Advances in Emotion Recognition: Link to Depressive Disorder

Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social text on social network platform. Recognition of emotion pattern based on explicit and implicit features extracted through wearable and other devices could be decoded through computational modeling. Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder. The chapter aims to summarize the main findings in the area of affective recognition and its applications in major depressive disorder (MDD), which have made rapid progress in the last decade

Bayesian Estimation of CIR Model

Abstract: This article concerns the Bayesian estimation of interest rate models based on Euler-Maruyama approximation. Assume the short term interest rate follows the CIR model, an iterative method of Bayesian estimation is proposed. Markov Chain Monte Carlo simulation based on Gibbs sampler is used for the posterior estimation of the parameters. The maximum A-posteriori estimation using the genetic algorithm is employed for finding the Bayesian estimates of the parameters. The method and the algorithm are calibrated with the historical data of US Treasury bills

Variability-driven module selection with joint design time optimization and post-silicon tuning

Abstract—Increasing delay and power variation are significant chal-lenges to the designers as technology scales to the deep sub-micron (DSM) regime. Traditional module selection techniques in high level synthesis use worst case delay/power information to perform the optimization, and therefore may be too pessimistic such that extra resources are used to guarantee design requirements. Parametric yield, which is defined as the probability of the synthesized hardware meeting the performance/power constraints, can be used to guide design space exploration. The para-metric yield can be effectively improved by combining both design-time variation-aware optimization and post silicon tuning techniques (such as adaptive body biasing (ABB)). In this paper, we propose a module selection algorithm that combines design-time optimization with post-silicon tuning (using ABB) to maximize design yield. A variation-aware module selection algorithm based on efficient performance and power yield gradient computation is developed. The post silicon optimization is formulated as an efficient sequential conic program to determine the optimal body bias distribution, which in turn affects design-time module selection. The experiment results show that significant yield can be achieved compared to traditional worst-case driven module selection technique. To the best of our knowledge, this is the first variability-driven high level synthesis technique that considers post-silicon tuning during design time optimization. 1 I

Numerical Simulation of Temperature Field in Ultra-Narrow Arc Welding of Thick-Walled Steam Turbine Valve Body Material

The welding problems of large and thick plates are becoming more prominent as the application of large-scale and thick-plate metal structures grows. Due to the issue of excessive welding deformation between the 60mm thick steam turbine valve body and the pipe joint, a new process method is employed to connect. In this paper, the welding technology of flux strip confined arc ultra-narrow gap is proposed to carry out welding test on the ZG13Cr9Mo2Co1NiVNbNB cast steel test block of steam turbine valve body material with a thickness of 60 mm. The welding temperature field is measured by means of a K-type thermocouple and numerical simulation. The results show that the thermal cycle curve obtained by the homogeneous body heat source simulation is basically consistent with the thermal cycle curve measured during the experiment, and the simulation results of the molten pool morphology are also consistent with the actual macroscopic morphology of the weld