Nesterov smoothing for sampling without smoothness

We study the problem of sampling from a target distribution in $\mathbb{R}^d$ whose potential is not smooth. Compared with the sampling problem with smooth potentials, this problem is much less well-understood due to the lack of smoothness. In this paper, we propose a novel sampling algorithm for a class of non-smooth potentials by first approximating them by smooth potentials using a technique that is akin to Nesterov smoothing. We then utilize sampling algorithms on the smooth potentials to generate approximate samples from the original non-smooth potentials. We select an appropriate smoothing intensity to ensure that the distance between the smoothed and un-smoothed distributions is minimal, thereby guaranteeing the algorithm's accuracy. Hence we obtain non-asymptotic convergence results based on existing analysis of smooth sampling. We verify our convergence result on a synthetic example and apply our method to improve the worst-case performance of Bayesian inference on a real-world example

A Personalized Human Drivers\u27 Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers\u27 risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from the design car-following target. With the risk-sensitive parameter embedded in LEQG, the proposed method has the capability to characterize risk preference heterogeneity of each AV against uncertainties according to each human drivers\u27 preference. Further, the established control theory can achieve both expensive control mode and non-expensive control mode via changing the weighting matrix of the cost function in LEQG to reveal different treatments on input. Simulation tests validate the proposed approach can characterize different driving behaviors and its effectiveness in terms of reducing the deviation from equilibrium state. The ability to produce different trajectories and generate smooth control of the proposed algorithm is also verified

Low-carbon economic operation of integrated energy systems in consideration of demand-side management and carbon trading

Under the background of carbon emission abatement worldwide, carbon trading is becoming an important carbon financing policy to promote emission mitigation. Aiming at the emerging coupling among various energy sectors, this paper proposes a bi-level scheduling model to investigate the low-carbon operation of the electricity and natural gas integrated energy systems (IES). Firstly, an optimal energy flow model considering carbon trading is formulated at the upper level, in which carbon emission flow model is employed to track the carbon flows accompanying energy flows and identify the emission responsibility from the consumption-based perspective, and the locational marginal price is determined at the same time. Then at the lower level, a developed demand-side management strategy is introduced, which can manage demands in response to both the dynamic energy prices and the nodal carbon intensities, enabling the user side to participate in the joint energy and carbon trading. The bi-level model is solved iteratively and reaches an equilibrium. Finally, case studies based on the IEEE 39-bus system and the Belgium 20-node system illustrate the effectiveness of the proposed method in reducing carbon emissions and improving consumer surplus

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201

TIMS: A Tactile Internet-Based Micromanipulation System with Haptic Guidance for Surgical Training

Microsurgery involves the dexterous manipulation of delicate tissue or fragile structures such as small blood vessels, nerves, etc., under a microscope. To address the limitation of imprecise manipulation of human hands, robotic systems have been developed to assist surgeons in performing complex microsurgical tasks with greater precision and safety. However, the steep learning curve for robot-assisted microsurgery (RAMS) and the shortage of well-trained surgeons pose significant challenges to the widespread adoption of RAMS. Therefore, the development of a versatile training system for RAMS is necessary, which can bring tangible benefits to both surgeons and patients. In this paper, we present a Tactile Internet-Based Micromanipulation System (TIMS) based on a ROS-Django web-based architecture for microsurgical training. This system can provide tactile feedback to operators via a wearable tactile display (WTD), while real-time data is transmitted through the internet via a ROS-Django framework. In addition, TIMS integrates haptic guidance to `guide' the trainees to follow a desired trajectory provided by expert surgeons. Learning from demonstration based on Gaussian Process Regression (GPR) was used to generate the desired trajectory. User studies were also conducted to verify the effectiveness of our proposed TIMS, comparing users' performance with and without tactile feedback and/or haptic guidance.Comment: 8 pages, 7 figures. For more details of this project, please view our website: https://sites.google.com/view/viewtims/hom

CausalCellSegmenter: Causal Inference inspired Diversified Aggregation Convolution for Pathology Image Segmentation

Deep learning models have shown promising performance for cell nucleus segmentation in the field of pathology image analysis. However, training a robust model from multiple domains remains a great challenge for cell nucleus segmentation. Additionally, the shortcomings of background noise, highly overlapping between cell nucleus, and blurred edges often lead to poor performance. To address these challenges, we propose a novel framework termed CausalCellSegmenter, which combines Causal Inference Module (CIM) with Diversified Aggregation Convolution (DAC) techniques. The DAC module is designed which incorporates diverse downsampling features through a simple, parameter-free attention module (SimAM), aiming to overcome the problems of false-positive identification and edge blurring. Furthermore, we introduce CIM to leverage sample weighting by directly removing the spurious correlations between features for every input sample and concentrating more on the correlation between features and labels. Extensive experiments on the MoNuSeg-2018 dataset achieves promising results, outperforming other state-of-the-art methods, where the mIoU and DSC scores growing by 3.6% and 2.65%.Comment: 10 pages, 5 figures, 2 tables, MICCA

Miniature ultrasound transducer incorporating Sm-PMN-PT 1-3 composite

Piezoelectric 1-3 composite materials have become extensively utilized in diagnostic ultrasound transducers owing to their high electromechanical coupling coefficient, low acoustic impedance, and low dielectric loss. In this study, Sm-doped PMN-PT ceramic/epoxy 1-3 composite with a ceramic volume fraction of 60% is fabricated by the dice-and-fill method, resulting in a high piezoelectric constant (650 pC/N) and clamped dielectric constant (2350). Utilizing the exceptionally high clamped dielectric constant, a low-frequency (12.4 MHz) ultrasound transducer is developed with a miniature aperture size (0.84 mm × 0.84 mm), exhibiting a −6 dB bandwidth of 70% and an insertion loss of −20.5 dB. The imaging capability of the miniature composite transducer is validated through both phantom and ex vivo imaging. The satisfactory results indicate that Sm-doped ceramic/epoxy composites possess significant potential for miniature devices in biomedical imaging applications