Soft robots have found extensive applications in the medical field, particularly in rehabilitation exercises, assisted grasping, and artificial organs. Despite significant advancements in simulating various components of the digestive system, the rectum has been largely neglected due to societal stigma. This study seeks to address this gap by developing soft circular muscle actuators (CMAs) and rectum models to replicate the defecation process. Using soft materials, both the rectum and the actuators were fabricated to enable seamless integration and attachment. We designed, fabricated, and tested three types of CMAs and compared them to the simulated results. A pneumatic system was employed to control the actuators, and simulated stool was synthesized using sodium alginate and calcium chloride. Experimental results indicated that the third type of actuator exhibited superior performance in terms of area contraction and pressure generation. The successful simulation of the defecation process highlights the potential of these soft actuators in biomedical applications, providing a foundation for further research and development in the field of soft robotics.

Fecal incontinence, arising from a myriad of pathogenic mechanisms, has attracted considerable global attention. Despite its significance, the replication of the defecatory system for studying fecal incontinence mechanisms remains limited largely due to social stigma and taboos. Inspired by the rectum's functionalities, we have developed a soft robotic system, encompassing a power supply, pressure sensing, data acquisition systems, a flushing mechanism, a stage, and a rectal module. The innovative soft rectal module includes actuators inspired by sphincter muscles, both soft and rigid covers, and soft rectum mold. The rectal mold, fabricated from materials that closely mimic human rectal tissue, is produced using the mold replication fabrication method. Both the soft and rigid components of the mold are realized through the application of 3D-printing technology. The sphincter muscles-inspired actuators featuring double-layer pouch structures are modeled and optimized based on multilayer perceptron methods aiming to obtain high contractions ratios (100 %), high generated pressure (9.8 kPa), and small recovery time (3 s). Upon assembly, this defecation robot is capable of smoothly expelling liquid faeces, performing controlled solid fecal cutting, and defecating extremely solid long faeces, thus closely replicating the human rectum and anal canal's functions. This defecation robot has the potential to assist humans in understanding the complex defecation system and contribute to the development of well-being devices related to defecation.

Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to contribute to a shared model without compromising data privacy. Due to the heterogeneous nature of local datasets, updated client models may overfit and diverge from one another, commonly known as the problem of client drift. In this paper, we propose FedBug (Federated Learning with Bottom-Up Gradual Unfreezing), a novel FL framework designed to effectively mitigate client drift. FedBug adaptively leverages the client model parameters, distributed by the server at each global round, as the reference points for cross-client alignment. Specifically, on the client side, FedBug begins by freezing the entire model, then gradually unfreezes the layers, from the input layer to the output layer. This bottom-up approach allows models to train the newly thawed layers to project data into a latent space, wherein the separating hyperplanes remain consistent across all clients. We theoretically analyze FedBug in a novel over-parameterization FL setup, revealing its superior convergence rate compared to FedAvg. Through comprehensive experiments, spanning various datasets, training conditions, and network architectures, we validate the efficacy of FedBug. Our contributions encompass a novel FL framework, theoretical analysis, and empirical validation, demonstrating the wide potential and applicability of FedBug.

The fields of soft and bio-inspired robotics promise to imbue synthetic systems with capabilities found in the natural world. However, many of these biological capabilities are yet to be realized. For example, vines in nature direct growth via localized responses embedded in the cells of vine body, allowing an organism without a central brain to successfully search for resources (e.g., light). Yet to date, vine-inspired robots have yet to show such localized embedded responsiveness. Here we present a vine-inspired robotic device with material-level responses embedded in its skin and capable of growing and steering toward either a light or heat stimulus. We present basic modeling of the concept, design details, and experimental results showing its behavior in response to infrared (IR) and visible light. Our simple design concept advances the capabilities of bio-inspired robots and lays the foundation for future growing robots that are capable of seeking light or heat, yet are extremely simple and low-cost. Potential applications include solar tracking, and in the future, firefighting smoldering fires. We envision using similar robots to find hot spots in hard-to-access environments, allowing us to put out potentially long-burning fires faster.

We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized auto-encoders as well as denoising auto-encoders on a range of datasets. This penalty term corresponds to the Frobenius norm of the Jacobian matrix of the encoder activations with respect to the input. We show that this penalty term results in a localized space contraction which in turn yields robust features on the activation layer. Furthermore, we show how this penalty term is related to both regularized auto-encoders and denoising encoders and how it can be seen as a link between deterministic and non-deterministic auto-encoders. We find empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold. Finally, we show that by using the learned features to initialize a MLP, we achieve state of the art classification error on a range of datasets, surpassing other methods of pre-training.