intelligent agents and humans, providing intelligent services that better meet user needs. We found that the neural network trained in multi-task learning benefits from a loss term that promotes relevant and irrelevant representations. Therefore, the complementary coding we found in CA3 can provide extensive computational advantages for solving complex tasks. Furthermore, the study emphasizes the importance of further elucidating the functional mechanisms of the hippocampus, with the expectation of providing a more solid theoretical foundation for the optimization and refinement of agent decision-making models in the future.

A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding

A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding

Cross-Channel Color Image Encryption Scheme Based on Discrete Memristive Coupled Neurons and DWT Compression

To address the consumption and security of color images for transmission and storage, a cross-channel color image encryption scheme based on a discrete memristive coupled neuron model and DWT compression is designed in this article. Firstly, the dynamics of the discrete memristive coupled neuron system are analyzed and found to possess the hyperchaotic phenomenon, which provides sufficient security for the encryption scheme. Secondly, the color image processed by discrete wavelet transform (DWT) has a quarter of the previous capacity. Then, the color image is combined with a Hash function, and the resulting Hash sequence is given the initial value of the hyperchaotic system. Next, a particle swarm foraging algorithm (PSFA) is designed to better disrupt the correlation in the RGB channel. Finally, a complementary DNA coding rule is implemented for the further encryption of color images. Simulation results show that even with DWT lossy compression, the recovered image can be clearly seen. The performance analysis illustrates that under the hyperchaotic system, the proposed encryption algorithm brings higher security for color images.

The Attachment Script Assessment: Introduction of a coding system to evaluate deactivation, hyperactivation, and anomalous content

ABSTRACT Although research on adult attachment has yielded insight into the legacy of attachment for functioning in adulthood, methodological challenges persist in the assessment of adult attachment. The Adult Attachment Interview (AAI) offers a rich assessment of secure, insecure, and unresolved states of mind. However, it is resource intensive to administer and code. Attachment Script Assessment (ASA) offers a resource-effective alternative to the AAI. However, the ASA coding system only yields a single, security-like dimension: secure base script knowledge. Here, we introduce a complementary coding system for the ASA to assess attachment deactivation (i.e. script characterized by limited interpersonal connection and minimization of attachment problems/emotions), hyperactivation (i.e. script in which attachment-relevant problems and negative emotions are heightened), and anomalous content (i.e. script in which attachment problems contain elements of fear and/or disorientation); and we discuss the conceptual convergence of these scripts with corresponding patterns of attachment insecurity and disorganization.

Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning

Vertical Federated Learning (VFL) enables an active party with labeled data to enhance model performance (utility) by collaborating with multiple passive parties that possess auxiliary features corresponding to the same sample identifiers (IDs). Model serving in VFL is vital for real-world, delay-sensitive applications, and it faces two major challenges: 1) robustness against arbitrarily-aligned data and stragglers; and 2) privacy protection, ensuring minimal label leakage to passive parties. Existing methods fail to transfer knowledge among parties to improve robustness in a privacy-preserving way. In this paper, we introduce a privacy-preserving knowledge transfer framework, Complementary Knowledge Distillation (CKD), designed to enhance the robustness and privacy of multi-party VFL systems. Specifically, we formulate a Complementary Label Coding (CLC) objective to encode only complementary label information of the active party's local model for passive parties to learn. Then, CKD selectively transfers the CLC-encoded complementary knowledge 1) from the passive parties to the active party, and 2) among the passive parties themselves. Experimental results on four real-world datasets demonstrate that CKD outperforms existing approaches in terms of robustness against arbitrarily-aligned data, while also minimizing label privacy leakage.

Snapshot-Type Polarization Spectral Imaging Resolution Enhancement Based on Dual-Channel Complementary Microarray Coding Imaging Fusion

Snapshot-type polarization spectral imaging is a crucial dynamic high-speed target detection method in remote sensing in space because it has a high temporal resolution. However, the sparse representation of snapshot-type imaging in spectral or spatial dimension leads to low spectral or spatial resolution. To resolve the problem we propose double-channel complementary microarray coding snapshot-type polarization spectral imaging by utilizing the transmission and reflection complementary principle of a single microarray coding plate, the dual-channel multispectral and hyper-spectral image fused to enhancing the resolution of polarization spectral imaging. This study combined with the principle of intensity modulation used in full polarization measurements, and established the spectral modulation and reconstruction model of dual-channel complementary microarray coding. According to the principle of dual-channel complementary snapshot polarization spectral imaging proposed in this paper, the dual-channel multispectral and hyper-spectral image fusion model was setted up. This study improved the snapshot polarization spectral imaging spectral and spatial resolutions, increased the spatial resolution PSNR by 3 dB, and made the spectral resolution significantly higher. The findings of this study can assist in achieving high temporal resolution, high spectral resolution, high spatial resolution, and fully polarized snapshot-type polarization spectral imaging.

