arXiv subjects

Min Hu

Publications and source records attributed to Min Hu.

23 records · Page 2Linked to original sources

arXiv2020

Atomic origin for hydrogenation promoted bulk oxygen vacancies removal in vanadium dioxide

Oxygen vacancies (VO), a common type of point defects in metal oxides materials, play important roles on the physical and chemical properties. To obtain stoichiometric oxide crystal, the pre-existing VO is always removed via careful post-annealing treatment at high temperature in air or oxygen atmosphere. However, the annealing conditions is difficult to control and the removal of VO in bulk phase is restrained due to high energy barrier of VO migration. Here, we selected VO2 crystal film as the model system and developed an alternative annealing treatment aided by controllable hydrogen doping, which can realizes effective removal of VO defects in VO2-δ crystal at lower temperature. This finding is attributed to the hydrogenation accelerated oxygen vacancies recovery in VO2-δ crystal. Theoretical calculations revealed that the H-doping induced electrons are prone to accumulate around the oxygen defects in VO2-δ film, which facilitates the diffusion of VO and thus makes it easier to be removed. The methodology is expected to be applied to other metal oxides for oxygen-related point defects control.

cond-mat.mtrl-sci↗

arXiv2020

Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild

Unlike the constraint frontal face condition, faces in the wild have various unconstrained interference factors, such as complex illumination, changing perspective and various occlusions. Facial expressions recognition (FER) in the wild is a challenging task and existing methods can't perform well. However, for occluded faces (containing occlusion caused by other objects and self-occlusion caused by head posture changes), the attention mechanism has the ability to focus on the non-occluded regions automatically. In this paper, we propose a Lossless Attention Model (LLAM) for convolutional neural networks (CNN) to extract attention-aware features from faces. Our module avoids decay information in the process of generating attention maps by using the information of the previous layer and not reducing the dimensionality. Sequentially, we adaptively refine the feature responses by fusing the attention map with the feature map. We participate in the seven basic expression classification sub-challenges of FG-2020 Affective Behavior Analysis in-the-wild Challenge. And we validate our method on the Aff-Wild2 datasets released by the Challenge. The total accuracy (Accuracy) and the unweighted mean (F1) of our method on the validation set are 0.49 and 0.38 respectively, and the final result is 0.42 (0.67 F1-Score + 0.33 Accuracy).

cs.CV↗

arXiv2018

Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition

Compared with facial emotion recognition on categorical model, the dimensional emotion recognition can describe numerous emotions of the real world more accurately. Most prior works of dimensional emotion estimation only considered laboratory data and used video, speech or other multi-modal features. The effect of these methods applied on static images in the real world is unknown. In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images. Firstly, the features of corresponding region(position-level features) are extracted and enhanced automatically by first-level attention mechanism. In the following, we utilize Bi-directional Recurrent Neural Network(Bi-RNN) with self-attention(second-level attention) to make full use of the relationship features of different layers(layer-level features) adaptively. Owing to the inherent complexity of dimensional emotion recognition, we propose a two-stage multi-task learning structure to exploited categorical representations to ameliorate the dimensional representations and estimate valence and arousal simultaneously in view of the correlation of the two targets. The quantitative results conducted on AffectNet dataset show significant advancement on Concordance Correlation Coefficient(CCC) and Root Mean Square Error(RMSE), illustrating the superiority of the proposed framework. Besides, extensive comparative experiments have also fully demonstrated the effectiveness of different components.

cs.CV↗

arXiv2018

Neural CRF transducers for sequence labeling

Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling. Various linear-chain neural CRFs (NCRFs) are developed to implement the non-linear node potentials in CRFs, but still keeping the linear-chain hidden structure. In this paper, we propose NCRF transducers, which consists of two RNNs, one extracting features from observations and the other capturing (theoretically infinite) long-range dependencies between labels. Different sequence labeling methods are evaluated over POS tagging, chunking and NER (English, Dutch). Experiment results show that NCRF transducers achieve consistent improvements over linear-chain NCRFs and RNN transducers across all the four tasks, and can improve state-of-the-art results.

cs.LG↗

arXiv2013

Tumor can originate from not only rare cancer stem cells

Tumors are believed to consist of a heterogeneous population of tumor cells originating from rare cancer stem cells (CSCs). However, emerging evidences show that tumor may also originate from non-CSCs. Here, we give evidences supporting that the number of tumorigenic tumor cells is higher than the number of CSCs and tumor can also derive from non-CSCs. First, we applied an idealized mathematical model and theoretically calculated that non-CSCs could initiate tumor if their proliferation potential was adequate. Next, we demonstrated by experimental studies that 17.7%, 38.6% and 5.2% of tumor cells in murine B16 solid melanoma, H22 hepatoma and Lewis lung carcinoma, respectively, were potentially tumorigenic. We propose that the rare CSCs, if exist, are not the only origination of a tumor.

q-bio.CB↗