Joint Hypergraph Learning and Sparse Regression for Feature Selection

In this paper, we propose a unified framework for improved structure estimation and feature selection. Most existing graph-based feature selection methods utilise a static representation of the structure of the available data based on the Laplacian matrix of a simple graph. Here on the other hand, we perform data structure learning and feature selection simultaneously. To improve the estimation of the manifold representing the structure of the selected features, we use a higher order description of the neighbour- hood structures present in the available data using hypergraph learning. This allows those features which participate in the most significant higher order relations to be se- lected, and the remainder discarded, through a sparsification process. We formulate a single objective function to capture and regularise the hypergraph weight estimation and feature selection processes. Finally, we present an optimization algorithm to re- cover the hyper graph weights and a sparse set of feature selection indicators. This process offers a number of advantages. First, by adjusting the hypergraph weights, we preserve high-order neighborhood relations reflected in the original data, which cannot be modeled by a simple graph. Moreover, our objective function captures the global discriminative structure of the features in the data. Comprehensive experiments on 9 benchmark data sets show that our method achieves statistically significant improve- ment over state-of-art feature selection methods, supporting the effectiveness of the proposed method

Water-responsive supercontractile polymer films for bioelectronic interfaces

Connecting different electronic devices is usually straightforward because they have paired, standardized interfaces, in which the shapes and sizes match each other perfectly. Tissue-electronics interfaces, however, cannot be standardized, because tissues are soft1-3 and have arbitrary shapes and sizes4-6. Shape-adaptive wrapping and covering around irregularly sized and shaped objects have been achieved using heat-shrink films because they can contract largely and rapidly when heated7. However, these materials are unsuitable for biological applications because they are usually much harder than tissues and contract at temperatures higher than 90 °C (refs. 8,9). Therefore, it is challenging to prepare stimuli-responsive films with large and rapid contractions for which the stimuli and mechanical properties are compatible with vulnerable tissues and electronic integration processes. Here, inspired by spider silk10-12, we designed water-responsive supercontractile polymer films composed of poly(ethylene oxide) and poly(ethylene glycol)-α-cyclodextrin inclusion complex, which are initially dry, flexible and stable under ambient conditions, contract by more than 50% of their original length within seconds (about 30% per second) after wetting and become soft (about 100 kPa) and stretchable (around 600%) hydrogel thin films thereafter. This supercontraction is attributed to the aligned microporous hierarchical structures of the films, which also facilitate electronic integration. We used this film to fabricate shape-adaptive electrode arrays that simplify the implantation procedure through supercontraction and conformally wrap around nerves, muscles and hearts of different sizes when wetted for in vivo nerve stimulation and electrophysiological signal recording. This study demonstrates that this water-responsive material can play an important part in shaping the next-generation tissue-electronics interfaces as well as broadening the biomedical application of shape-adaptive materials.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityNational Research Foundation (NRF)National Supercomputing Centre (NSCC) SingaporeThe project was supported by the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under the Singapore Hybrid-Integrated Next-Generation μ-Electronics (SHINE) Centre, Campus of Research Excellence and Technological Enterprise (CREATE), the Smart Grippers for Soft Robotics (SGSR) Program, and the A*STAR MTC Programmatic Funding Scheme (project no. M23L8b0049). G.L. and Z.L. acknowledge support from Shenzhen Science and Technology Program (grant no. KQTD20210811090217009), the Science and Technology Program of Guangdong Province (2022A0505090007) and the National Natural Science Foundation of China (U81927804 and 1913601). G.Z. and H.G. acknowledge support from a start-up grant (002479-00001) from the Nanyang Technological University and the Institute of High Performance Computing, A*STAR, Singapore. Molecular dynamics simulations reported were performed on resources of the National Supercomputing Centre, Singapore. B.H. acknowledges support from the National Natural Science Foundation of China (81971701), the Natural Science Foundation of Jiangsu Province (BK20201352) and the Nanjing Medical University Introduced Talents Scientific Research Start-up Fund (NMUR20190003)