A Novel Composite Coding Method for Incoherent Scatter Radar

Many modern ionospheric studies rely on incoherent scatter radars (ISR) since this kind of radar is able to detect various ionospheric parameters over very long ranges. The performance of ISR significantly depends on its coding system. In recent decades, a new type of coding system, which is the so-called composite coding, was presented. It used to be constructed by using a certain code to modulate alternating code to achieve better detection resolution and anti-noise performance for ISRs. In the present study, a new composite coding system was presented, which is constructed based on complementary codes and alternating codes. In this paper, the performance of the new composite code will be compared with that of several traditional codes to show that the new composite code can help to improve the detection performance of the ISR. According to the analysis based on the ambiguity function, the present composite coding system helps to improve the range resolution and detection range for ISR detections. In addition, numerical tests on anti-noise performance show that the complementary composite coding system has a good anti-noise performance and helps to reduce the necessary times of incoherent integration. As a result, the composite coding system can improve the time resolution.

A Representational Similarity Analysis of Cognitive Control during Color-Word Stroop.

Progress in understanding the neural bases of cognitive control has been supported by the paradigmatic color-word Stroop task, in which a target response (color name) must be selected over a more automatic, yet potentially incongruent, distractor response (word). For this paradigm, models have postulated complementary coding schemes: dorsomedial frontal cortex (DMFC) is proposed to evaluate the demand for control via incongruency-related coding, whereas dorsolateral PFC (DLPFC) is proposed to implement control via goal and target-related coding. Yet, mapping these theorized schemes to measured neural activity within this task has been challenging. Here, we tested for these coding schemes relatively directly, by decomposing an event-related color-word Stroop task via representational similarity analysis. Three neural coding models were fit to the similarity structure of multivoxel patterns of human fMRI activity, acquired from 65 healthy, young-adult males and females. Incongruency coding was predominant in DMFC, whereas both target and incongruency coding were present with indistinguishable strength in DLPFC. In contrast, distractor information was strongly encoded within early visual cortex. Further, these coding schemes were differentially related to behavior: individuals with stronger DLPFC (and lateral posterior parietal cortex) target coding, but weaker DMFC incongruency coding, exhibited less behavioral Stroop interference. These results highlight the utility of the representational similarity analysis framework for investigating neural mechanisms of cognitive control and point to several promising directions to extend the Stroop paradigm.SIGNIFICANCE STATEMENT How the human brain enables cognitive control - the ability to override behavioral habits to pursue internal goals - has been a major focus of neuroscience research. This ability has been frequently investigated by using the Stroop color-word naming task. With the Stroop as a test-bed, many theories have proposed specific neuroanatomical dissociations, in which medial and lateral frontal brain regions underlie cognitive control by encoding distinct types of information. Yet providing a direct confirmation of these claims has been challenging. Here, we demonstrate that representational similarity analysis, which estimates and models the similarity structure of brain activity patterns, can successfully establish the hypothesized functional dissociations within the Stroop task. Representational similarity analysis may provide a useful approach for investigating cognitive control mechanisms.

Spatial-Time Complementary Coding Signal Design for RadCom System

Sharing signal design is an important challenge in radar communication integrated (RadCom) system. In this paper, a spatial-time complementary coding signal design method is proposed. Coding signal generation, radar receive processing and communication receive processing are presented sequentially. Numerical simulation results show that the proposed method achieves better sidelobe reduction than ordinary random coding signals.

Photonic generation of microwave binary modulation signals with high frequency multiplication factors

An approach for photonic generation of binary modulation microwave signals with high frequency multiplication factors is proposed and investigated. The key components are a polarization division multiplexing (PDM) dual parallel Mach–Zehnder modulator (DPMZM) and a polarization modulator (PolM). The PDM-DPMZM is employed to produce two orthogonally polarized lightwaves with their polarization directions aligned with the two principle axes of the following PolM. Then the two lightwaves are sent into the PolM for complementary phase coding. A theoretical analysis and a simulation work are performed to demonstrate the generation of microwave amplitude shift keying (ASK) signal with frequency multiplication factor of eight, frequency shift keying (FSK) signal with frequency multiplication factors of four/eight and phase shift keying (PSK) signal with frequency multiplication factor of four. The modulation format can be switched simply by adjusting the system parameters without revising the structure. In addition, the bit rate and the carrier frequency can be tuned flexibly and independently, and good frequency tunability is realized.

Development of an internal physiological muscle controller within an open‐source Hill‐type material model in LS‐DYNA

AbstractNowadays automotive industry is paying more attention to vehicle safety systems with the aim to reduce the number of injuries and fatalities among road users. Simulations are used throughout the whole research and development process for such systems involving Finite Element Human Body Models (FE HBMs) and the multiphysics simulation software package LS‐DYNA. Because the safety of living (active) humans in traffic situations has to be assured, special material models are applied to generate reflexive or voluntary muscle reactions of the FE HBMs. These elements are governed by an application‐dependent controller, which produce individual activation signals for each of them. The current contribution presents the development of a physiologically inspired controller for muscle elements, enabling the simulation of voluntary or reflexive movements with the FE HBM. As a result, it is possible to control muscle elements internally without any additional superior controller level or complementary coding with LS‐DYNA keywords. A validation example with the calculation of appropriate muscle activation levels through optimization of the controller parameters is given.

Hb Palencia: a novel δβδ-type two-way fusion variant with β-globin-like expression levels

AimsFusion proteins of unequal recombination events at the β-globin locus have pathological effect. The haemoglobin (Hb) variants of type Lepore are fusion proteins characterised by β-like globin chains with a...

SNR-Enhanced Fast BOTDA Combining Channel Estimation Technique With Complementary Pulse Coding

SNR-Enhanced Fast BOTDA Combining Channel Estimation Technique With Complementary Pulse Coding

Chaotic Phase-Coded Waveforms With Space-Time Complementary Coding for MIMO Radar Applications

A framework for designing orthogonal chaotic phase-coded waveforms with space-time complementary coding is proposed for multiple-input multiple-output (MIMO) radar applications. The phase-coded waveform set to be transmitted is generated with an arbitrary family size and an arbitrary code length by using chaotic sequences. Due to the properties of chaos, this chaotic waveform set has many advantages in performance, such as anti-interference and low probability of intercept. However, it cannot be directly exploited due to the high range sidelobes, mutual interferences, and Doppler intolerance. In order to widely implement it in practice, we optimize the chaotic phase-coded waveform set from two aspects. First, the autocorrelation property of the waveform is improved by transmitting complementary chaotic phase-coded waveforms, and an adaptive clonal selection algorithm is utilized to optimize a pair of complementary chaotic phase-coded pulses. Second, the crosscorrelation among different waveforms is eliminated by implementing space-time coding into the complementary pulses. Moreover, to enhance the detection ability for moving targets in MIMO radars, a method of weighting different pulses by a null space vector is utilized at the receiver to compensate the interpulse Doppler phase shift and accumulate different pulses coherently. Simulation results demonstrate the efficiency of our proposed method.

Genetic coding algorithm for sense and antisense peptide interactions

Sense and antisense peptides, i.e. peptides specified by complementary DNA and RNA sequences, interact with increased probability. Biro, Blalock, Mekler, Root-Bernstein and Siemion investigated the recognition rules of peptide—peptide interaction based on the complementary coding of DNA and RNA sequences in 3′→5′ and 5′→3′ directions. After more than three decades of theoretical and experimental investigations, the efficiency of this approach to predict peptide—peptide binding has been experimentally verified for more than 50 ligand—receptor systems, and represents a promising field of research. The natural genetic coding algorithm for sense and antisense peptide interactions combines following elements: of amino acid physico-chemical properties, stereochemical interaction, and bidirectional transcription. The interplay of these factors influences the specificity of sense—antisense peptide interactions, and affects the selection and evolution of peptide ligand—receptor systems. Complementary mRNA codon—tRNA anticodon complexes, and recently discovered Carter-Wolfenden tRNA acceptor-stem code, provide the basis for the rational modeling of peptide interactions based on their hydrophobic and lipophilic amino acid physico-chemical properties. It is shown that the interactions of complementary amino acid pairs according to the hydrophobic and lipophilic properties strongly depend on the central (second) purine base of the mRNA codon and its pyrimidine complement of the tRNA anticodon. This enables the development of new algorithms for the analysis of structure, function and evolution of protein and nucleotide sequences that take into account the residue's tendency to leave water and enter a nonpolar condensed phase considering its mass, size and accessible surface area. The practical applications of the sense—antisense peptide modeling are illustrated using different interaction assay types based on: microscale thermophoresis (MST), tryptophan fluorescence spectroscopy (TFS), nuclear magnetic resonance spectroscopy (NMR), and magnetic particles enzyme immunoassay (MPEIA). Various binding events and circumstances were considered, e.g., in situations with—short antisense peptide ligand (MST), L- and D-enantiomer acceptors (TFS), in low affinity conditions (NMR), and with more than one antisense peptide targeting hormone (MPEIA).

Complementary codes for odor identity and intensity in olfactory cortex.

