For a category of unknown discrete-time systems with non-Gaussian sampling interval distributions, this article presents an optimal controller built using reinforcement learning (RL). MiFRENc and MiFRENa architectures are respectively utilized for the construction of the actor network and the critic network. The developed learning algorithm's learning rates are determined from the convergence analysis of internal signals and the tracking errors. Experimental setups featuring comparative controllers were used to evaluate the proposed strategy. Comparative analysis of the outcomes demonstrated superior performance for non-Gaussian distributions, excluding weight transfer in the critic network. The learning laws, employing the approximated co-state, lead to a significant improvement in dead-zone compensation and nonlinear variation.

A widely employed bioinformatics tool, the Gene Ontology (GO), serves to describe proteins' diverse biological processes, molecular functions, and cellular locations. Biobased materials Within a directed acyclic graph, there exist over 5,000 hierarchically structured terms, with corresponding known functional annotations. The automated annotation of protein functions with computational models rooted in Gene Ontology (GO) has been a continuing area of intensive study. The complex topological structures of GO, coupled with the limited functional annotation information, prevent existing models from effectively representing the knowledge within GO. We devise a method based on the functional and topological attributes of GO to support the prediction of protein function for this problem. A multi-view GCN model within this method serves to extract a multitude of GO representations from a confluence of functional information, topological structure, and their combinations. Dynamically determining the weightings of these representations is accomplished through an attention mechanism, which ultimately results in the final knowledge representation of GO. Moreover, biologically relevant characteristics for each protein sequence are learned efficiently through the application of a pre-trained language model, for example, ESM-1b. In conclusion, predicted scores are ascertained through the calculation of the dot product between sequence features and GO representations. Our method exhibits superior performance compared to existing state-of-the-art methods, as empirically verified through experimentation across datasets derived from Yeast, Human, and Arabidopsis. Our proposed method's implementation code is situated at https://github.com/Candyperfect/Master, accessible via the GitHub platform.

A radiation-free, photogrammetric 3D surface scan-based approach shows promise in diagnosing craniosynostosis, replacing the need for traditional computed tomography. We propose using convolutional neural networks (CNNs) for an initial classification of craniosynostosis, facilitated by converting 3D surface scans to 2D distance maps. Employing 2D images presents several benefits, such as maintaining patient privacy, enabling data enhancement during the training phase, and exhibiting a strong under-sampling strategy for the 3D surface, coupled with exceptional classification outcomes.
Via coordinate transformation, ray casting, and distance extraction, the proposed distance maps collect samples of 2D images from 3D surface scans. This work details a convolutional neural network-based classification approach, evaluating its performance against alternative strategies on a dataset of 496 patients. We explore the impacts of low-resolution sampling, data augmentation, and the mapping of attributions.
ResNet18 demonstrated superior classification capabilities compared to other models on our dataset, marked by an F1-score of 0.964 and an accuracy of 98.4%. A substantial performance gain was observed for all classifiers after augmenting data originating from 2D distance maps. A 256-fold reduction in computational complexity was observed in ray casting when under-sampling was applied, with an F1-score of 0.92 being maintained. Significant amplitudes were observed in the attribution maps of the frontal head region.
Through a flexible mapping approach, we extracted a 2D distance map from the 3D head's geometry, leading to improved classification performance. This methodology allowed for the use of data augmentation during training on 2D distance maps, combined with convolutional neural networks. Good classification performance was attained with low-resolution images, according to our observations.
Photogrammetric surface scans serve as an appropriate diagnostic tool for craniosynostosis in clinical settings. The transition of domain applications to computed tomography holds the potential to contribute to lower ionizing radiation exposure for infants.
Photogrammetric surface scans provide a suitable clinical diagnostic approach to craniosynostosis. A transfer of domain knowledge to computed tomography is possible, and it could further decrease the amount of ionizing radiation exposure for infants.

