Employing a Hough transform framework for convolutional matching, this work introduces a novel geometric matching algorithm, Convolutional Hough Matching (CHM). The method employs geometric transformations to distribute the similarities of candidate matches, and a convolutional evaluation process is used on these transformed similarities. We trained a neural layer, possessing a semi-isotropic high-dimensional kernel, to learn non-rigid matching, with its parameters being both small and interpretable. Improving the efficiency of high-dimensional voting procedures requires an effective approach for kernel decomposition. This technique, centered on the concept of center-pivot neighbors, remarkably reduces the sparsity of the proposed semi-isotropic kernels without compromising overall performance. To verify the proposed techniques, we implemented a neural network with CHM layers that perform convolutional matching, encompassing translational and scaling operations. Employing our approach, a new standard of excellence is achieved on established benchmarks for semantic visual correspondence, showcasing its remarkable robustness against complex intra-class variations.
In the construction of modern deep neural networks, batch normalization (BN) is an essential unit. However, BN and its variants, despite their emphasis on normalization statistics, miss the recovery stage that capitalizes on linear transformations to enhance the ability to adapt to intricate data distributions. This research paper demonstrates the potential for enhanced recovery by utilizing the aggregation of neighboring neurons for each processing unit, instead of relying on singular neuronal units. We introduce BNET, a simple yet effective batch normalization method incorporating enhanced linear transformations, to embed spatial contextual information and boost representational power. Leveraging depth-wise convolution, BNET implementation is simplified and its integration into existing BN architectures is seamless. To the best of our comprehension, BNET is the inaugural effort at augmenting the recovery aspect of BN. TVB-2640 Consequently, BN is classified as a specific instance of BNET, from both a spatial and a spectral standpoint. Results from experimental trials confirm the consistent performance improvements of BNET when deployed across a wide range of visual tasks and different backbones. Moreover, BNET can improve the convergence speed of network training and augment spatial information by awarding higher weights to critical neurons.
Deep learning-based detection models' performance suffers when confronted with adverse weather conditions in practical applications. Before object detection is performed, using image restoration methods to boost the quality of degraded images is a well-established strategy. Still, the development of a positive relationship between these two processes remains a technically demanding issue. The restoration labels prove elusive in the practical application. To this end, we illustrate the concept with the hazy scene and propose the BAD-Net architecture, which unites the dehazing and detection modules within an end-to-end system. A two-branch system, coupled with an attention fusion module, is established for the full combination of hazy and dehazed features. This mechanism allows for resilience in the detection module despite possible lapses in the dehazing module's operation. Beyond that, we introduce a self-supervised haze-resistant loss that facilitates the detection module's capacity to address varying haze severities. Crucially, a strategy for iterative data refinement, specifically within an interval, is proposed to facilitate learning within the dehazing module, leveraging weak supervision. BAD-Net's detection-friendly dehazing strategy results in a further improvement in detection performance. Experiments conducted on the RTTS and VOChaze datasets indicate that BAD-Net achieves a higher accuracy rate than the leading contemporary methods. A robust detection framework bridges the gap between low-level dehazing and high-level detection.
For superior generalization in inter-site ASD diagnosis, models incorporating domain adaptation are introduced to address the variations in data characteristics between sites. However, the majority of existing methods merely focus on reducing the disparity in marginal distributions, without taking into account class-discriminative details, thereby posing challenges to achieving satisfactory results. This paper proposes a multi-source unsupervised domain adaptation method leveraging a low-rank and class-discriminative representation (LRCDR) to concurrently reduce marginal and conditional distribution differences, ultimately leading to improved ASD identification. To address the difference in marginal distributions across domains, LRCDR leverages low-rank representation to align the global structure of the projected multi-site data. To lessen the difference in conditional distributions across data from all sites, LRCDR learns class-discriminative representations from multiple source and the target domains, promoting compact clustering within classes and clear separation between classes in the projected data. Applying LRCDR to inter-site prediction tasks across the entire ABIDE dataset (1102 subjects, 17 sites), the observed mean accuracy is 731%, demonstrating superior performance compared to existing domain adaptation and multi-site ASD identification techniques. Besides this, we discover several meaningful biomarkers. The topmost vital biomarkers are found within the inter-network resting-state functional connectivities (RSFCs). The LRCDR method, a proposed approach, significantly enhances ASD identification, presenting substantial clinical diagnostic potential.
