The Path Towards Smart Cities in China: From the Case of Shanghai Expo 2010

With the development of the digital technology, physical buildings are becoming more and more intelligent, high-efficiency and ecological; partial physical situational functions could be transferred to the virtual building world in a more efficient, intensive and convenient way. With these great achievement, we should believe that the digital technology will create more chances for the future of the smart buildings, and the smart city will be the most important foundation. To create the best human dwelling environment, we should construct smart buildings on the microscopic level and smart city on the macroscopic level. Smart city is not just about technology, but also the all-round innovation of the urban space, economic, society, system and management. The promotion for smart city will improve the quality of the urbanization, and the integrated development of the informatization, industrialization and urbanization, which will result in a wide influence on the city development and reform. Being a developing country severely hit by information and technology revolution, China met a small climax of smart city construction after 2010 Expo. As the theme of 2010 Expo, the idea of 'better city better life' was implemented through the process of the planning, construction, development operation and utilization in the Expo Park of 5.28 km2 area, which made the best use of information and intelligent technology, as well as the idea of sustainable development. Intelligent and ecological buildings in Shanghai 2010 Expo have been the most important practice in China, which had effected profoundly on the construction of smart buildings and smart city. At the beginning, this paper will introduce the background of of 'smart city', as well as it's meaning and feature. Then, through the case study of 2010 Expo, this paper will present a real scene of the development of smart cities in China. It reflected the path to smart cities in China, and the policies and achievements in large pilot cities during this process. It will also talk about the influence on urban planning and authorities. Obviously, when we talk about smart city, we should not only pay attention to its present, but also look forward to future, which is the last part in this paper

Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring

Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation. However, we observe that these approaches often generate wrong question words or keywords and copy answer-irrelevant words from the input. We believe that lacking global question semantics and exploiting answer position-awareness not well are the key root causes. In this paper, we propose a neural question generation model with two concrete modules: sentence-level semantic matching and answer position inferring. Further, we enhance the initial state of the decoder by leveraging the answer-aware gated fusion mechanism. Experimental results demonstrate that our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO datasets. Owing to its generality, our work also improves the existing models significantly.Comment: Revised version of paper accepted to Thirty-fourth AAAI Conference on Artificial Intelligenc

A New Image Quality Database for Multiple Industrial Processes

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types

Reduced Expression of DNA Repair and Redox Signaling Protein APE1/Ref-1 Impairs Human Pancreatic Cancer Cell Survival, Proliferation, and Cell Cycle Progression

Pancreatic cancer is a deadly disease that is virtually never cured. Understanding the chemoresistance intrinsic to this cancer will aid in developing new regimens. High expression of APE1/Ref-1, a DNA repair and redox signaling protein, is associated with resistance, poor outcome, and angiogenesis; little is known in pancreatic cancer. Immunostaining of adenocarcinoma shows greater APE1/Ref-1 expression than in normal pancreas tissue. A decrease in APE1/Ref-1 protein levels results in pancreatic cancer cell growth inhibition, increased apoptosis, and altered cell cycle progression. Endogenous cell cycle inhibitors increase when APE1/ Ref-1 is reduced, demonstrating its importance to proliferation and growth of pancreatic cancer

FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models

Large Language models (LLMs) usually rely on extensive training datasets. In the financial domain, creating numerical reasoning datasets that include a mix of tables and long text often involves substantial manual annotation expenses. To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models. First, we compile a list of common financial formulas and construct a graph based on the variables these formulas employ. We then augment the formula set by combining those that share identical variables as new elements. Specifically, we explore formulas obtained by manual annotation and merge those formulas with shared variables by traversing the constructed graph. Finally, utilizing GPT-3.5, we generate financial question-answering data that encompasses both tabular information and long textual content, building on the collected formula set. Our experiments demonstrate that synthetic data generated by FinLLMs effectively enhances the performance of several large-scale numerical reasoning models in the financial domain, outperforming two established benchmark financial question-answering datasets.Comment: Under submission of IEEE Transaction

A potential anti-tumor herbal medicine, Corilagin, inhibits ovarian cancer cell growth through blocking the TGF-β signaling pathways

BACKGROUND: Phyllanthus niruri L. is a well-known hepatoprotective and antiviral medicinal herb. Recently, we identified Corilagin as a major active component with anti-tumor activity in this herbal medicine. Corilagin is a member of the tannin family that has been discovered in many medicinal plants and has been used as an anti-inflammatory agent. However, there have been few reports of the anti-tumor effects of Corilagin, and its anti-tumor mechanism has not been investigated clearly. The aim of the present study is to investigate the anticancer properties of Corilagin in ovarian cancer cells. METHODS: The ovarian cancer cell lines SKOv3ip, Hey and HO-8910PM were treated with Corilagin and analyzed by Sulforhodamine B (SRB) cell proliferation assay, flow cytometry, and reverse phase protein array (RPPA). Corilagin was delivered intraperitoneally to mice bearing SKOv3ip xenografts. RESULTS: Corilagin inhibited the growth of the ovarian cancer cell lines SKOv3ip and Hey, with IC50 values of less than 30 μM, while displaying low toxicity against normal ovarian surface epithelium cells, with IC50 values of approximately 160 μM. Corilagin induced cell cycle arrest at the G2/M stage and enhanced apoptosis in ovarian cancer cells. Immunoblotting assays demonstrated that Cyclin B1, Myt1, Phospho-cdc2 and Phospho-Weel were down-regulated after Corilagin treatment. Xenograft tumor growth was significantly lower in the Corilagin-treated group compared with the untreated control group (P <0.05). More interestingly, Corilagin inhibited TGF-β secretion into the culture supernatant of all tested ovarian cancer cell lines and blocked the TGF-β-induced stabilization of Snail. In contrast, a reduction of TGF-β secretion was not observed in cancer cells treated with the cytotoxic drug Paclitaxel, suggesting that Corilagin specifically targets TGF-β secretion. Corilagin blocked the activation of both the canonical Smad and non-canonical ERK/AKT pathways. CONCLUSIONS: Corilagin extracted from Phyllanthus niruri L. acts as a natural, effective therapeutic agent against the growth of ovarian cancer cells via targeted action against the TGF-β/AKT/ERK/Smad signaling pathways

Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation

Many methods of semantic image segmentation have borrowed the success of open compound domain adaptation. They minimize the style gap between the images of source and target domains, more easily predicting the accurate pseudo annotations for target domain's images that train segmentation network. The existing methods globally adapt the scene style of the images, whereas the object styles of different categories or instances are adapted improperly. This paper proposes the Object Style Compensation, where we construct the Object-Level Discrepancy Memory with multiple sets of discrepancy features. The discrepancy features in a set capture the style changes of the same category's object instances adapted from target to source domains. We learn the discrepancy features from the images of source and target domains, storing the discrepancy features in memory. With this memory, we select appropriate discrepancy features for compensating the style information of the object instances of various categories, adapting the object styles to a unified style of source domain. Our method enables a more accurate computation of the pseudo annotations for target domain's images, thus yielding state-of-the-art results on different datasets.Comment: Accepted by NeurlPS202