Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech Tasks

Human emotion understanding is pivotal in making conversational technology mainstream. We view speech emotion understanding as a perception task which is a more realistic setting. With varying contexts (languages, demographics, etc.) different share of people perceive the same speech segment as a non-unanimous emotion. As part of the ACM Multimedia 2023 Computational Paralinguistics ChallengE (ComParE) in the EMotion Share track, we leverage their rich dataset of multilingual speakers and multi-label regression target of 'emotion share' or perception of that emotion. We demonstrate that the training scheme of different foundation models dictates their effectiveness for tasks beyond speech recognition, especially for non-semantic speech tasks like emotion understanding. This is a very complex task due to multilingual speakers, variability in the target labels, and inherent imbalance in the regression dataset. Our results show that HuBERT-Large with a self-attention-based light-weight sequence model provides 4.6% improvement over the reported baseline.Comment: Accepted to appear at ACM Multimedia 2023 Multimedia Grand Challenges Trac

Jacobian Methods for Dynamic Polarization Control in Optical Applications

Dynamic polarization control (DPC) is beneficial for many optical applications. It uses adjustable waveplates to perform automatic polarization tracking and manipulation. Efficient algorithms are essential to realizing an endless polarization control process at high speed. However, the standard gradientbased algorithm is not well analyzed. Here we model the DPC with a Jacobian-based control theory framework that finds a lot in common with robot kinematics. We then give a detailed analysis of the condition of the Stokes vector gradient as a Jacobian matrix. We identify the multi-stage DPC as a redundant system enabling control algorithms with null-space operations. An efficient, reset-free algorithm can be found. We anticipate more customized DPC algorithms to follow the same framework in various optical systems

Neural encoding of socially adjusted value during competitive and hazardous foraging

In group foraging organisms, optimizing the conflicting demands of competitive food loss and safety is critical. We demonstrate that humans select competition avoidant and risk diluting strategies during foraging depending on socially adjusted value. We formulate a mathematically grounded quantification of socially adjusted value in foraging environments and show using multivariate fMRI analyses that socially adjusted value is encoded by mid-cingulate and ventromedial prefrontal cortices, regions that integrate value and action signals

StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS

Most existing methods for category-level pose estimation rely on object point clouds. However, when considering transparent objects, depth cameras are usually not able to capture meaningful data, resulting in point clouds with severe artifacts. Without a high-quality point cloud, existing methods are not applicable to challenging transparent objects. To tackle this problem, we present StereoPose, a novel stereo image framework for category-level object pose estimation, ideally suited for transparent objects. For a robust estimation from pure stereo images, we develop a pipeline that decouples category-level pose estimation into object size estimation, initial pose estimation, and pose refinement. StereoPose then estimates object pose based on representation in the normalized object coordinate space~(NOCS). To address the issue of image content aliasing, we further define a back-view NOCS map for the transparent object. The back-view NOCS aims to reduce the network learning ambiguity caused by content aliasing, and leverage informative cues on the back of the transparent object for more accurate pose estimation. To further improve the performance of the stereo framework, StereoPose is equipped with a parallax attention module for stereo feature fusion and an epipolar loss for improving the stereo-view consistency of network predictions. Extensive experiments on the public TOD dataset demonstrate the superiority of the proposed StereoPose framework for category-level 6D transparent object pose estimation.Comment: 7 pages, 6 figures, Project homepage: https://appsrv.cse.cuhk.edu.hk/~kaichen/stereopose.htm

Research on Flavor Substances of Cooked Rice by Electromagnetic Induction Heating with Pots Made of Different Materials Based on GC-IMS

The rice cooked by IH electromagnetic heating do not normally appear to be pinched and appear to be more uniform, while the rice heated by the electric heating element is easier to pinch and paste pot.Based on gas phase ion mobility spectrometry analysis of the volatile substances produced by IH electromagnetic heating of Japonica rice and Indica rice in pots with stainless steel, enamel, aluminum alloy and cast iron as inner tanks, the changes of flavor substances in the heated rice cooked with pots of different materials and utensils were investigated and the IH electromagnetic optimum is taken as a confirmed material.The results show that the material of the IH electromagnetic heating pot will affect the concentration of rice flavor substances, and the effects on different varieties of rice are distinctive. The concentration of caprylic aldehyde and 1-octene-3-one in the stainless-steel pot heated Japonica rice increases, and the concentration of 3-hydroxy-2-butanone in the rice cooked with the cast iron pot increases. The aluminum alloy pot and the cast iron pot bring up the concentration of the alcohols and ketones in cooked Indica rice. The concentration of similar substances increases too. Both Japonica rice and Indica rice heated with aluminum alloy pots show an increase in the concentration of butyl acetate, and furfural is produced while cooking rice in enamel pots.Through comparative analysis of all flavor substances and their concentrations of IH electromagnetic in heating rice, we conclude that rice cooked in cast iron pot have the best flavor, followed by that in stainless steel pot, while aluminum alloy pot brings down the rice flavor to below average level, and pasting pot is often observed in the enamel pot

Supervised physical training improves fine motor skills of 5-year-old children

Introduction: Fine motor skills are important for children not only in the activities of daily living, but also for learning activities. In the present study, the effects of supervised physical training were investigated in normal children. Objective: To evaluate the effects of supervised training by combining full-body exercise and the eye-hand coordination activities to improve fine motor skills in a group of five-year-old normal children. Methods: Fifty-two children were selected and randomized in exercise and control groups. The exercise group participated in three 30-minute training sessions per week for 24 weeks. Results: The fine motor skills and hand grip strength of the exercise group were significantly increased, while there was no significant change in the control group during the experimental period. Conclusion: The results indicate that the current exercise training program is effective and can be applied to 5-year-old normal children to improve their fine motor skills. In addition, this program has simple physical activities that are appropriate to the physical and mental level of child development. The 30-minute training session would be easily implemented in the kindergarten program