Multi-robot system (MRS) missions in real-world scenarios consistently demand significant human involvement, and hand controllers remain the prevalent input method for operators. However, in circumstances requiring concurrent management of MRS and system monitoring, especially when the operator's hands are committed to other tasks, the hand-controller proves insufficient for enabling proficient human-MRS interaction. Our study aims to establish a foundation for a multimodal interface by incorporating a hands-free, gaze- and brain-computer interface (BCI)-driven input mechanism into the hand-controller, creating a hybrid gaze-BCI system. dysplastic dependent pathology In terms of MRS velocity control, the hand-controller's proficiency in continuous velocity commands remains assigned, whereas formation control is enacted using a more natural hybrid gaze-BCI, in preference to the hand-controller's less intuitive mapping. Utilizing a dual-task paradigm that mimicked real-world hand-occupied situations, operators using a hand-controller enhanced by a hybrid gaze-BCI showed gains in simulated MRS control, including a 3% rise in the average accuracy of formation inputs and a 5-second reduction in average completion time; there was also a decrease in cognitive load (a 0.32-second decrease in average secondary task reaction time), and a reduction in perceived workload (a 1.584 average reduction in rating scores), in comparison to using the hand-controller alone. This study's findings highlight the hands-free hybrid gaze-BCI's potential to broaden the scope of traditional manual MRS input devices, yielding a more operator-centric interface within the context of challenging hands-occupied dual-tasking scenarios.
Seizure prediction has become possible due to the evolution of brain-machine interface technology. Despite the potential, the transmission of a substantial volume of electrophysiological data between sensing devices and processing units, along with the computational burden involved, often creates key bottlenecks for seizure prediction systems. This is especially true for power-restricted wearable and implantable medical technologies. To reduce the communication bandwidth required for signals, diverse data compression strategies can be utilized; however, intricate compression and reconstruction processes must be executed beforehand to prepare the signals for seizure prediction. This paper proposes C2SP-Net, a system that integrates compression, prediction, and reconstruction, without adding any extra computational complexity. Bandwidth requirements for transmission are minimized by the framework, through a plug-and-play in-sensor compression matrix. For seizure prediction, the compressed signal offers a direct application, eliminating the need for reconstructing the signal. Also achievable is the high-fidelity reconstruction of the original signal. immunoturbidimetry assay To examine the framework's efficacy, we analyzed the energy consumption, prediction accuracy, sensitivity, rate of false predictions, and reconstruction quality under the influence of compression and classification overhead, utilizing various compression ratios. The experimental results unequivocally support the energy-efficiency and superior prediction accuracy of our proposed framework, which demonstrably outperforms the existing state-of-the-art baselines. Specifically, our proposed methodology results in an average loss of 0.6% in prediction precision, with a compression ratio spanning from 1/2 to 1/16.
This article examines a generalized form of multistability concerning almost periodic solutions within memristive Cohen-Grossberg neural networks (MCGNNs). The frequent disruptions within biological neurons contribute to the greater prevalence of almost periodic solutions in natural systems, compared to equilibrium points (EPs). Mathematically, these are also extended presentations of EPs. Based on almost periodic solutions and -type stability, this article provides a generalized definition for the multistability of almost periodic solutions. Generalized stable almost periodic solutions, (K+1)n in number, can coexist in an n-neuron MCGNN, with K a parameter of the activation functions, as the results demonstrate. According to the original state-space partitioning method, the attraction basins' dimensions, expanded, have also been estimated. Concluding this article, illustrative comparisons and compelling simulations are presented to validate the theoretical findings.