Identity and Support for Political Communities Based on Language Choice Data in Tibet

National sentiment and identities are affective orientations of diffuse political support toward political communities. Language choice is suggested be a reliable indicator of community identity in Tibet for theoretical, historical, and practical reasons. Tibetan, Mandarin, and English are three language choices that are used to indicate three identities and three political communities in this paper. By using the language orientations of Tibetan high school students as the indicators of their community identities, I demonstrate the patterns of identity of Tibetan students with survey data. I also use empirical evidence to test the attitudinal and demographic sources of the students¡¯ variation in their community identities. The results reconfirm that the constructivist theory of the identity construction, which includes the primordialist and circumstantialist factors, has a fairly good explanatory power regarding the community identities of students in Tibet. And policy implications are offered from the educational perspective

Effect of Fuzheng Huayu formula and its actions against liver fibrosis

Liver fibrosis is a common histological process to develop into cirrhosis in various chronic liver diseases including chronic hepatitis and fatty liver. Therefore anti-liver fibrosis is very important strategy to treat chronic liver diseases. Fuzheng Huayu (FZHY), a preparation containing herbs such as Radix Salvia Miltiorrhizae, Cordyceps, Semen Persicae, was formulated on the basis of Chinese medicine theory in treating liver fibrosis and was approved. Pharmacological studies and clinical trials demonstrate that FZHY has a significant effect against liver fibrosis and that many of the pharmacological actions are attributable to the effect. This article reviews the effects and actions of FZHY, in particular the effects observed from clinical trials in treating liver fibrosis caused by chronic hepatitis B and the actions on inhibition of hepatic stellate cell activation, protection of hepatocytes and inhibition of hepatic sinusoidal capillarization. This article also reviews the coordinated effects of the constituent herbs of FZHY and the actions of their active compounds such as salvianonic acid B (SA-B) on liver fibrosis

SkCoder: A Sketch-based Approach for Automatic Code Generation

Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy the content from similar code snippets to obtain better performance. Practically, human developers recognize the content in the similar code that is relevant to their needs, which can be viewed as a code sketch. The sketch is further edited to the desired code. However, existing copy-based approaches ignore the code sketches and tend to repeat the similar code without necessary modifications, which leads to generating wrong results. In this paper, we propose a sketch-based code generation approach named SkCoder to mimic developers' code reuse behavior. Given a natural language requirement, SkCoder retrieves a similar code snippet, extracts relevant parts as a code sketch, and edits the sketch into the desired code. Our motivations are that the extracted sketch provides a well-formed pattern for telling models "how to write". The post-editing further adds requirement-specific details to the sketch and outputs the complete code. We conduct experiments on two public datasets and a new dataset collected by this work. We compare our approach to 20 baselines using 5 widely used metrics. Experimental results show that (1) SkCoder can generate more correct programs, and outperforms the state-of-the-art - CodeT5-base by 30.30%, 35.39%, and 29.62% on three datasets. (2) Our approach is effective to multiple code generation models and improves them by up to 120.1% in Pass@1. (3) We investigate three plausible code sketches and discuss the importance of sketches. (4) We manually evaluate the generated code and prove the superiority of our SkCoder in three aspects.Comment: Accepted by the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023

Low-Level Saturated Fatty Acid Palmitate Benefits Liver Cells by Boosting Mitochondrial Metabolism via CDK1-SIRT3-CPT2 Cascade.

Saturated fatty acids (SFAs) (the "bad" fat), especially palmitate (PA), in the human diet are blamed for potential health risks such as obesity and cancer because of SFA-induced lipotoxicity. However, epidemiological results demonstrate a latent benefit of SFAs, and it remains elusive whether a certain low level of SFAs is physiologically essential for maintaining cell metabolic hemostasis. Here, we demonstrate that although high-level PA (HPA) indeed induces lipotoxic effects in liver cells, low-level PA (LPA) increases mitochondrial functions and alleviates the injuries induced by HPA or hepatoxic agent carbon tetrachloride (CCl4). LPA treatment in mice enhanced liver mitochondrial activity and reduced CCl4 hepatotoxicity with improved blood levels of aspartate aminotransferase (AST), alanine transaminase (ALT), and mitochondrial aspartate transaminase (m-AST). LPA-mediated mitochondrial homeostasis is regulated by CDK1-mediated SIRT3 phosphorylation, which in turn deacetylates and dimerizes CPT2 to enhance fatty acid oxidation. Thus, an advantageous effect is suggested by the consumption of LPA that augments mitochondrial metabolic homeostasis via CDK1-SIRT3-CPT2 cascade

