This work presents a scalable grayscale UV technique for fabricating spatially programmable soft actuators with diverse actuation behaviors in one actuator. The advanced programmability lays the foundation for soft robotics and adaptive devices.
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Free, publicly-accessible full text available January 1, 2026
-
Free, publicly-accessible full text available December 1, 2025
-
Abstract Silent speech interfaces offer an alternative and efficient communication modality for individuals with voice disorders and when the vocalized speech communication is compromised by noisy environments. Despite the recent progress in developing silent speech interfaces, these systems face several challenges that prevent their wide acceptance, such as bulkiness, obtrusiveness, and immobility. Herein, the material optimization, structural design, deep learning algorithm, and system integration of mechanically and visually unobtrusive silent speech interfaces are presented that can realize both speaker identification and speech content identification. Conformal, transparent, and self‐adhesive electromyography electrode arrays are designed for capturing speech‐relevant muscle activities. Temporal convolutional networks are employed for recognizing speakers and converting sensing signals into spoken content. The resulting silent speech interfaces achieve a 97.5% speaker classification accuracy and 91.5% keyword classification accuracy using four electrodes. The speech interface is further integrated with an optical hand‐tracking system and a robotic manipulator for human‐robot collaboration in both assembly and disassembly processes. The integrated system achieves the control of the robot manipulator by silent speech and facilitates the hand‐over process by hand motion trajectory detection. The developed framework enables natural robot control in noisy environments and lays the ground for collaborative human‐robot tasks involving multiple human operators.
Free, publicly-accessible full text available October 1, 2025 -
Abstract Benthic storms are episodes of intensified near‐bottom currents capable of sediment resuspension in the deep ocean. They typically occur under regions of high surface eddy kinetic energy (EKE), such as the Gulf Stream. Although they have long been observed, the mechanism(s) responsible for their formation and their relationships with salient features of the deep ocean, such as bottom mixed layers (BMLs) and benthic nepheloid layers (BNLs), remain poorly understood. Here we conduct idealized experiments with a primitive‐equation model to explore the impacts of the unforced instability of a surface‐intensified jet on near‐bottom flows of a deep zonal channel. Vertical resolution is increased near the bottom to represent the bottom boundary layer. We find that the unstable near‐surface jet develops meanders and evolves into alternating, deep‐reaching cyclones and anticyclones. Simultaneously, EKE increases near the bottom due to the convergence of vertical eddy pressure fluxes, leading to near‐bottom currents comparable to those observed during benthic storms. These currents in turn form BMLs with thickness of O(100 m) from enhanced velocity shears and turbulence production near the bottom. The deep cyclonic eddies transport fluid particles both laterally and vertically, from near the bottom through the entire BML and may contribute to the formation of the lower part of BNLs. A sloping bottom reduces both the intensity of the near‐bottom currents and the extent of vertical transport. Overall, our study highlights a significant response of the abyssal environment to near‐surface current instability, with potential implications for sediment transport in the deep ocean.
Free, publicly-accessible full text available July 1, 2025 -
Active learning is a promising paradigm to reduce the labeling cost by strategically requesting labels to improve model performance. However, existing active learning methods often rely on expensive acquisition function to compute, extensive modeling retraining and multiple rounds of interaction with annotators. To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition. A key challenge in this approach is developing an acquisition function that generalizes well, as the history of data, which forms part of the utility function’s input, grows over time. Our novel algorithmic contribution is a multi-task bilevel optimization framework that predicts the relative utility, measured by the validation accuracy, of different training sets, and ensures the learned acquisition function generalizes effectively. For cases where validation accuracy is expensive to evaluate, we introduce efficient interpolation-based surrogate models to estimate the utility function, reducing the evaluation cost. We demonstrate the performance of our approach through extensive experiments on standard active classification benchmarks.more » « lessFree, publicly-accessible full text available May 11, 2025
-
Free, publicly-accessible full text available April 26, 2025
-
Free, publicly-accessible full text available May 11, 2025
-
Free, publicly-accessible full text available May 11, 2025
-
Previous research underscored the potential of danmaku–a text-based commenting feature on videos–in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce “Signmaku,” a new commenting mechanism that uses ASL, serving as a sign language counterpart to danmaku. Through a need-finding study (N=12) and a within-subject experiment (N=20), we evaluated three design styles: real human faces, cartoon-like figures, and robotic representations. The results showed that cartoon-like signmaku not only entertained but also encouraged participants to create and share ASL comments, with fewer privacy concerns compared to the other designs. Conversely, the robotic representations faced challenges in accurately depicting hand movements and facial expressions, resulting in higher cognitive demands on users. Signmaku featuring real human faces elicited the lowest cognitive load and was the most comprehensible among all three types. Our findings offered novel design implications for leveraging generative AI to create signmaku comments, enriching co-learning experiences for DHH individuals.more » « lessFree, publicly-accessible full text available May 11, 2025
-
Abstract Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure–function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper we first introduced an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, we selected interpolating data points in the learned latent space that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β1-42(Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42’s conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.
Free, publicly-accessible full text available May 5, 2025