Fast computation of soft tissue deformations in real-time simulation with Hyper-Elastic Mass Links

International audienceVirtual surgery simulators show a lot of advantages in the world of surgery training, where they allow to improve the quality of surgeons' gesture. One of the current major technical difficulties for the development of surgery simulation is the possibility to perform a real-time computation of soft tissue deformation by considering the accurate modeling of their mechanical properties. However today, few models are available, they are still time consuming and limited in number of elements by algorithm complexity. We present in this paper a new method and framework that we call 'HEML' (Hyper-Elastic Mass Links), which is particularly fast. It is derived from the finite element method, can handle visco-hyperelastic and large deformation modeling. Although developed initially for medical applications, the HEML method can be used for any numerical computation of hyperelastic material deformations based on a tetrahedral mesh. A comparison with existing methods shows a much faster speed. A comparison with Mass-Spring methods, that are particularly fast but not realistic, shows that they can be considered as a degenerate case of the HEML framework

Colloidal III–V Nitride Quantum Dots

Colloidal quantum dots (QDs) have attracted intense attention in both fundamental studies and practical applications. To date, the size, morphology, and composition-controlled syntheses have been successfully achieved in II–VI semiconductor nanocrystals. Recently, III-nitride semiconductor quantum dots have begun to draw significant interest due to their promising applications in solid-state lighting, lasing technologies, and optoelectronic devices. The quality of nitride nanocrystals is, however, dramatically lower than that of II–VI semiconductor nanocrystals. In this review, the recent development in the synthesis techniques and properties of colloidal III–V nitride quantum dots as well as their applications are introduced

Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework

Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for entity resolution (ER), which divides an ER workload between the machine and the human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternatives in quality control.Comment: 12 pages, 11 figures. Camera-ready version of the paper submitted to ICDE 2018, In Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018

Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL

Discrete reinforcement learning (RL) algorithms have demonstrated exceptional performance in solving sequential decision tasks with discrete action spaces, such as Atari games. However, their effectiveness is hindered when applied to continuous control problems due to the challenge of dimensional explosion. In this paper, we present the Soft Decomposed Policy-Critic (SDPC) architecture, which combines soft RL and actor-critic techniques with discrete RL methods to overcome this limitation. SDPC discretizes each action dimension independently and employs a shared critic network to maximize the soft $Q$-function. This novel approach enables SDPC to support two types of policies: decomposed actors that lead to the Soft Decomposed Actor-Critic (SDAC) algorithm, and decomposed $Q$-networks that generate Boltzmann soft exploration policies, resulting in the Soft Decomposed-Critic Q (SDCQ) algorithm. Through extensive experiments, we demonstrate that our proposed approach outperforms state-of-the-art continuous RL algorithms in a variety of continuous control tasks, including Mujoco's Humanoid and Box2d's BipedalWalker. These empirical results validate the effectiveness of the SDPC architecture in addressing the challenges associated with continuous control

Optimised limit for polarimetric calibration of fully polarised SAR systems

The optimised limit for polarimetric calibration of fully polarised synthetic aperture radar systems is derived by establishing an error model as a function of cross-talk, channel imbalance and system noise. Compared with noise equivalent sigma zero, the polarimetric error below the optimised limit is too small to affect the signal of cross-polarised channel. Thus, polarimetric calibration could be relaxed or even ignored in this case. With the backscatter model, optimised limits for cross-talk and channel imbalance at X, C and L-bands are presented. Moreover, when ignoring channel imbalance, the limit for cross-talk is given in a quantitative way. These results are very useful in practice, allowing significant reduction in calibration cost