Empirical study on the impact of intermediate target of monetary policy on real estate market prices in China

In recent years, China's real estate market prices have been rising continuously, with the 9,310.28 yuan/m2 of average price of commercial housing and 9,287 yuan/m2 of residential housing by 2019. The real estate industry is a capital-intensive industry. Thus, the Chinese government began to consider whether the intermediary target of monetary policy has an impact on real estate prices. Firstly, this paper reviews and summarizes the literature and related theories on the selection of the intermediary target of monetary policy and the influence of money supply and interest rate on real estate prices. Secondly, based on the review of relevant literature, this paper makes a theoretical analysis and draws a preliminary conclusion: money supply has a positive impact on real estate prices, while interest rates have a negative impact on real estate prices. Thirdly, this study sorts out the actual data of money supply, interest rates and real estate prices of 1996-2019 China and analyzes them in the descriptive perspective. Next, this study chooses the average price of national commercial housing market, M1, M2 and the 30-day weighted average interest rate of interbank lending as the empirical research variables, and carries out processing on these data. Then, it quantifies the impact of monetary policy intermediary target on real estate prices in China. According to the results, both money supply and interest rates, as the intermediary target of monetary policy, will have a long-term effect on real estate prices. Finally, this study put forward the corresponding policy recommendations.Nos últimos anos, os preços no mercado imobiliário continuaram a aumentar. Em 2019, o preço médio de uma casa comercial foi de 9310, 28 yuan/m2, e o preço médio de uma casa foi de 9287 yuan/m2 . A indústria imobiliária é uma indústria de capital intensivo. Por conseguinte, o governo começou a ponderar se os objectivos de intermediação da política monetária teriam um impacto nos preços dos imóveis. Em primeiro lugar, este artigo apresenta uma revisão e um resumo da literatura e das teorias relevantes sobre a escolha dos objetivos de intermediação da política monetária e sobre o impacto da oferta de dinheiro e das taxas de juro nos preços imobiliários. Em segundo lugar, o artigo faz uma análise teórica baseada na análise da literatura relevante e chega à conclusão preliminar de que a oferta de dinheiro tem um efeito positivo sobre os preços da propriedade, enquanto as taxas de juro têm um efeito negativo sobre os preços da propriedade. Em terceiro lugar, os dados reais sobre a oferta monetária, as taxas de juro e os preços imobiliários na china para o período 1996-2019 são analisados a partir de uma perspectiva descritiva. Seguidamente, foram selecionadas como variáveis de estudo empírico o preço médio do mercado nacional de habitação comercial, M1, M2 e a média ponderada das taxas de empréstimo interbancário a 30 dias. Estes dados são então utilizados para quantificar o impacto dos objetivos de intermediação da política monetária nos preços da propriedade na china. Os resultados mostram que tanto a oferta monetária como as taxas de juro, como objectivos de intermediação da política monetária, têm um impacto a longo prazo nos preços imobiliários. Por último, o estudo apresenta propostas políticas correspondentes

Towards Generalizable Reinforcement Learning for Trade Execution

Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from real deployment. In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. First, we model the optimized trade execution as offline RL with dynamic context (ORDC), where the context represents market variables that cannot be influenced by the trading policy and are collected in an offline manner. Under this framework, we derive the generalization bound and find that the overfitting issue is caused by large context space and limited context samples in the offline setting. Accordingly, we propose to learn compact representations for context to address the overfitting problem, either by leveraging prior knowledge or in an end-to-end manner. To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms. Our experiments on the high-fidelity simulator demonstrate that our algorithms can effectively alleviate overfitting and achieve better performance.Comment: Accepted by IJCAI-2

5 Fluorouracil as firs t line treatment for low risk gestational trophoblastic neoplasia

Purpose: To investigate the efficacy and prognostic factors in response to 5-fluorouracil (5-FU) in lowrisk gestational trophoblastic neoplasia (GTN).Methods: This single-center retrospective study analyzed the hospital records of 204 LRGTN patients admitted to Department of Gynecology, Liaoning Cancer Hospital & Institute of China from 2002 to 2016 for retrieval of their clinical data, chemotherapy regimens, related side-effects, and evaluation of treatment efficacy and prognostic factors.Results: The median progression-free survival (PFS) was 55 months (3 - 190 months). The overall cure rate was 100 %, with no tumor-related deaths. When a single-agent regimen i.e. 5-FU, was selected for initiation of treatment for 132 patients while only 49 of them were treated with chemotherapy, the effective cure rate was 62.88 % (83/132); while the overall drug resistance r was 27.27 % (36/132). For patients with FIGO scores ≥ 4 points, the incidence of drug resistance was 71.43 % (5/7), while the incidence of III/IV myelosuppression was 10.61 % (14/132). A total of 38 patients (18.63 %) received surgical treatment in addition to chemotherapy. A comparison was made between two groups of patients with non-drug resistance, i.e., patients with unexpected GTN diagnosed postoperatively and those who received chemotherapy preoperatively. It was found that the number of courses of GTN chemotherapy for those who were unexpectedly diagnosed postoperatively was more than that for those who received chemotherapy preoperatively (p = 0.004).Conclusion: The single drug (5-FU) was effective in the management of low-risk (LR)-GTN. Treatment failure was related to drug resistance, high tumor score, and severe toxicity. Multi-agent regiments in combination with surgery, were an effective treatment method for GTN. For patients without metastasis and fertility requirements, surgery after chemotherapy significantly shortened the treatment cycle without increasing complications

DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser

Speech-driven 3D facial animation has been an attractive task in both academia and industry. Traditional methods mostly focus on learning a deterministic mapping from speech to animation. Recent approaches start to consider the non-deterministic fact of speech-driven 3D face animation and employ the diffusion model for the task. However, personalizing facial animation and accelerating animation generation are still two major limitations of existing diffusion-based methods. To address the above limitations, we propose DiffusionTalker, a diffusion-based method that utilizes contrastive learning to personalize 3D facial animation and knowledge distillation to accelerate 3D animation generation. Specifically, to enable personalization, we introduce a learnable talking identity to aggregate knowledge in audio sequences. The proposed identity embeddings extract customized facial cues across different people in a contrastive learning manner. During inference, users can obtain personalized facial animation based on input audio, reflecting a specific talking style. With a trained diffusion model with hundreds of steps, we distill it into a lightweight model with 8 steps for acceleration. Extensive experiments are conducted to demonstrate that our method outperforms state-of-the-art methods. The code will be released

An Edge Extraction Algorithm for Weld Pool Based on Component Tree

In order to realize the automation and intelligence of welding process, the visual sensor and image processing technology of weld pool edge feature has become one of the key points. During the course of gas metal arc welding (GMAW), since this kind of welding requires a larger current, it makes the arc very strong and products so many droplets transfer and spatter interference. Therefore it is so difficult to extract the edge of welding pool. A new edge extraction algorithm based on component tree is proposed in the paper. It can realize the image segmentation adaptively using local features, retain the useful edge effectively and remove the false edge and noise as well. The experiments show that this algorithm can get more accurate edge information