Recognizing the potential of recent vision transformers (ViTs), we develop the multistage alternating time-space transformers (ATSTs) for learning robust feature representations. Temporal and spatial tokens at each stage are handled alternately by separate Transformers for encoding and extraction. Subsequently, a novel cross-attention discriminator is presented, directly generating response maps in the search area without the addition of prediction heads or correlation filters. Testing reveals that the ATST model, in contrast to state-of-the-art convolutional trackers, offers promising outcomes. Importantly, the ATST model achieves comparable results to the latest CNN + Transformer trackers on a wide range of benchmarks, requiring considerably less training data.
Functional magnetic resonance imaging (fMRI), particularly functional connectivity network (FCN) measures, is now used more extensively for the diagnosis of brain-related illnesses. Even though the most advanced research used a single brain parcellation atlas at a particular spatial resolution to construct the FCN, it overlooked the functional interactions between diverse spatial scales within hierarchical configurations. This study introduces a novel approach to multiscale FCN analysis, thereby advancing brain disorder diagnosis. To commence, we utilize a collection of well-defined multiscale atlases for the computation of multiscale FCNs. Employing multiscale atlases, we leverage biologically relevant brain region hierarchies to execute nodal pooling across various spatial scales, a technique we term Atlas-guided Pooling (AP). Accordingly, a hierarchical graph convolutional network, MAHGCN, is presented, incorporating stacked graph convolution layers alongside the AP, aiming to comprehensively extract diagnostic information from multi-scale functional connectivity networks (FCNs). Experiments on neuroimaging data from 1792 subjects underscore the effectiveness of our proposed diagnostic approach for Alzheimer's disease (AD), its early stages (mild cognitive impairment), and autism spectrum disorder (ASD), achieving accuracies of 889%, 786%, and 727%, respectively. Our proposed method shows a substantial edge over other methods, according to all the results. This study, using resting-state fMRI and deep learning, successfully demonstrates the possibility of brain disorder diagnosis while also emphasizing the need to investigate and integrate the functional interactions within the multi-scale brain hierarchy into deep learning models to improve the understanding of brain disorder neuropathology. The public codes for MAHGCN are found on the GitHub page linked below: https://github.com/MianxinLiu/MAHGCN-code.
The growing need for energy, the declining price of physical assets, and the worldwide environmental issues are responsible for the current increased interest in rooftop photovoltaic (PV) panels as a clean and sustainable energy source. Integration of large-scale generation sources in residential areas modifies the electricity demand patterns of customers, creating an unpredictable element in the distribution system's net load. Recognizing that these resources are normally located behind the meter (BtM), a precise measurement of the BtM load and photovoltaic power will be crucial for the operation of the electricity distribution network. selleck compound This study proposes a spatiotemporal graph sparse coding (SC) capsule network, which effectively incorporates SC within deep generative graph modeling and capsule networks for the accurate estimation of BtM load and PV generation. In a dynamic graph, the relationship between the net demands of neighboring residential units is illustrated by the edges. reconstructive medicine The dynamic graph's highly nonlinear spatiotemporal patterns are meticulously extracted using a generative encoder-decoder model, specifically, spectral graph convolution (SGC) attention coupled with peephole long short-term memory (PLSTM). The proposed encoder-decoder's hidden layer, at a later stage, learns a dictionary to elevate the sparsity of the latent space, resulting in the extraction of their respective sparse codes. Estimates for the BtM PV generation and the load across all residential units are accomplished using sparse representations within a capsule network. Using the Pecan Street and Ausgrid energy disaggregation datasets, the experimental results showcase more than 98% and 63% improvements in root mean square error (RMSE) for building-to-module PV and load estimation, respectively, compared to currently used state-of-the-art methods.
The security of nonlinear multi-agent systems' tracking control, when subjected to jamming attacks, is the central topic of this article. Jamming attacks cause unreliable communication networks among agents, necessitating the introduction of a Stackelberg game to portray the interaction dynamics between multi-agent systems and the malicious jammer. Using a pseudo-partial derivative technique, the system's dynamic linearization model is initially built. Subsequently, a new adaptive control strategy, free of model dependence, is introduced, guaranteeing multi-agent systems' bounded tracking control in the mathematical expectation, even under jamming attacks. In addition to this, a pre-defined threshold event-driven method is implemented to lower communication costs. The proposed methods rely exclusively on the input and output information supplied by the agents. Finally, the proposed methods are corroborated through two illustrative simulations.
