17 accepted papers
Artificial Neural Networks (ANNs) are powerful tools for complex decision-making tasks. While existing activation mechanisms often promote sparsity through thresholding, they lack explicit awareness of feature channel relevance, causing networks to continuously suffer from interference by noisy chan…
The applicability of drug molecules in various clinical scenarios is significantly influenced by a diverse range of molecular properties. By leveraging self-supervised conditions such as atom attributes and interatomic bonds, existing advanced molecular foundation models can generate expressive repr…
Fraudulent activities have caused substantial negative social impacts and are exhibiting emerging characteristics such as intelligence and industrialization, posing challenges of high-order interactions, intricate dependencies, and the sparse yet concealed nature of fraudulent entities. Existing gra…
Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems. Prior efforts focus on agent societies created from scratch, assigning agents with newly defined personas. However, simulating established fictional worlds and characters remain largely undere…
Untethered magnetic manipulation of biomedical millirobots has a high potential for minimally invasive surgical applications. However, it is still challenging to exert high actuation forces on the small robots over a large distance. Permanent magnets offer stronger magnetic torques and forces than e
Magnetic helical micro-robots (microbots) have attracted strong interest due to their unique propulsion mechanisms and potential applications in biomedical fields, particularly in minimally-invasive surgical procedures. Earlier research primarily focused on studying helical microbots in viscous liqu
Modern orchards are planted in structured rows with distinct panel divisions to improve management. Accurate and efficient joint segmentation of point cloud from Panel to Tree and Branch (P2TB) is essential for robotic operations. However, most current segmentation methods focus on single-instance s
Early detection of gastrointestinal (GI) cancer is critical for improving treatment outcomes and survival rates. Yet conventional endoscopic techniques remain invasive and labor-intensive, thus presenting significant challenges for cancer screening on large populations. Current commercially availabl
Surgical planning and training based on machine learning requires a large amount of 3D anatomical models reconstructed from medical imaging, which is currently one of the major bottlenecks. Obtaining these data from real patients and during surgery is very demanding, if even possible, due to legal,
Robotic branch pruning, a rapidly growing field addressing labor shortages in agriculture, requires detailed perception of branch geometry and topology. However, point clouds obtained in agricultural settings often lack completeness, limiting pruning accuracy. This work addressed point cloud quality…
The design of Artificial Neural Network (ANN) is inspired by the working patterns of the human brain. Connections in biological neural networks are sparse, as they only exist between few neurons. Meanwhile, the sparse representation in ANNs has been shown to possess significant advantages. Activatio…
Artificial Neural Networks (ANNs) have gained widespread applications across various areas in recent years. The ANN design was initially inspired by principles of biology. The biological neural network's fundamental response process comprises information transmission and aggregation. The information…
To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfold…
Deep learning has recently achieved remarkable performance in image classification tasks, which depends heavily on massive annotation. However, the classification mechanism of existing deep learning models seems to contrast to humans' recognition mechanism. With only a glance at an image of the obje…
Non-contact manipulation is of great importance in the actuation of micro-robotics. It is challenging to contactless manipulate micro-scale objects over large spatial distance in fluid. Here, we describe a novel approach for the dynamic position control of microparticles in three-dimensional (3D) sp…
Wireless actuation by magnetic fields allows for the operation of untethered miniaturized devices, e.g. in biomedical applications. Nevertheless, generating large controlled forces over relatively large distances is challenging. Magnetic torques are easier to generate and control, but they are not a…
Soft materials are being adopted in robotics in order to facilitate biomedical applications and in order to achieve simpler and more capable robots. One route to simplification is to design the robot's body using `smart materials' that carry the burden of control and actuation. Metamaterials enable