Complex system nonlinearity is modeled using PNNs. Particle swarm optimization (PSO) is incorporated for the optimization of parameters when creating recurrent predictive neural networks. The integration of RF and PNNs within RPNNs results in high accuracy, attributed to the ensemble learning methods employed in the RF portion, and enables the powerful description of high-order nonlinear dependencies between input and output variables, as a consequence of the PNN component. The proposed RPNNs, as demonstrated by experimental results across a selection of well-regarded modeling benchmarks, consistently outperform previously reported state-of-the-art models in the literature.
Mobile devices, now equipped with integrated intelligent sensors, have made the implementation of detailed human activity recognition (HAR), employing lightweight sensors, a valuable method for personalized applications. Past decades have seen numerous shallow and deep learning algorithms developed for human activity recognition, yet these methods often prove inadequate in harnessing the semantic information embedded in data collected from multiple sensor types. To overcome this constraint, we introduce a novel HAR framework, DiamondNet, capable of generating diverse multi-sensor data streams, removing noise, extracting, and integrating features from a unique viewpoint. DiamondNet effectively extracts robust encoder features by employing multiple 1-D convolutional denoising autoencoders (1-D-CDAEs). To further develop heterogeneous multisensor modalities, we introduce an attention-based graph convolutional network, which dynamically leverages the interconnections between various sensors. Furthermore, the proposed attentive fusion sub-network, utilizing a global attention mechanism alongside shallow features, adeptly adjusts the various levels of features from multiple sensor modalities. The approach to HAR's perception benefits from amplified informative features, creating a comprehensive and robust understanding. Using three publicly available datasets, the efficacy of the DiamondNet framework is tested and validated. Through rigorous experimentation, the results conclusively show DiamondNet exceeding other cutting-edge baselines, resulting in remarkable and consistent enhancements in accuracy. In sum, our research presents a fresh viewpoint on HAR, utilizing the strengths of various sensor inputs and attention mechanisms to markedly enhance performance.
The synchronization of discrete Markov jump neural networks (MJNNs) forms the core topic of this article. For optimized communication, a universal model is proposed, featuring event-triggered transmission, logarithmic quantization, and asynchronous phenomena, thereby mimicking actual situations. Developing a more encompassing event-driven protocol, conservatism is reduced by incorporating a diagonal matrix to define the threshold parameter. To compensate for the discrepancies in node and controller modes, arising from potential temporal lags and packet loss events, a hidden Markov model (HMM) strategy is applied. With the awareness that state information from nodes may not be accessible, asynchronous output feedback controllers are developed using a novel decoupling scheme. Leveraging Lyapunov's stability theory, we present sufficient conditions in the form of linear matrix inequalities (LMIs) for achieving dissipative synchronization within multiplex jump neural networks (MJNNs). Removing asynchronous terms yields a corollary with lower computational cost; this is the third point. To conclude, two numerical illustrations exemplify the efficacy of the preceding findings.
This analysis probes the stability characteristics of neural networks impacted by time-varying delays. Employing free-matrix-based inequalities and introducing variable-augmented-based free-weighting matrices, the derivation of novel stability conditions for the estimation of the derivative of Lyapunov-Krasovskii functionals (LKFs) is facilitated. Using both techniques, the non-linear time-varying delay components are masked from view. native immune response The presented criteria are improved through the amalgamation of the time-varying free-weighting matrices linked to the delay's derivative, and the time-varying S-Procedure relating to the delay and its derivative. To demonstrate the value of the proposed methods, a series of numerical examples are provided.