Modulation Design and Optimization for RIS-Assisted Symbiotic Radios

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR), the RIS acts as a secondary transmitter by modulating its information bits over the incident primary signal and simultaneously assists the primary transmission, then a cooperative receiver is used to jointly decode the primary and secondary signals. Most existing works of SR focus on using RIS to enhance the reflecting link while ignoring the ambiguity problem for the joint detection caused by the multiplication relationship of the primary and secondary signals. Particularly, in case of a blocked direct link, joint detection will suffer from severe performance loss due to the ambiguity, when using the conventional on-off keying and binary phase shift keying modulation schemes for RIS. To address this issue, we propose a novel modulation scheme for RIS-assisted SR that divides the phase-shift matrix into two components: the symbol-invariant and symbol-varying components, which are used to assist the primary transmission and carry the secondary signal, respectively. To design these two components, we focus on the detection of the composite signal formed by the primary and secondary signals, through which a problem of minimizing the bit error rate (BER) of the composite signal is formulated to improve both the BER performance of the primary and secondary ones. By solving the problem, we derive the closed-form solution of the optimal symbol-invariant and symbol-varying components, which is related to the channel strength ratio of the direct link to the reflecting link. Moreover, theoretical BER performance is analyzed. Finally, simulation results show the superiority of the proposed modulation scheme over its conventional counterpart.Comment: 16 pages,15 figure

Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization

The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and document has become an important and urgent task. Most of the current evaluation metrics are adopted from the question answering (QA) or natural language inference (NLI) task. However, the application of QA-based metrics is extremely time-consuming in practice while NLI-based metrics are lack of interpretability. In this paper, we propose a cloze-based evaluation framework called ClozE and show the great potential of the cloze-based metric. It inherits strong interpretability from QA, while maintaining the speed of NLI- level reasoning. We demonstrate that ClozE can reduce the evaluation time by nearly 96% relative to QA-based metrics while retaining their interpretability and performance through experiments on six human-annotated datasets and a meta-evaluation benchmark GO FIGURE (Gabriel et al., 2021). Finally, we discuss three important facets of ClozE in practice, which further shows better overall performance of ClozE compared to other metrics.Comment: The manuscript for JAI

What Matters for 3D Scene Flow Network

3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it encodes the point motion between two consecutive frames. Thus, it is critical for the flow embeddings to capture the correct overall direction of the motion. However, previous works only search locally to determine a soft correspondence, ignoring the distant points that turn out to be the actual matching ones. In addition, the estimated correspondence is usually from the forward direction of the adjacent point clouds, and may not be consistent with the estimated correspondence acquired from the backward direction. To tackle these problems, we propose a novel all-to-all flow embedding layer with backward reliability validation during the initial scene flow estimation. Besides, we investigate and compare several design choices in key components of the 3D scene flow network, including the point similarity calculation, input elements of predictor, and predictor & refinement level design. After carefully choosing the most effective designs, we are able to present a model that achieves the state-of-the-art performance on FlyingThings3D and KITTI Scene Flow datasets. Our proposed model surpasses all existing methods by at least 38.2% on FlyingThings3D dataset and 24.7% on KITTI Scene Flow dataset for EPE3D metric. We release our codes at https://github.com/IRMVLab/3DFlow.Comment: Accepted by ECCV 202

A review of enhancement of biohydrogen productions by chemical addition using a supervised machine learning method

In this work, the impact of chemical additions, especially nano‐particles (NPs), was quan-titatively analyzed using our constructed artificial neural networks (ANNs)‐response surface methodology (RSM) algorithm. Fe‐based and Ni‐based NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Fe‐based NPs and ions, but not for Ni‐based NPs and ions. An optimal range of particle size (86–120 nm) and Ni‐ion/NP concentration (81–120 mg L−1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40–50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ \u3e Mg2+ \u3e Cu2+ \u3e NH4+ \u3e K+

Exploration of Macro-Micro Biomarkers for Dampness-Heat Syndrome Differentiation in Different Diseases

Increased attention is being paid to traditional Chinese medicine (TCM) as a complementary and alternative medicine to provide an effective approach for personalized diagnosis and clinical treatment. TMC performs treatment based on differentiation of TCM syndrome (ZHENG), which may identify special phenotypes by symptoms and signs of patients even if they are in different diseases. There has, however, been skepticism and criticism because syndrome classification only depends on observation, knowledge, and clinical experience of TCM practitioners, which lacks objectivity and repeatability. In order to transform syndrome classification into mainstream medicine, we introduce a macro-micro approach that combines symptoms, clinical indicators, and metabolites. The present paper explores the macro-micro biomarkers of dampness-heat syndrome in chronic hepatitis B and nonalcoholic fatty liver patients, which could provide the basis for developing a possible population-screening tool for selecting target individuals and creating an evaluation index for personalized treatment