BiSPL: Bidirectional Self-Paced Learning for recognition from web data

Deep learning (DL) is inherently subject to the requirement of a large amount of well-labeled data, which is expensive and time-consuming to obtain manually. In order to broaden the reach of DL, leveraging free web data becomes an attractive strategy to alleviate the issue of data scarcity. However, directly utilizing collected web data to train a deep model is ineffective because of the mixed noisy data. To address such problems, we develop a novel bidirectional self-paced learning (BiSPL) framework which reduces the effect of noise by learning from web data in a meaningful order. Technically, the BiSPL framework consists of two essential steps. Relying on distances defined between web samples and labeled source samples, first, the web samples with short distances are sampled and combined to form a new training set. Second, based on the new training set, both easy and hard samples are initially employed to train deep models for higher stability, and hard samples are gradually dropped to reduce the noise as the training progresses. By iteratively alternating such steps, deep models converge to a better solution. We mainly focus on the fine-grained visual classification (FGVC) tasks because their corresponding datasets are generally small and therefore face a more significant data scarcity problem. Experiments conducted on six public FGVC tasks demonstrate that our proposed method outperforms the state-of-the-art approaches. Especially, BiSPL suffices to achieve the highest stable performance when the scale of the well-labeled training set decreases dramatically

Looking into gait for perceiving emotions via bilateral posture and movement graph convolutional networks

Emotions can be perceived from a person's gait, i.e., their walking style. Existing methods on gait emotion recognition mainly leverage the posture information as input, but ignore the body movement, which contains complementary information for recognizing emotions evoked in the gait. In this paper, we propose a Bilateral Posture and Movement Graph Convolutional Network (BPM-GCN) that consists of two parallel streams, namely posture stream and movement stream, to recognize emotions from two views. The posture stream aims to explicitly analyse the emotional state of the person. Specifically, we design a novel regression constraint based on the hand-engineered features to distill the prior affective knowledge into the network and boost the representation learning. The movement stream is designed to describe the intensity of the emotion, which is an implicitly cue for recognizing emotions. To achieve this goal, we employ a higher-order velocity-acceleration pair to construct graphs, in which the informative movement features are utilized. Besides, we design a PM-Interacted feature fusion mechanism to adaptively integrate the features from the two streams. Therefore, the two streams collaboratively contribute to the performance from two complementary views. Extensive experiments on the largest benchmark dataset Emotion-Gait show that BPM-GCN performs favorably against the state-of-the-art approaches (with at least 4.59% performance improvement). The source code is released on https://github.com/exped1230/BPM-GCN

APSE: Attention-aware polarity-sensitive embedding for emotion-based image retrieval

With the popularity of social media, an increasing number of people are accustomed to expressing their feelings and emotions online using images and videos. An emotion-based image retrieval (EBIR) system is useful for obtaining visual contents with desired emotions from a massive repository. Existing EBIR methods mainly focus on modeling the global characteristics of visual content without considering the crucial role of informative regions of interest in conveying emotions. Further, they ignore the hierarchical relationships between coarse polarities and fine categories of emotions. In this paper, we design an attention-aware polarity-sensitive embedding (APSE) network to address these issues. First, we develop a hierarchical attention mechanism to automatically discover and model the informative regions of interest. Specifically, both polarity-and emotion-specific attended representations are aggregated for discriminative feature embedding. Second, we propose a generated emotion-pair (GEP) loss to simultaneously consider the inter-and intra-polarity relationships of the emotion labels. Moreover, we adaptively generate negative examples of different hard levels in the feature space guided by the attention module to further improve the performance of feature embedding. Extensive experiments on four popular benchmark datasets demonstrate that the proposed APSE method outperforms the state-of-the-art EBIR approaches by a large margin

Whole exome sequencing identifies frequent somatic mutations in cell-cell adhesion genes in chinese patients with lung squamous cell carcinoma

Lung squamous cell carcinoma (SQCC) accounts for about 30% of all lung cancer cases. Understanding of mutational landscape for this subtype of lung cancer in Chinese patients is currently limited. We performed whole exome sequencing in samples from 100 patients with lung SQCCs to search for somatic mutations and the subsequent target capture sequencing in another 98 samples for validation. We identified 20 significantly mutated genes, including TP53, CDH10, NFE2L2 and PTEN. Pathways with frequently mutated genes included those of cell-cell adhesion/Wnt/Hippo in 76%, oxidative stress response in 21%, and phosphatidylinositol-3-OH kinase in 36% of the tested tumor samples. Mutations of Chromatin regulatory factor genes were identified at a lower frequency. In functional assays, we observed that knockdown of CDH10 promoted cell proliferation, soft-agar colony formation, cell migration and cell invasion, and overexpression of CDH10 inhibited cell proliferation. This mutational landscape of lung SQCC in Chinese patients improves our current understanding of lung carcinogenesis, early diagnosis and personalized therapy

Text to speech synthesis and voice conversion system

61 p.Speech synthesis is a rapidly evolving field of research in computer science which involves the simulation of human speech by computers. The most commonly cited application in this field is the text-to-speech (TTS) synthesizers. During recent years, much effort has been made to improve the performance of the synthesized speech, however, this problem is still far from being completed. The main problem in TTS system is to improve the naturalness and intelligence of the synthesized speech. In this project, we will focus on building a TTS (Text to Speech) and voice conversion system based on Matlab.Master of Science (Signal Processing

Numerical Simulation on goaf with different vertical distances in high-drainage roadways

High drainage roadway horizon parameters not only influence goaf drainage effect, but also have effect on the air leakage field and spontaneous combustion three zones’ distribution in the goaf. Combined with mine fire-prevention and goaf spontaneous combustion three zones recognition, it was put forward to stimulate the distribution law of goaf air leakage field and spontaneous combustion three zones with fluid mechanics software FLUENT. The results showed that the width of goaf oxidization and heat accumulation zone increased with high drainage roadway vertically. Through stimulation study, it supplies a certain method to reasonablely optimizatate high drainage roadway vertical parameters, which to a great extent ensures mine safety production, fire and gas prevention

Study on the mechanism of water inrush in the arid western mining area

The analysis found that the coal mining process in the western mining area has the mining loss and disaster effect of the water-rich aquifer of the coal seam roof, which is mainly manifested by the overburden water in the roof. On this basis, the formation and development of the separation water of the roof is proposed, and the mechanism of the water inrush from the layer is revealed. It is found that there is hydrostatic pressure and hydrodynamic pressure in the separated water, under the combined action of bed separation water pressure, the mining-induced fracture and water-isolation layer tension fracture are connected, which causes water inrushing in the coal working face of the mine, and provides a theoretical guarantee for the large-scale development of coal resources in western mining areas