Ke Dong

Xiaohong Fan, Yin Yang, Ke Chen et al.

Although existing deep learning compressed-sensing-based Magnetic Resonance Imaging (CS-MRI) methods have achieved considerably impressive performance, explainability and generalizability continue to be challenging for such methods since the transition from mathematical analysis to network design not always natural enough, often most of them are not flexible enough to handle multi-sampling-ratio reconstruction assignments. {In this work, to tackle explainability and generalizability, we propose a unifying deep unfolding multi-sampling-ratio interpretable CS-MRI framework.} The combined approach offers more generalizability than previous works whereas deep learning gains explainability through a geometric prior module. Inspired by the multigrid algorithm, we first embed the CS-MRI-based optimization algorithm into correction-distillation scheme that consists of three ingredients: pre-relaxation module, correction module and geometric prior distillation module. Furthermore, we employ a condition module to learn adaptively step-length and noise level, which enables the proposed framework to jointly train multi-ratio tasks through a single model. { The proposed model not only compensates for the lost contextual information of reconstructed image which is refined from low frequency error in geometric characteristic k-space}, but also integrates the theoretical guarantee of model-based methods and the superior reconstruction performances of deep learning-based methods. Therefore, it can give us a novel perspective to design biomedical imaging networks. { Numerical experiments show that our framework outperforms state-of-the-art methods in terms of qualitative and quantitative evaluations.} {Our method achieves 3.18 dB improvement at low CS ratio 10\% and average 1.42 dB improvement over other comparison methods on brain dataset using Cartesian sampling mask.

Ke Dong, Kejie Huang, Tao Luo et al.

Shifted-and-Duplicated-Kernel (SDK) mapping has emerged as an effective strategy to accelerate convolutional layers on compute-in-memory (CIM) hardware. However, existing SDK variants (e.g., VWC-SDK) merely optimize mapping for a single CIM macro, leaving inter-macro parallelism unexplored. Moreover, their mapping methodologies are still suboptimal. To address these limitations, we present TetrisG-SDK, a novel framework that employs adaptive windows to boost mapping performance. The proposed windows accommodate more input channels, increase array utilization at marginal space, and adapt to different channel depths. More importantly, TetrisG-SDK reduces compute latency by searching for optimal window configurations across multiple CIM macros with a fixed hardware budget. Besides, it incorporates grouped convolution to further decrease computing cycles while maintaining near-lossless model accuracy. In addition, TetrisG-SDK integrates a validated CIM hardware simulator to provide accurate system-/application-level estimations of latency, area and energy. Compared to the single-macro VWC-SDK, the proposed framework achieves a speed-up by 1.2x, 1.3x, and 1.3x for CNN8, GoogLeNet Inception, and DenseNet40 models, respectively. When deployed on the simulator, it reduces system-level latency and energy by 2.4x and 1.7x for CNN8, 1.3x and 1.2x for Inception, and 1.3x and 1.6x for DenseNet40, respectively. When leveraging macro-level parallelism, TetrisG-SDK reduces the Energy-Delay-Area-Product (EDAP) by 70% for CNN8, 68% for Inception, and 36% for DenseNet40 compared to its non-grouped counterpart. These results manifest that TetrisG-SDK is a promising solution to efficiently mapping convolutional layers on CIM hardware.

Adrita Barua, Gary Brase, Ke Dong et al.

We subject GPT-4 to a number of rigorous psychometric tests and analyze the results. We find that, compared to the average human, GPT-4 tends to show more honesty and humility, and less machiavellianism and narcissism. It sometimes exhibits ambivalent sexism, leans slightly toward masculinity, is moderately anxious but mostly not depressive (but not always). It shows human-average numerical literacy and has cognitive reflection abilities that are above human average for verbal tasks.

Ke Dong, Karime Pereida, Florian Shkurti et al.

Mobile manipulators consist of a mobile platform equipped with one or more robot arms and are of interest for a wide array of challenging tasks because of their extended workspace and dexterity. Typically, mobile manipulators are deployed in slow-motion collaborative robot scenarios. In this paper, we consider scenarios where accurate high-speed motions are required. We introduce a framework for this regime of tasks including two main components: (i) a bi-level motion optimization algorithm for real-time trajectory generation, which relies on Sequential Quadratic Programming (SQP) and Quadratic Programming (QP), respectively; and (ii) a learning-based controller optimized for precise tracking of high-speed motions via a learned inverse dynamics model. We evaluate our framework with a mobile manipulator platform through numerous high-speed ball catching experiments, where we show a success rate of 85.33%. To the best of our knowledge, this success rate exceeds the reported performance of existing related systems and sets a new state of the art.