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港股上市公司ESG气候相关信息披露新规解读
香港联合交易所有限公司2024年就《咨询文件：优化环境、社会及管治框架下的气候相关信息披露》刊发了咨询总结，要求港股上市公司于2025年1月1日起分层次、分阶段实施气候相关信息披露新规定，这是我国上市公司ESG信息披露发展进程中的里程碑。本文通过对港股上市公司ESG信息披露要求进展的梳理、对ESG气候相关信息披露新规定的系统分析和解读、提出我国内地ESG气候相关信息披露建设的启示三部分展开，深入剖析港股上市公司ESG气候相关信息披露新规的核心内容及披露要求，为我国上市公司气候相关信息披露政策的制定及实施提供参考。
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Generalized likelihood ratio method for stochastic models with uniform random numbers as inputs
We propose a new unbiased stochastic gradient estimator for a family of stochastic models driven by uniform random numbers as inputs. Dropping the requirement that the tails of the density of the input random variables decay smoothly, the estimator extends the applicability of the generalized likelihood ratio (GLR) method. We demonstrate the new estimator for several general classes of input random variates, including independent inverse transform random variates and dependent input random variables governed by an Archimedean copula. We show how the new estimator works in settings such as density estimation, and we illustrate applications to credit risk derivatives. Numerical experiments substantiate broad applicability and flexibility in dealing with discontinuities in the
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Detection of interictal epileptiform discharges using transformer based deep neural network for patients with self-limited epilepsy with centrotemporal spikes
已知头皮可见发作间期癫痫样放电 （IED） 对于诊断伴中央颞区棘波的自限性癫痫 （SeLECTS） 至关重要。然而，在脑电图 （EEG） 记录上标记和映射这些放电是重复且耗时的，需要大量繁琐和仔细的努力。具有高精度和泛化能力的全自动 IED 检测算法非常受欢迎。（#br）方法（#br）我们设计了一个高效的数据预处理-特征提取-分类工作流程，该工作流程由独立的成分分析、基于临床知识的波形字典和基于变压器的深度神经网络分类器组成，以识别与每个单独的 IED 相关的时序和记录电极以及偶极子模式。（#br）结果（#br）共收集了 44,908 例 SeLECTS 患者视频脑电图记录中标记的 IED。所提出的程序在由 8 名患者组成的测试数据集中实现了 99.8% 的平均准确率和 97.8% 的灵敏度，非癫痫性脑电图记录的误报率为每小时 1.8 次。测试集中的 8 名 SeLECTS 患者均被检测到偶极子模式，其中 5 名患者在睡眠阶段的 IED 频率比清醒期高 10 倍。通过在公开可用的 TUEV 数据集中进行跨数据集评估，进一步证实了该程序的泛化能力。（#br）结论（#br）所提出的全自动 IED 检测程序具有较高的准确率和较好的泛化能力。（#br）意义 （#br）所提出的程序可以显着减轻神经科医生的工作负担，并为进一步癫痫研究中的 IED 分析提供定量工具
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Testing sufficiency for transfer learning
Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes it difficult to estimate high-dimensional statistical models based on target data with limited sample size. Then how to borrow information from another large sized source data for more accurate target model estimation becomes an interesting problem. This leads to the useful idea of transfer learning. Various estimation methods in this regard have been developed recently. In this work, we study transfer learning from a different perspective. Specifically, we consider here the problem of testing for transfer learning sufficiency. We denote transfer learning sufficiency to be the null hypothesis. It refers to the situation that, with the help of the source data, the useful information contained in the feature vectors of the target data can be sufficiently extracted for predicting the interested target response. