An Empirical Research of Concentration of China’s Civil Aviation Industry

With China’s sustained and rapid economic development, China’s civil aviation industry gradually market-oriented, and has undergone several major reforms, gradual deregulation. At the same time, industry concentration showed a gradual downward trend. The internationally accepted measure of industrial concentration of two indicators: Industry moderate and HHI index. We described and analyzed the industry concentration and development trends on China’s civil aviation industry, and we got the main factors of the change of industry concentration. Also put forward policy recommendations

DistilXLSR: A Light Weight Cross-Lingual Speech Representation Model

Multilingual self-supervised speech representation models have greatly enhanced the speech recognition performance for low-resource languages, and the compression of these huge models has also become a crucial prerequisite for their industrial application. In this paper, we propose DistilXLSR, a distilled cross-lingual speech representation model. By randomly shuffling the phonemes of existing speech, we reduce the linguistic information and distill cross-lingual models using only English data. We also design a layer-jumping initialization method to fully leverage the teacher's pre-trained weights. Experiments on 2 kinds of teacher models and 15 low-resource languages show that our method can reduce the parameters by 50% while maintaining cross-lingual representation ability. Our method is proven to be generalizable to various languages/teacher models and has the potential to improve the cross-lingual performance of the English pre-trained models.Comment: Accepted by INTERSPEECH 202

CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model

With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese landscape painting generation due to the lack of data and limited modeling capabilities. In this work, we propose a controllable Chinese landscape painting generation method named CCLAP, which can generate painting with specific content and style based on Latent Diffusion Model. Specifically, it consists of two cascaded modules, i.e., content generator and style aggregator. The content generator module guarantees the content of generated paintings specific to the input text. While the style aggregator module is to generate paintings of a style corresponding to a reference image. Moreover, a new dataset of Chinese landscape paintings named CLAP is collected for comprehensive evaluation. Both the qualitative and quantitative results demonstrate that our method achieves state-of-the-art performance, especially in artfully-composed and artistic conception. Codes are available at https://github.com/Robin-WZQ/CCLAP.Comment: 8 pages,13 figure

Position-Aware Contrastive Alignment for Referring Image Segmentation

Referring image segmentation aims to segment the target object described by a given natural language expression. Typically, referring expressions contain complex relationships between the target and its surrounding objects. The main challenge of this task is to understand the visual and linguistic content simultaneously and to find the referred object accurately among all instances in the image. Currently, the most effective way to solve the above problem is to obtain aligned multi-modal features by computing the correlation between visual and linguistic feature modalities under the supervision of the ground-truth mask. However, existing paradigms have difficulty in thoroughly understanding visual and linguistic content due to the inability to perceive information directly about surrounding objects that refer to the target. This prevents them from learning aligned multi-modal features, which leads to inaccurate segmentation. To address this issue, we present a position-aware contrastive alignment network (PCAN) to enhance the alignment of multi-modal features by guiding the interaction between vision and language through prior position information. Our PCAN consists of two modules: 1) Position Aware Module (PAM), which provides position information of all objects related to natural language descriptions, and 2) Contrastive Language Understanding Module (CLUM), which enhances multi-modal alignment by comparing the features of the referred object with those of related objects. Extensive experiments on three benchmarks demonstrate our PCAN performs favorably against the state-of-the-art methods. Our code will be made publicly available.Comment: 12 pages, 6 figure

Patch Is Not All You Need

Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks. However, their inherent reliance on sequential input enforces the manual partitioning of images into patch sequences, which disrupts the image's inherent structural and semantic continuity. To handle this, we propose a novel Pattern Transformer (Patternformer) to adaptively convert images to pattern sequences for Transformer input. Specifically, we employ the Convolutional Neural Network to extract various patterns from the input image, with each channel representing a unique pattern that is fed into the succeeding Transformer as a visual token. By enabling the network to optimize these patterns, each pattern concentrates on its local region of interest, thereby preserving its intrinsic structural and semantic information. Only employing the vanilla ResNet and Transformer, we have accomplished state-of-the-art performance on CIFAR-10 and CIFAR-100, and have achieved competitive results on ImageNet

ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation

In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally learn such concepts in an optimization-based manner, yet bringing excessive computation or memory burden. In this paper, we instead propose a learning-based encoder, which consists of a global and a local mapping networks for fast and accurate customized text-to-image generation. In specific, the global mapping network projects the hierarchical features of a given image into multiple new words in the textual word embedding space, i.e., one primary word for well-editable concept and other auxiliary words to exclude irrelevant disturbances (e.g., background). In the meantime, a local mapping network injects the encoded patch features into cross attention layers to provide omitted details, without sacrificing the editability of primary concepts. We compare our method with existing optimization-based approaches on a variety of user-defined concepts, and demonstrate that our method enables high-fidelity inversion and more robust editability with a significantly faster encoding process. Our code is publicly available at https://github.com/csyxwei/ELITE.Comment: Accepted by ICCV 2023, oral presentation. Code: https://github.com/csyxwei/ELIT

PO-023 The Effects of Aerobic Exercise on Alternative Splicing of PKC δI pre-mRNA

Objective Alternative splicing of genes is the main way to produce large numbers of proteins, but the mechanism is unclear. The aim of this study was to evaluated the effect of aerobic exercise on PKC δI pre-mRNA alternative splicing. Further, to explore the effect of aerobic exercise on SFRS10 concentration. Because the PKCδ1 is involved in the regulation of adipocyte differentiation and splice factor SFRS10 regulates alternative of PKCδ1, explore the mechanism of PKCδ1 alternative splicing, understand the role of the alternative splicing variants, to provide the theory basis for the mechanism of aerobic exercise reduce the incidence of obesity. Methods C57BL/6 male mice were randomly divided into normal quiet group, normal exercise group, obese and quiet group, and obese exercise group. The exercise group performed aerobic exercise for 8 weeks. The intensity of aerobic exercise was: running platform slope is 0, speed 10 m/min, 1 h/time, 1 time/day, 6 times/week for a total of 8 weeks. Immediately after exercise, the cDNA was extracted from liver and adipose tissue. The contents of PKCδ1 and SFRS10 in liver and adipose tissue were determined by PCR and RT-PCR. Liver and fat were stained by oil red O staining to observe lipid droplet changes. And the mouse's Lee's index and blood lipids were determined. Results  Lee's index = 3√ (body weight * 1000) / body length, Lee's index of obese mice decreased significantly after aerobic exercise, in addition, after aerobic exercise, total cholesterol (TC), triglyceride (TG) and low density Lipoprotein cholesterol (LDL-C) also showed a downward trend (P < 0.05), while high-density lipoprotein cholesterol (HDL-C) increased (P < 0.05); oil red O staining results showed lipid droplets become smaller after aerobic exercise. The results of PCR and RT-PCR showed in the obese and quiet group than in the normal quiet group, the content of PKCδ1-FL decreased, the content of PKCδ1-△Exon9 increased, and the content of SFRS10 decreased. In the normal exercise group than in the normal quiet group and in the obese exercise group than in the obese and quiet group, the PKCδ1-FL content increased, the PKCδ1-△Exon9 content decreased, and the SFRS10 content increased. Conclusions  Aerobic exercise can significantly increase the content of PKCδ1-FL and SFRS10. PKCδ1-FL inhibits the formation of adipocytes, SFRS10 promotes the inclusion of PKCδ1 exon 9, and there is a molecular mechanism of alternative splicing between PKCδ1 and SFRS10