A comprehensive assessment of cuffless blood pressure (BP) measurement techniques was undertaken on a large and diverse study population in this study. 3077 participants (18-75 years old, 65.16% female, and 35.91% hypertensive) were enrolled, and a follow-up examination was completed over approximately one month. Electrocardiogram, pulse pressure wave, and multiwavelength photoplethysmogram readings were synchronously collected using smartwatches; dual-observer auscultation furnished the reference systolic and diastolic blood pressure measurements. Pulse transit time, traditional machine learning (TML) algorithms, and deep learning (DL) models were examined under conditions of both calibration and calibration-free operation. TML models, built upon ridge regression, support vector machines, adaptive boosting, and random forests, stood in contrast to DL models, which employed convolutional and recurrent neural networks. Among the calibration-based models assessed, the most accurate model revealed DBP estimation errors reaching 133,643 mmHg and SBP estimation errors of 231,957 mmHg across all participants. Substantial reductions in SBP errors were observed within the normotensive (197,785 mmHg) and younger (24,661 mmHg) segments of the population. Estimation errors for DBP in the top-performing calibration-free model were -0.029878 mmHg, while the corresponding errors for SBP were -0.0711304 mmHg. We find smartwatches to be effective for measuring diastolic blood pressure (DBP) in all study participants, and systolic blood pressure (SBP) in normotensive and younger participants, provided calibration is performed. However, performance significantly declines when assessing heterogeneous groups, such as older or hypertensive individuals. Routine medical environments often present limitations in the accessibility of calibration-free cuffless blood pressure measurement. read more In our large-scale benchmark study on cuffless blood pressure measurement, we highlight the need for exploring more signals and principles to improve accuracy in diverse and heterogeneous patient populations.

The process of segmenting the liver from CT scans is vital for computational support in diagnosing and treating liver ailments. The 2DCNN, in contrast, overlooks the spatial depth, whereas the 3DCNN faces problems of excessive parameters and computational expenditure. To surmount this restriction, we propose the Attentive Context-Enhanced Network (AC-E Network), composed of 1) an attentive context encoding module (ACEM) that can be integrated into the 2D backbone, extracting 3D context without a substantial increase in parameters; 2) a dual segmentation branch incorporating a complementary loss, allowing the network to focus on both the liver region and its boundary, thereby achieving precise liver surface segmentation. Evaluated against the LiTS and 3D-IRCADb datasets, our approach surpasses existing methods and performs on par with the state-of-the-art 2D-3D hybrid technique, achieving a balanced performance between segmentation accuracy and the number of model parameters.

Crowded scenes pose a significant difficulty for computer vision systems attempting to detect pedestrians, as the overlapping of pedestrians often hinders accurate identification. Employing the non-maximum suppression (NMS) technique is crucial in eliminating extraneous false positive detection proposals, thereby maintaining the accuracy of true positive detection proposals. However, the results exhibiting significant overlap may be discarded if the non-maximum suppression threshold is lowered. Simultaneously, a more demanding NMS standard will generate a more significant number of false positive detections. The optimal threshold prediction (OTP) NMS approach, which forecasts an appropriate NMS threshold for each human instance, offers a solution to this challenge. For the purpose of obtaining the visibility ratio, a visibility estimation module is formulated. To automatically determine the ideal NMS threshold, we propose a threshold prediction subnet, leveraging the visibility ratio and classification score. Calanopia media Last, we revise the subnet's objective function, subsequently applying the reward-driven gradient estimation algorithm to update the subnet's parameters. Benchmarking the proposed pedestrian detection approach using CrowdHuman and CityPersons datasets yields superior results, especially in dense pedestrian crowds.

Our paper proposes novel additions to the JPEG 2000 standard, tailored for encoding discontinuous media, exemplified by piecewise smooth imagery such as depth maps and optical flows. These extensions utilize breakpoints to model discontinuity boundary geometries, subsequently applying a breakpoint-dependent Discrete Wavelet Transform (BP-DWT) for processing. The JPEG 2000 compression framework's highly scalable and accessible coding features are maintained by our proposed extensions, which encode the breakpoint and transform components as independent bit streams for progressive decoding. Visual examples, alongside comparative rate-distortion results, illustrate the benefits of breakpoint representations coupled with BD-DWT and embedded bit-plane coding. Our recently proposed extensions have been accepted and are currently undergoing the publication process, slated to become a new Part 17 of the JPEG 2000 coding standards.