The https://github.com/PeterouZh/CIPS-3D open-source CIPS-3D framework is on top. The current paper introduces CIPS-3D++, a refined GAN model, aiming for superior robustness, high resolution, and exceptional efficiency in its handling of 3D data. Within a style-based framework, our foundational model CIPS-3D encompasses a shallow NeRF-based 3D shape encoder and a deep MLP-based 2D image decoder, facilitating robust and rotation-invariant image generation and editing. By virtue of its inheritance of the rotational invariance property from CIPS-3D, our CIPS-3D++ model, augmented with geometric regularization and upsampling techniques, effectively facilitates the generation and editing of high-resolution, high-quality images with considerable computational efficiency. Without any extra features, CIPS-3D++ leverages raw, single-view images to achieve unparalleled results for 3D-aware image synthesis, demonstrating a remarkable FID of 32 on FFHQ at a resolution of 1024×1024. CIPS-3D++ operates with efficiency and a small GPU memory footprint, allowing for end-to-end training on high-resolution images directly; this contrasts sharply with previous alternative or progressive training methods. Utilizing the CIPS-3D++ framework, we introduce FlipInversion, a 3D-aware GAN inversion algorithm capable of reconstructing 3D objects from a single image. Employing CIPS-3D++ and FlipInversion, we also furnish a 3D-cognizant stylization method for actual images. Moreover, we examine the problem of mirror symmetry experienced in training and resolve it by utilizing an auxiliary discriminator for the NeRF model. CIPS-3D++ provides a strong model, suitable as a testing environment to adapt GAN-based 2D image editing approaches for use in three dimensions. At 2 https://github.com/PeterouZh/CIPS-3Dplusplus, you will find our open-source project, including the accompanying demonstration videos.

The standard practice in existing GNNs involves complete aggregation of neighbor information in each layer of message propagation. This process can become problematic when dealing with graphs that contain noise from incorrect or unnecessary connections. Graph Sparse Neural Networks (GSNNs), built upon Sparse Representation (SR) theory, are introduced within Graph Neural Networks (GNNs) to address this issue. GSNNs employ sparse aggregation for the selection of reliable neighboring nodes in the process of message aggregation. Discrete/sparse constraints pose a considerable obstacle in optimizing the GSNNs problem. As a result, we then created a strong continuous relaxation model called Exclusive Group Lasso Graph Neural Networks (EGLassoGNNs) to handle Graph Spatial Neural Networks (GSNNs). The proposed EGLassoGNNs model is improved through the derivation of an effective algorithm. Benchmark datasets' results show a stronger performance and resilience in the EGLassoGNNs model, as seen from the experimental study.

Few-shot learning (FSL) in multi-agent environments, where agents possess limited labeled data, is the focus of this article, with collaboration necessary to forecast query observation labels. Our goal is a coordinated learning framework for multiple agents, like drones and robots, to achieve accurate and efficient environmental perception while operating under limited communication and computational resources. A multi-agent, few-shot learning approach, utilizing metrics, is presented, structured around three crucial elements. A streamlined communication mechanism facilitates the transmission of detailed, compressed query feature maps from query agents to support agents. An asymmetric attention mechanism calculates region-based attention weights between query and support feature maps. A metric learning module calculates the image-level similarity between query and support data rapidly and precisely. We propose a custom-designed ranking-based feature learning module that fully leverages the order information in the training data. This is done by maximizing the inter-class distance while minimizing the intra-class distance. check details Through comprehensive numerical experiments, we show that our approach dramatically improves accuracy in visual and acoustic perception tasks, including face recognition, semantic image segmentation, and sound genre classification, systematically surpassing baselines by 5% to 20%.

Understanding the reasoning behind policies is an ongoing problem in Deep Reinforcement Learning (DRL). This paper examines interpretable deep reinforcement learning (DRL) by representing policies with Differentiable Inductive Logic Programming (DILP), resulting in a theoretical and empirical investigation into DILP-based policy learning, specifically from an optimization viewpoint. Initially, we recognized that the process of learning policies based on DILP principles necessitates a constrained optimization approach to policy design. To address the limitations of DILP-based policies, we then suggested leveraging Mirror Descent for policy optimization (MDPO). Our derivation of a closed-form regret bound for MDPO, leveraging function approximation, is instrumental in the development of DRL frameworks. Furthermore, we investigated the convexity of the DILP-based policy to confirm the advantages derived from MDPO. Experimental results, based on empirical data, demonstrate the performance of MDPO, its on-policy variant, and three leading policy learning methods, thereby validating our theoretical analysis.

In a multitude of computer vision undertakings, vision transformers have achieved noteworthy success. Nonetheless, the core softmax attention mechanism within vision transformers limits their ability to process high-resolution images, imposing a quadratic burden on both computational resources and memory requirements. In natural language processing (NLP), linear attention was developed to restructure the self-attention mechanism and address a comparable problem, however, directly adapting existing linear attention methods to visual data might not yield the desired outcomes. We examine this issue, highlighting how current linear attention methods neglect the inherent 2D locality bias present in visual tasks. This article introduces Vicinity Attention, a type of linear attention that effectively integrates two-dimensional local context. Based on its 2-dimensional Manhattan distance from neighboring picture sections, each image patch's attention weight is modified. This procedure yields 2D locality within a linear time complexity, and in this system, nearby image segments are prioritized with more attention compared to those situated remotely. Our novel Vicinity Attention Block, comprising Feature Reduction Attention (FRA) and Feature Preserving Connection (FPC), is designed to alleviate the computational bottleneck inherent in linear attention methods, including our Vicinity Attention, whose complexity grows quadratically with respect to the feature space. The Vicinity Attention Block calculates attention in a reduced feature space, with the addition of a skip connection designed to retain the full original feature distribution. Experimental results validate that the block leads to a reduction in computational resources while maintaining accuracy. Ultimately, to confirm the efficacy of the suggested approaches, a linear vision transformer framework, termed Vicinity Vision Transformer (VVT), was constructed. medial geniculate To address general vision tasks, we developed VVT using a hierarchical pyramid structure, decreasing the sequence length at each level. We rigorously evaluate our method's effectiveness through extensive experimentation on the CIFAR-100, ImageNet-1k, and ADE20K datasets. When input resolution expands, the computational overhead of our method increases at a slower rate than that of previous transformer-based and convolution-based networks. Specifically, our method attains cutting-edge image classification precision, utilizing 50% fewer parameters compared to prior techniques.

Transcranial focused ultrasound stimulation (tFUS) stands as a promising non-invasive therapeutic option. The attenuation of the skull at high ultrasound frequencies dictates the need for sub-MHz ultrasound waves for effective focused ultrasound surgery (tFUS) to reach sufficient penetration depths. This, in turn, contributes to relatively poor stimulation specificity, particularly in the axial direction orthogonal to the ultrasound transducer. Severe malaria infection Overcoming this deficiency is achievable by strategically deploying two distinct US beams, precisely aligned in both time and spatial dimensions. To execute transcranial focused ultrasound procedures on a large scale, dynamic steering of focused ultrasound beams toward the intended neural locations necessitates a phased array. This article explores the theoretical basis and optimization, using a wave-propagation simulator, of crossed-beam generation facilitated by two US phased arrays. Employing two individually crafted 32-element phased arrays (operating at 5555 kHz) situated at various angles, the experimental procedure corroborates the formation of crossed beams. At a focal distance of 46 mm, sub-MHz crossed-beam phased arrays in measurements yielded a lateral/axial resolution of 08/34 mm, significantly better than the 34/268 mm resolution of individual phased arrays at a 50 mm focal distance, representing a 284-fold improvement in reducing the main focal zone area. Validation of the crossed-beam formation, alongside a rat skull and a tissue layer, was also performed in the measurements.

To differentiate gastroparesis patients, diabetic patients without gastroparesis, and healthy controls, this study sought to identify throughout-the-day autonomic and gastric myoelectric biomarkers, shedding light on the causes of these conditions.
19 healthy controls and patients suffering from diabetic or idiopathic gastroparesis served as subjects for the collection of 24-hour electrocardiogram (ECG) and electrogastrogram (EGG) recordings. The extraction of autonomic and gastric myoelectric information from ECG and EGG data, respectively, was achieved through the application of physiologically and statistically rigorous models. From the provided data, we developed quantitative indices that successfully differentiated distinct groups, illustrating their effectiveness in automated classification systems and as concise quantitative summaries.