Gelatin Methacryloyl (GelMA) - 45S5 Bioactive Glass (BG) Composites for Bone Tissue Engineering: 3D Extrusion Printability and Cytocompatibility Assessment Using Human Osteoblasts.

3D extrusion printing has been widely investigated for low-volume production of complex-shaped scaffolds for tissue regeneration. Gelatin methacryloyl (GelMA) is used as a baseline material for the synthesis of biomaterial inks, often with organic/inorganic fillers, to obtain a balance between good printability and biophysical properties. The present study demonstrates how 45S5 bioactive glass (BG) addition and GelMA concentrations can be tailored to develop GelMA composite scaffolds with good printability and buildability.

Eye Gaze Guided Cross-Modal Alignment Network for Radiology Report Generation.

The potential benefits of automatic radiology report generation, such as reducing misdiagnosis rates and enhancing clinical diagnosis efficiency, are significant. However, existing data-driven methods lack essential medical prior knowledge, which hampers their performance. Moreover, establishing global correspondences between radiology images and related reports, while achieving local alignments between images correlated with prior knowledge and text, remains a challenging task.

Sensitivity Decouple Learning for Image Compression Artifacts Reduction.

With the benefit of deep learning techniques, recent researches have made significant progress in image compression artifacts reduction. Despite their improved performances, prevailing methods only focus on learning a mapping from the compressed image to the original one but ignore the intrinsic attributes of the given compressed images, which greatly harms the performance of downstream parsing tasks. Different from these methods, we propose to decouple the intrinsic attributes into two complementary features for artifacts reduction, i.

Dual modality prompt learning for visual question-grounded answering in robotic surgery.

With recent advancements in robotic surgery, notable strides have been made in visual question answering (VQA). Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the image. This limitation restricts the interpretative capacity of the VQA models and their ability to explore specific image regions.

Fully Spiking Actor Network With Intralayer Connections for Reinforcement Learning.

With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (DRL). In this article, we focus on the task where the agent needs to learn multidimensional deterministic policies to control, which is very common in real scenarios.

Cascaded Attention: Adaptive and Gated Graph Attention Network for Multiagent Reinforcement Learning.

Modeling the interactive relationships of agents is critical to improving the collaborative capability of a multiagent system. Some methods model these by predefined rules. However, due to the nonstationary problem, the interactive relationship changes over time and cannot be well captured by rules.

Adversarial Reciprocal Points Learning for Open Set Recognition.

Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as 'unknown', is essential for reliable machine learning. The key challenge of OSR is how to reduce the empirical classification risk on the labeled known data and the open space risk on the potential unknown data simultaneously. To handle the challenge, we formulate the open space risk problem from the perspective of multi-class integration, and model the unexploited extra-class space with a novel concept Reciprocal Point.

Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning.

A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes.