FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation

Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion. Publicly available datasets truly help to accelerate research in this area by providing a benchmark resource, but all of these datasets, to the best of our knowledge, are limited to rough annotations for action units, including only their absence, presence, or a five-level intensity according to the Facial Action Coding System. To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D Facial Animation. One hundred and twenty-two participants, including children, young adults and elderly people, were recorded in real-world conditions. In addition, 99,356 frames were manually labeled using Expression Quantitative Tool developed by us to quantify 9 symmetrical FACS action units, 10 asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action descriptors and 2 asymmetrical FACS action descriptors, and each action unit or action descriptor is well-annotated with a floating point number between 0 and 1. To provide a baseline for use in future research, a benchmark for the regression of action unit values based on Convolutional Neural Networks are presented. We also demonstrate the potential of our FEAFA dataset for 3D facial animation. Almost all state-of-the-art algorithms for facial animation are achieved based on 3D face reconstruction. We hence propose a novel method that drives virtual characters only based on action unit value regression of the 2D video frames of source actors.Comment: 9 pages, 7 figure

Flexible Neural Electrode Array Based-on Porous Graphene for Cortical Microstimulation and Sensing.

Neural sensing and stimulation have been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To-date, most of the neural stimulation systems have relied on sharp metal microelectrodes with poor electrochemical properties that induce extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. Here, we demonstrate a flexible cortical microelectrode array based on porous graphene, which is capable of efficient electrophysiological sensing and stimulation from the brain surface, without penetrating into the tissue. Porous graphene electrodes show superior impedance and charge injection characteristics making them ideal for high efficiency cortical sensing and stimulation. They exhibit no physical delamination or degradation even after 1 million biphasic stimulation cycles, confirming high endurance. In in vivo experiments with rodents, same array is used to sense brain activity patterns with high spatio-temporal resolution and to control leg muscles with high-precision electrical stimulation from the cortical surface. Flexible porous graphene array offers a minimally invasive but high efficiency neuromodulation scheme with potential applications in cortical mapping, brain-computer interfaces, treatment of neurological disorders, where high resolution and simultaneous recording and stimulation of neural activity are crucial

ESTIMATING EFFECTS OF AGRICULTURAL RESEARCH AND EXTENSION EXPENDITURES ON PRODUCTIVITY: A TRANSLOG PRODUCTION FUNCTION APPROACH

The effects of agricultural research and extension expenditures on productivity in the United States are estimated during the period 1949-81 using data for ten production regions. The large time-series cross-sectional data base allows the translog production function to be estimated directly. Results from the translog and Cobb-Douglas production functions are compared. The results indicate that use of the Cobb-Douglas production function would overestimate the internal rate of return of agricultural research and extension expenditures in the United States and eight production regions. The total marginal product and internal rate of return for the United States are $8.11 and 66 percent, respectively.Productivity Analysis,

Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination

The learned policy of model-free offline reinforcement learning (RL) methods is often constrained to stay within the support of datasets to avoid possible dangerous out-of-distribution actions or states, making it challenging to handle out-of-support region. Model-based RL methods offer a richer dataset and benefit generalization by generating imaginary trajectories with either trained forward or reverse dynamics model. However, the imagined transitions may be inaccurate, thus downgrading the performance of the underlying offline RL method. In this paper, we propose to augment the offline dataset by using trained bidirectional dynamics models and rollout policies with double check. We introduce conservatism by trusting samples that the forward model and backward model agree on. Our method, confidence-aware bidirectional offline model-based imagination, generates reliable samples and can be combined with any model-free offline RL method. Experimental results on the D4RL benchmarks demonstrate that our method significantly boosts the performance of existing model-free offline RL algorithms and achieves competitive or better scores against baseline methods.Comment: NeurIPS 202

Effect of thermal cycling frequency on the durability of Yb-Gd-Y-based thermal barrier coatings

The effects of thermal cycling frequency and buffer layer on the crack generation and thermal fatigue behaviors of Yb–Gd–Y-stabilized zirconia (YGYZ)-based thermal barrier coatings (TBCs) were investigated through thermally graded mechanical fatigue (TGMF) test. TGMF tests with low- (period of 10 min) and high-frequency (period of 2 min) cycling were performed at 1100 °C with a 60 MPa tensile load. Different cycling frequencies in TGMF test generate two kinds of crack propagation modes. The sample with low-frequency cycling condition shows penetration cracks in the YGYZ top coat, and multiple narrow vertical cracks are generated in high-frequency cycling. To enhance the thermomechanical properties, different buffer layers were introduced into the TBC systems, which were deposited with the regular (RP) or high-purity 8 wt% yttria stabilized zirconia (HP-YSZ) feedstock. The purity of the feedstock powder used for preparing the buffer layer affected the fracture behavior, showing a better thermal durability for the TBCs with the HP-YSZ in both frequency test conditions. A finite element model is developed, which takes creep effect into account due to thermal cycling. The model shows the high stresses at the interfaces between different layers due to differential thermal expansion. The failure mechanisms of YGYZ-based TBCs in TGMF test are also proposed. The vertical cracks are preferentially created, and then the vertical and horizontal cracks will be propagated when the vertical cracks are impeded by pores and micro-cracks