A 2D based Partition Strategy for Solving Ranking under Team Context (RTP)

In this paper, we propose a 2D based partition method for solving the problem of Ranking under Team Context(RTC) on datasets without a priori. We first map the data into 2D space using its minimum and maximum value among all dimensions. Then we construct window queries with consideration of current team context. Besides, during the query mapping procedure, we can pre-prune some tuples which are not top ranked ones. This pre-classified step will defer processing those tuples and can save cost while providing solutions for the problem. Experiments show that our algorithm performs well especially on large datasets with correctness

Structure and regulation of ZCCHC4 in m6A-methylation of 28S rRNA.

N6-methyladenosine (m6A) modification provides an important epitranscriptomic mechanism that critically regulates RNA metabolism and function. However, how m6A writers attain substrate specificities remains unclear. We report the 3.1 Å-resolution crystal structure of human CCHC zinc finger-containing protein ZCCHC4, a 28S rRNA-specific m6A methyltransferase, bound to S-adenosyl-L-homocysteine. The methyltransferase (MTase) domain of ZCCHC4 is packed against N-terminal GRF-type and C2H2 zinc finger domains and a C-terminal CCHC domain, creating an integrated RNA-binding surface. Strikingly, the MTase domain adopts an autoinhibitory conformation, with a self-occluded catalytic site and a fully-closed cofactor pocket. Mutational and enzymatic analyses further substantiate the molecular basis for ZCCHC4-RNA recognition and a role of the stem-loop structure within substrate in governing the substrate specificity. Overall, this study unveils unique structural and enzymatic characteristics of ZCCHC4, distinctive from what was seen with the METTL family of m6A writers, providing the mechanistic basis for ZCCHC4 modulation of m6A RNA methylation

Bank lending and CEO turnover: Evidence from China

To maintain bank relationship, borrowers have motives to discipline themselves by forcing out underperforming CEOs. In this paper, we show that the state ownership in emerging markets renders this disciplinary mechanism ineffective. Using the contract information of bank loans for Chinese listed firms, we find that higher bank loan intensity overall does not affect the probability of forcing out an underperforming CEO. The absence of disciplinary effect is driven by the bank-firm pairs in which either the borrower or the lender is state-owned. However, the disciplinary effect is significant if a firm’s bank loans mostly consist of secured and short-term bank loans. Bank loans increase the likelihood of a forced CEO turnover, especially when joint-equity banks serve as the main lender. Overall, we propose that state ownership is an important factor driving the inefficiency of credit market in emerging countries

Linearized Relative Positional Encoding

Relative positional encoding is widely used in vanilla and linear transformers to represent positional information. However, existing encoding methods of a vanilla transformer are not always directly applicable to a linear transformer, because the latter requires a decomposition of the query and key representations into separate kernel functions. Nevertheless, principles for designing encoding methods suitable for linear transformers remain understudied. In this work, we put together a variety of existing linear relative positional encoding approaches under a canonical form and further propose a family of linear relative positional encoding algorithms via unitary transformation. Our formulation leads to a principled framework that can be used to develop new relative positional encoding methods that preserve linear space-time complexity. Equipped with different models, the proposed linearized relative positional encoding (LRPE) family derives effective encoding for various applications. Experiments show that compared with existing methods, LRPE achieves state-of-the-art performance in language modeling, text classification, and image classification. Meanwhile, it emphasizes a general paradigm for designing broadly more relative positional encoding methods that are applicable to linear transformers. The code is available at https://github.com/OpenNLPLab/Lrpe.Comment: Reviewed by TMLR, decision pending. Yiran Zhong is the corresponding author. Code is available at https://github.com/OpenNLPLab/Lrp

Institutional Ownership and Private Equity Placements: Evidence from Chinese Listed Firms

This paper examines the impact of institutional ownership on the performance of private equity placements (PEPs) for listed firms in China. We find that the presence of institutional investors can alleviate the information asymmetries between listed firms and the market. The market reaction to PEP announcements is significantly smaller if there is a higher portion of institutional shareholdings. Long-term firm operational performance after PEPs is positively correlated with institutional shareholdings. Moreover, we find that the relationship between institutional shareholdings and PEP performance is mainly driven by non-listed corporate investors and mutual funds. Finally, the relationship between PEP performance and institutional shareholdings is stronger in smaller PEP issuers

Large Chinese land carbon sink estimated from atmospheric carbon dioxide data

Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO2) emissions and on the removal of CO2 by land carbon sinks. China is currently the single largest emitter of CO2, responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 20171. Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage2–4, which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO2, measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of −1.11 ± 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions5,6, with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper7. Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions