ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U

Facilitating Interskin Communication in Artificial Polymer Systems through Liquid Transfer

Chemical communication is a ubiquitous process in nature, and it has sparked interest in the development of electric-sense-based robotic perception systems with chemical components. Here, a novel liquid crystal polymer is introduced that combines the transferring, receiving, and sensing of chemical signals, providing a new principle to achieve chemical communication in robotic systems. This approach allows for the transfer of cargo between two polymer coatings, and the transfer can be monitored through an electrical signal. Additionally, cascade transfer can be achieved through this approach, as the transfer of cargo is not limited to only two coatings, but can continue from the second to a third coating. Furthermore, the two coatings can be infused with different reagents, and upon exchange, a reaction takes place to generate the desired species. The novel method of chemical communication that is developed presents a notable improvement in embodied perception. This advancement facilitates human–robot and robot–robot interactions and enhances the ability of robots to efficiently and accurately perform complex tasks in their environment.</p

Distributed Adaptive Gradient Algorithm with Gradient Tracking for Stochastic Non-Convex Optimization

This paper considers a distributed stochastic non-convex optimization problem, where the nodes in a network cooperatively minimize a sum of $L$-smooth local cost functions with sparse gradients. By adaptively adjusting the stepsizes according to the historical (possibly sparse) gradients, a distributed adaptive gradient algorithm is proposed, in which a gradient tracking estimator is used to handle the heterogeneity between different local cost functions. We establish an upper bound on the optimality gap, which indicates that our proposed algorithm can reach a first-order stationary solution dependent on the upper bound on the variance of the stochastic gradients. Finally, numerical examples are presented to illustrate the effectiveness of the algorithm.Comment: 14 pages, 8 figure

CATP: Context-Aware Trajectory Prediction with Competition Symbiosis

Contextual information is vital for accurate trajectory prediction. For instance, the intricate flying behavior of migratory birds hinges on their analysis of environmental cues such as wind direction and air pressure. However, the diverse and dynamic nature of contextual information renders it an arduous task for AI models to comprehend its impact on trajectories and consequently predict them accurately. To address this issue, we propose a ``manager-worker'' framework to unleash the full potential of contextual information and construct CATP model, an implementation of the framework for Context-Aware Trajectory Prediction. The framework comprises a manager model, several worker models, and a tailored training mechanism inspired by competition symbiosis in nature. Taking CATP as an example, each worker needs to compete against others for training data and develop an advantage in predicting specific moving patterns. The manager learns the workers' performance in different contexts and selects the best one in the given context to predict trajectories, enabling CATP as a whole to operate in a symbiotic manner. We conducted two comparative experiments and an ablation study to quantitatively evaluate the proposed framework and CATP model. The results showed that CATP could outperform SOTA models, and the framework could be generalized to different context-aware tasks

Through-Thickness Electric Field Establishes Complex Molecular Architectures for Localized Liquid Secretion

Localized liquid secretion, being an important process in nature such as the secretion of tears or mucus, has been an attractive point in developing biomimetic materials. However, precise localization remains challenging due to the cohesive and mobile nature of liquids. In this paper, light-induced localized liquid secretion is demonstrated on the scale of tens of micrometers by a liquid crystal polymer coating with an alternating homeotropic-planar alignment. The light responsiveness is achieved by the incorporation of azobenzene derivative. The localization is achieved by applying regional through-thickness electric fields to the monomeric liquid crystals before polymerization. The polymerized coating preserves both homeotropic and planar alignment. Upon actuation, the liquid can be locally secreted from the homeotropic region while suppressed in the planar area. This method allows precise control over various secretion patterns based on different pre-designed electrodes, which paves the way for the development of responsive devices in a multitude of fields, such as targeted drug delivery, tissue engineering, and microfluidic devices.</p