Author name cluster

Ming Xiang

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4 papers

2 author rows

IROS Conference 2024 Conference Paper

Design of a Variable Wheel-propeller Integrated Mechanism for Amphibious Robots

Liang Lu
Xiangquan Gao
Ming Xiang
Zefeng Yan
Bin Han

In order to address the high complexity and low efficiency of amphibious propulsion systems, this paper proposes a novel variable wheel-propeller integrated mechanism for amphibious robots. By adjusting the blade pitch angle, it enables multiple motion modes, including rapid and stable movement on flat ground, obstacle crossing, and omnidirectional movement on water surface. This study establishes a kinematic model for the propeller blades and conducts multi-objective optimization of the structural parameters by considering both the land obstacle-crossing performance and underwater propulsion performance. Based on the optimized structural parameters, a virtual simulation prototype is constructed. Simulation results indicate that when water surface movement, with a driving torque of 3N. m, robot achieves a maximum linear velocity of 1. 25m/s and a maximum angular self-rotation velocity of 3. 5rad/s. Moreover, varying the blade pitch angle can alter the thrust direction, enabling omnidirectional mobility on water surface. During land movement, with a rotation speed of 60rpm, the highest obstacle-crossing height is 184mm. This wheel-propeller integrated mechanism exhibits robust comprehensive motion performance and environmental adaptability, with convenient motion modes switching.

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NeurIPS Conference 2024 Conference Paper

Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability

Ming Xiang
Stratis Ioannidis
Edmund Yeh
Carlee Joe-Wong
Lili Su

Addressing intermittent client availability is critical for the real-world deployment of federated learning algorithms. Most prior work either overlooks the potential non-stationarity in the dynamics of client unavailability or requires substantial memory/computation overhead. We study federated learning in the presence of heterogeneous and non-stationary client availability, which may occur when the deployment environments are uncertain, or the clients are mobile. The impacts of heterogeneity and non-stationarity on client unavailability can be significant, as we illustrate using FedAvg, the most widely adopted federated learning algorithm. We propose FedAWE, which includes novel algorithmic structures that (i) compensate for missed computations due to unavailability with only $O(1)$ additional memory and computation with respect to standard FedAvg, and (ii) evenly diffuse local updates within the federated learning system through implicit gossiping, despite being agnostic to non-stationary dynamics. We show that FedAWE converges to a stationary point of even non-convex objectives while achieving the desired linear speedup property. We corroborate our analysis with numerical experiments over diversified client unavailability dynamics on real-world data sets.

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AAAI Conference 2023 Conference Paper

Adjective Scale Probe: Can Language Models Encode Formal Semantics Information?

Wei Liu
Ming Xiang
Nai Ding

It is an open question what semantic representations transformer-based language models can encode and whether they have access to more abstract aspects of semantic meaning. Here, we propose a diagnostic dataset to investigate how well language models understand the degree semantics of adjectives. In the dataset, referred as the Adjective Scale Probe (ASP), we semi-automatically generate 8 tests of Natural Language Inference (NLI) questions to test 8 key capabilities of adjective interpretation. We apply the ASP dataset to evaluate the performance of 3 language models, i.e., BERT, DeBERTa, and T0. It is found that language models perform below the majority baseline for most tests of the ASP, even when the models have been fine-tuned to achieve high performance on the large-scale MNLI dataset. But after we fine-tune the pre-trained models on a subset of the ASP, DeBERTa can achieve high performance on the untrained adjectives and untrained tests, suggesting that DeBERTa may have captured degree semantic information of adjectives through pre-training but it needs specific training data to learn how to apply such information to the current tasks. In sum, the ASP provides an easy-to-use method to test fine-grained formal semantic properties of adjectives, and reveals language models' abilities to access formal semantic information.

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YNIMG Journal 2006 Journal Article

Reduction in V1 activation associated with decreased visibility of a visual target

Jie Huang
Ming Xiang
Yue Cao

The perception of a brief visual target stimulus can be affected by another visual mask stimulus immediately preceding or following the target. The link of this visual masking illusion, with visual cortical activation, offers insights into the neural mechanisms for visual perception. The present study investigated the association of the visibility of a target with cortical activation in humans using psychophysical testing and functional magnetic resonance imaging (fMRI). A visual masking protocol that was suitable for an fMRI study was developed. The event-related fMRI was used to measure activation in primary visual cortex (V1) during visual masking and unmasking stimulation. We found that the visibility of the target stimulus was reduced in the masking condition, due to the presence of mask stimuli, but not in the unmasking condition. We also found that the activation in V1 was modulated by the temporal separation of the mask stimuli from the target and was associated with the visibility of the target that was recorded during psychophysical testing and fMRI. These findings are consistent with what has been observed in the primate visual cortex of monkeys, i. e. , the transient on-response and after-discharge of V1 neurons to the target stimulus were suppressed by forward and backward mask stimuli, respectively.

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