This paper describes a multimodal electrochemical sensing system-on-chip (SoC), which includes the functions of cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and temperature sensing as integral components. The CV readout circuitry's automatic range adjustment, in conjunction with resolution scaling, ensures an adaptive readout current range of 1455 dB. The EIS unit's impedance resolution, set at 92 mHz with a 10 kHz sweep frequency, allows a maximum output current of 120 A. Leveraging an impedance boost mechanism, this instrument extends the maximum detectable load impedance to 2295 kOhms while maintaining a total harmonic distortion below 1%. eye drop medication The swing-boosted relaxation oscillator, built into a resistor-based temperature sensor, yields a 31 mK resolution across a 0-85 degrees Celsius range. In a 0.18 m CMOS process, the design was implemented. The total power consumption measures precisely 1 milliwatt.
Image-text retrieval is a fundamental aspect of elucidating the semantic relationship between visual information and language, forming the bedrock of many vision and language applications. A common approach in prior work was to learn summarized representations of visual and textual content, while others dedicated significant effort to aligning image regions with specific words in the text. However, the significant relationships between coarse and fine-grained modalities are essential for image-text retrieval, but frequently overlooked. Therefore, previous efforts are inherently limited by either low retrieval accuracy or computationally intensive processes. Employing a unified framework, this work tackles image-text retrieval by integrating coarse- and fine-grained representation learning from a novel perspective. The framework aligns with human cognitive processes, where individuals attend to both the complete sample and its constituent parts to derive semantic meaning. In the context of image-text retrieval, a Token-Guided Dual Transformer (TGDT) architecture is developed. This architecture comprises two identical branches for handling image and text, respectively. The TGDT framework combines coarse and fine-grained retrieval, capitalizing on the strengths of both methods. A novel training objective, Consistent Multimodal Contrastive (CMC) loss, is proposed to maintain intra- and inter-modal semantic consistency between images and texts within a shared embedding space. Based on a two-part inference methodology utilizing a combination of global and local cross-modal similarities, this method achieves superior retrieval performance and incredibly fast inference times compared to existing recent approaches. Code for TGDT is openly available on the internet, specifically at github.com/LCFractal/TGDT.
A novel framework for 3D scene semantic segmentation, rooted in active learning and 2D-3D semantic fusion, was proposed. This framework, utilizing rendered 2D images, allows for efficient segmentation of large-scale 3D scenes with just a few 2D image annotations. In our system's initial phase, perspective views of the 3D environment are rendered at specific points. A pre-trained network for image semantic segmentation undergoes continuous refinement, with all dense predictions projected onto the 3D model for fusion thereafter. We iteratively scrutinize the 3D semantic model, concentrating on regions of unstable 3D segmentation. To improve the model, these regions are re-imaged, annotated, and subsequently used to train the network. By repeatedly applying rendering, segmentation, and fusion, intricate image samples within the scene can be generated without complex 3D annotation, leading to effective and efficient 3D scene segmentation with minimal labeling. The proposed method's superior performance, in comparison to contemporary state-of-the-art techniques, is substantiated by experiments on three large-scale indoor and outdoor 3D datasets.
Surface electromyography (sEMG) signals have become prevalent in rehabilitation medicine over recent decades due to their non-invasive nature, ease of use, and rich information content, particularly within the rapidly evolving field of human action recognition. While sparse EMG multi-view fusion research has not kept pace with high-density EMG, a technique to enrich sparse EMG feature information is necessary to minimize channel-based feature signal loss. The proposed IMSE (Inception-MaxPooling-Squeeze-Excitation) network module, detailed in this paper, addresses the issue of feature information loss during deep learning. Feature encoders, constructed using multi-core parallel processing within multi-view fusion networks, are employed to enhance the informational content of sparse sEMG feature maps. SwT (Swin Transformer) acts as the classification network's backbone.