IPO underpricing in China growth enterprise market

1 online resource (vi, 53 leaves) : ill.Includes abstract and appendices.Includes bibliographical references (leaf 31).The phenomenon of IPOs’ underpricing has been investigated of stock markets around the world. In this paper, I focus on 203 IPOs from 2009 to 2011 extracted from Shenzhen growth enterprise market. Underpricing is directly related to turnover ratio, initial P/E ratio, prior year’s ROE, subscribe multiple and free float. The study shows that the initial abnormal return on the secondary market is significantly positive. This study also finds that the initial return in the primary market is negatively related to the free float and IPO P/E ratio. And it is positively related to the prior year’s ROE, subscribe multiple and the turnover rate

Improved Approximation Ratios of Fixed-Price Mechanisms in Bilateral Trades

We continue the study of the performance for fixed-price mechanisms in the bilateral trade problem, and improve approximation ratios of welfare-optimal mechanisms in several settings. Specifically, in the case where only the buyer distribution is known, we prove that there exists a distribution over different fixed-price mechanisms, such that the approximation ratio lies within the interval of [0.71, 0.7381]. Furthermore, we show that the same approximation ratio holds for the optimal fixed-price mechanism, when both buyer and seller distributions are known. As a result, the previously best-known (1 - 1/e+0.0001)-approximation can be improved to $0.71$. Additionally, we examine randomized fixed-price mechanisms when we receive just one single sample from the seller distribution, for both symmetric and asymmetric settings. Our findings reveal that posting the single sample as the price remains optimal among all randomized fixed-price mechanisms

Minotaur: A SIMD-Oriented Synthesizing Superoptimizer

Minotaur is a superoptimizer for LLVM's intermediate representation that focuses on integer SIMD instructions, both portable and specific to x86-64. We created it to attack problems in finding missing peephole optimizations for SIMD instructions-this is challenging because there are many such instructions and they can be semantically complex. Minotaur runs a hybrid synthesis algorithm where instructions are enumerated concretely, but literal constants are generated by the solver. We use Alive2 as a verification engine; to do this we modified it to support synthesis and also to support a large subset of Intel's vector instruction sets (SSE, AVX, AVX2, and AVX-512). Minotaur finds many profitable optimizations that are missing from LLVM. It achieves limited speedups on the integer parts of SPEC CPU2017, around 1.3%, and it speeds up the test suite for the libYUV library by 2.2%, on average, and by 1.64x maximum, when targeting an Intel Cascade Lake processor

Rondotia melanoleuca sp. nov., a new wild-mulberry silkworm from China (Lepidoptera, Bombycidae)

Several yellow larvae with black spots were discovered in the wild of Chinese Sichuan and Yunnan provinces, and were further raised in captivity. Reared adults exhibit a striking black and white wing pattern, and they represent unequivocally a new species, here described as Rondotia melanoleuca sp. nov. Molecular analyses suggest that this species could be sister to all previously known species of Rondotia

The SJTU X-LANCE Lab System for CNSRC 2022

This technical report describes the SJTU X-LANCE Lab system for the three tracks in CNSRC 2022. In this challenge, we explored the speaker embedding modeling ability of deep ResNet (Deeper r-vector). All the systems are only trained on the Cnceleb training set and we use the same systems for the three tracks in CNSRC 2022. In this challenge, our system ranks the first place in the fixed track of speaker verification task. Our best single system and fusion system achieve 0.3164 and 0.2975 minDCF respectively. Besides, we submit the result of ResNet221 to the speaker retrieval track and achieve 0.4626 mAP. More importantly, we have helped the wespeaker toolkit reproduce our result: https://github.com/wenet-e2e/wespeaker

RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.Comment: 8 pages, 4 figures. Conference paper accepted by AAAI 202