Minimizing the extensive commonalities within video sequences is the primary goal of video coding algorithms. BODIPY 581/591 C11 Chemical Compared to previous standards, each new video coding standard provides tools for more effective performance of this task. Modern video coding systems, adopting block-based approaches, use commonality modeling exclusively on the forthcoming block needing encoding. This research argues for a commonality modeling technique that enables a smooth interweaving of global and local motion homogeneity. In order to predict the current frame, the frame needing encoding, a two-step discrete cosine basis-oriented (DCO) motion modeling is first carried out. The DCO motion model's superior ability to represent sophisticated motion fields through a smooth and sparse representation makes it a more suitable choice compared to traditional translational or affine models. Furthermore, the proposed two-stage motion modeling strategy can lead to enhanced motion compensation while simultaneously decreasing computational intricacy, because a well-informed initial estimate is devised for initiating the motion search algorithm. Subsequently, the current frame is partitioned into rectangular spaces, and the adherence of these spaces to the learned motion model is investigated. If the estimated global motion model exhibits inconsistencies, a secondary DCO motion model is introduced to ensure a more consistent local motion pattern. The proposed method's output is a motion-compensated prediction of the current frame, deriving from reducing the commonalities in both global and local motion. A reference HEVC encoder, augmented with the DCO prediction frame as a reference point for encoding current frames, has exhibited a substantial improvement in rate-distortion performance, with bit-rate savings as high as approximately 9%. A noteworthy 237% bit rate reduction is observed when employing the versatile video coding (VVC) encoder, in contrast to more modern video coding standards.
Mapping chromatin interactions is indispensable for advancing knowledge in the field of gene regulation. Nevertheless, high-throughput experimental methodologies' restrictions underscore the immediate requirement for computational techniques to predict chromatin interactions. IChrom-Deep, a novel attention-based deep learning model, is proposed in this study for the purpose of identifying chromatin interactions, drawing upon sequence and genomic features. Satisfactory performance is a hallmark of IChrom-Deep, as evidenced by experimental results based on datasets from three cell lines, demonstrably superior to previous methods. We also investigate the interplay of DNA sequence characteristics and genomic features with chromatin interactions, emphasizing how features like sequence conservation and positional information apply in various scenarios. In addition, we discover a handful of genomic features that are extremely important across different cellular lineages, and IChrom-Deep performs comparably using just these crucial genomic features rather than all genomic features. IChrom-Deep is anticipated to be a beneficial tool for future investigations into the identification of chromatin interactions.
REM sleep behavior disorder (RBD), a parasomnia, is recognized by the acting out of dreams during REM sleep, accompanied by the absence of atonia. The process of diagnosing RBD using manually scored polysomnography (PSG) data is time-consuming. A high probability of Parkinson's disease is frequently linked to the existence of isolated RBD (iRBD). The assessment of iRBD predominantly relies on a clinical evaluation, combined with subjective REM sleep stage ratings from polysomnography, specifically noting the absence of atonia. In this study, we present a novel spectral vision transformer (SViT), for the first time applied to PSG signals to detect Rapid Eye Movement Behavior Disorder (RBD). We then assess its performance relative to the performance of a more typical convolutional neural network approach. The application of vision-based deep learning models to scalograms (30 or 300 seconds) of PSG data (EEG, EMG, and EOG) led to predictions that were interpreted. In the study, a 5-fold bagged ensemble approach was adopted for the analysis of 153 RBDs (96 iRBDs and 57 RBDs with PD), along with 190 controls. The SViT interpretation, using integrated gradients, was done in a manner considering sleep stage averages per patient. Regarding the test F1 score, there was little variation between the models per epoch. While other models fell short, the vision transformer performed exceptionally well on a per-patient basis, boasting an F1 score of 0.87. When training the SViT model on selected channels, an F1 score of 0.93 was achieved using a combined EEG and EOG dataset. Dermal punch biopsy Although EMG is anticipated to offer the most comprehensive diagnostic information, the model's output highlights EEG and EOG as crucial factors, implying their integration into RBD diagnosis procedures.
Computer vision's most basic tasks include object detection. Object detection approaches commonly leverage dense object proposals, k pre-defined anchor boxes distributed across all grid points of an image feature map, with height and width dimensions. Using a very simple and sparse approach, Sparse R-CNN is introduced in this paper for detecting objects in images. Learned object proposals, fixed in number at N, are supplied to the object recognition head in our method for the task of classification and localization. Sparse R-CNN obviates the entire process of object candidate design and one-to-many label assignments, substituting HWk (ranging up to hundreds of thousands) manually crafted object candidates with N (such as 100) learnable proposals. Essentially, Sparse R-CNN's output is immediate predictions, eschewing the subsequent non-maximum suppression (NMS) procedure.