Correlation Analysis of Road Freight Transport and Economic Development in Shaanxi Province

Based on the data from 1987-2017 of the Statistical Yearbook of Shaanxi Province, this paper selects Shaanxi Road freight transportation evaluation indicators and economic development evaluation indicators, and uses the method of co-integration test and ADF unit root test to determine whether there is a long-term equilibrium relationship between the indicators. Through the establishment of VAR model and analysis, it demonstrates the impact of road freight transportation on economic development in Shaanxi Province. Based on the impulse impact between the road freight transportation and economic development in Shaanxi Province, the correlation between road freight transportation and economic development in Shaanxi Province is analyzed and studied to provide suggestions for the coordinated development of road freight transportation and economy in Shaanxi Province

Mechanism and application of Lactobacillus in type 2 diabetes-associated periodontitis

Type 2 diabetes mellitus (T2DM) accelerates the progression of periodontitis through diverse pathways. Abnormal immune responses, excessive activation of inflammation, increased levels of advanced glycation end products, and oxidative stress have defined roles in the pathophysiological process of T2DM-associated periodontitis. Furthermore, in the periodontium of diabetic individuals, there are high levels of advanced glycation end-products and glucose. Meanwhile, progress in microbiomics has revealed that dysbacteriosis caused by T2DM also contributes to the progression of periodontitis. Lactobacillus, owing to its fine-tuning function in the local microbiota, has sparked tremendous interest in this field. Accumulating research on Lactobacillus has detailed its beneficial role in both diabetes and oral diseases. In this study, we summarize the newly discovered mechanisms underlying Lactobacillus-mediated improvement of T2DM-associated periodontitis and propose the application of Lactobacillus in the clinic

Un estudio del impacto de las actividades humanas en los murales de las cuevas de Mogao en Dunhuang (China)

[ES] Las cuevas de Mogao o cuevas de Dunhuang, ciudad situada en la provincia de Gansu (República Popular China), es el patrimonio cultural budista más grande y completo del mundo. El conjunto está compuesto por 492 cuevas, ornamentadas con murales y esculturas de un alto valor cultural por lo que la protección de los restos de las grutas de Mogao es de gran relevancia. Este trabajo final de grado analiza los efectos positivos y negativos de las actividades turísticas en las grutas de Mogao, estudia la relación entre la protección de las reliquias culturales y las actividades turísticas y cómo abordar la problemática con el fin de mantener un equilibrio entre ambas.[EN] The Mogao Grottoes or Dunhuang grottoes, a city located in the province of Gansu (People's Republic of China), is the largest and most complete Buddhist cultural heritage in the world. The site consists of 492 caves, decorated with murals and sculptures of high cultural value, which makes the protection of the remains of the Mogao caves very important. This final degree work analyzes the positive and negative effects of tourist activities in the Mogao caves, studies the relationship between the protection of cultural relics and tourist activities and how to address the issue in order to maintain a balance between bothChen, Y. (2020). Un estudio del impacto de las actividades humanas en los murales de las cuevas de Mogao en Dunhuang (China). http://hdl.handle.net/10251/149806TFG

StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation

Unconditional video generation is a challenging task that involves synthesizing high-quality videos that are both coherent and of extended duration. To address this challenge, researchers have used pretrained StyleGAN image generators for high-quality frame synthesis and focused on motion generator design. The motion generator is trained in an autoregressive manner using heavy 3D convolutional discriminators to ensure motion coherence during video generation. In this paper, we introduce a novel motion generator design that uses a learning-based inversion network for GAN. The encoder in our method captures rich and smooth priors from encoding images to latents, and given the latent of an initially generated frame as guidance, our method can generate smooth future latent by modulating the inversion encoder temporally. Our method enjoys the advantage of sparse training and naturally constrains the generation space of our motion generator with the inversion network guided by the initial frame, eliminating the need for heavy discriminators. Moreover, our method supports style transfer with simple fine-tuning when the encoder is paired with a pretrained StyleGAN generator. Extensive experiments conducted on various benchmarks demonstrate the superiority of our method in generating long and high-resolution videos with decent single-frame quality and temporal consistency.Comment: ICCV 2023. Code: https://github.com/johannwyh/StyleInV Project page: https://www.mmlab-ntu.com/project/styleinv/index.htm

A Comprehensive Study on Off-path SmartNIC

SmartNIC has recently emerged as an attractive device to accelerate distributed systems. However, there has been no comprehensive characterization of SmartNIC especially on the network part. This paper presents the first comprehensive study of off-path SmartNIC. Our experimental study uncovers the key performance characteristics of the communication among the client, SmartNIC SoC, and the host. We find without considering SmartNIC hardware architecture, communications with it can cause up to 48% bandwidth degradation due to performance anomalies. We also propose implications to address the anomalies.Comment: This is the short version. Full version will appear at OSDI2

Representing Affect Information in Word Embeddings

A growing body of research in natural language processing (NLP) and natural language understanding (NLU) is investigating human-like knowledge learned or encoded in the word embeddings from large language models. This is a step towards understanding what knowledge language models capture that resembles human understanding of language and communication. Here, we investigated whether and how the affect meaning of a word (i.e., valence, arousal, dominance) is encoded in word embeddings pre-trained in large neural networks. We used the human-labeled dataset as the ground truth and performed various correlational and classification tests on four types of word embeddings. The embeddings varied in being static or contextualized, and how much affect specific information was prioritized during the pre-training and fine-tuning phase. Our analyses show that word embedding from the vanilla BERT model did not saliently encode the affect information of English words. Only when the BERT model was fine-tuned on emotion-related tasks or contained extra contextualized information from emotion-rich contexts could the corresponding embedding encode more relevant affect information