The ability to represent both stimulus identity and intensity is fundamental for perception. Using large-scale population recordings in awake mice, we find distinct coding strategies facilitate non-interfering representations of odor identity and intensity in piriform cortex. Simply knowing which neurons were activated is sufficient to accurately represent odor identity, with no additional information about identity provided by spike time or spike count. Decoding analyses indicate that cortical odor representations are not sparse. Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations. Cortical inhibition transforms olfactory bulb output to sharpen these dynamics. Our data therefore reveal complementary coding strategies that can selectively represent distinct features of a stimulus.

Grid and Nongrid Cells in Medial Entorhinal Cortex Represent Spatial Location and Environmental Features with Complementary Coding Schemes

The medial entorhinal cortex (mEC) has been identified as a hub for spatial information processing by the discovery of grid, border, and head-direction cells. Here we find that in addition to these well-characterized classes, nearly all of the remaining two-thirds of mEC cells can be categorized as spatially selective. We refer to these cells as nongrid spatial cells and confirmed that their spatial firing patterns were unrelated to running speed and highly reproducible within the same environment. However, in response to manipulations of environmental features, such as box shape or box color, nongrid spatial cells completely reorganized their spatial firing patterns. At the same time, grid cells retained their spatial alignment and predominantly responded with redistributed firing rates across their grid fields. Thus, mEC contains a joint representation of both spatial and environmental feature content, with specialized cell types showing different types of integrated coding of multimodal information.

Noise-constrained switching times for heteroclinic computing.

Heteroclinic computing offers a novel paradigm for universal computation by collective system dynamics. In such a paradigm, input signals are encoded as complex periodic orbits approaching specific sequences of saddle states. Without inputs, the relevant states together with the heteroclinic connections between them form a network of states-the heteroclinic network. Systems of pulse-coupled oscillators or spiking neurons naturally exhibit such heteroclinic networks of saddles, thereby providing a substrate for general analog computations. Several challenges need to be resolved before it becomes possible to effectively realize heteroclinic computing in hardware. The time scales on which computations are performed crucially depend on the switching times between saddles, which in turn are jointly controlled by the system's intrinsic dynamics and the level of external and measurement noise. The nonlinear dynamics of pulse-coupled systems often strongly deviate from that of time-continuously coupled (e.g., phase-coupled) systems. The factors impacting switching times in pulse-coupled systems are still not well understood. Here we systematically investigate switching times in dependence of the levels of noise and intrinsic dissipation in the system. We specifically reveal how local responses to pulses coact with external noise. Our findings confirm that, like in time-continuous phase-coupled systems, piecewise-continuous pulse-coupled systems exhibit switching times that transiently increase exponentially with the number of switches up to some order of magnitude set by the noise level. Complementarily, we show that switching times may constitute a good predictor for the computation reliability, indicating how often an input signal must be reiterated. By characterizing switching times between two saddles in conjunction with the reliability of a computation, our results provide a first step beyond the coding of input signal identities toward a complementary coding for the intensity of those signals. The results offer insights on how future heteroclinic computing systems may operate under natural, and thus noisy, conditions.

Enhancing the performance of BOTDR based on the combination of FFT technique and complementary coding.

We implement a BOTDR sensor that combines the complementary coding with the fast Fourier transform (FFT) technique for high-performance distributed sensing. The employment of the complementary coding provides an enhanced signal-to-noise ratio of the sensing system, which leads to high accuracy measurement. Meanwhile, FFT technique in BOTDR is combined to reduce the measurement time sharply compared to the classical frequency sweeping technique. In addition, a pre-depletion two-wavelength probe pulse is proposed to suppress the distortion of the coding probe pulse induced by EDFA. Experiments are carried out beyond 10 km single-mode fiber, and the results show the capabilities of the proposed scheme to achieve 2 m spatial resolution with 0.37 MHz frequency uncertainty which corresponds to ∼0.37 °C temperature resolution or ∼7.4 με strain resolution. The measurement time can be more than tens of times faster than traditional frequency sweeping method in theory.

An image denoising algorithm for mixed noise combining nonlocal means filter and sparse representation technique

Nonlocal means (NLM) filtering or sparse representation based denoising method has obtained a remarkable denoising performance. In order to integrate the advantages of two methods into a unified framework, we propose an image denoising algorithm through skillfully combining NLM and sparse representation technique to remove Gaussian noise mixed with random-valued impulse noise. In the non-Gaussian circumstance, we propose a customized blockwise NLM (CBNLM) filter to generate an initial denoised image. Based on it, we classify the different noisy pixels according to the three-sigma rule. Besides, an overcomplete dictionary is trained on the initial denoised image. Then, a complementary sparse coding technique is used to find the sparse vector for each input noisy patch over the overcomplete dictionary. Through solving a more reasonable variational denoising model, we can reconstruct the clean image. Experimental results verify that our proposed algorithm can obtain the best denoising performance, compared with some typical methods.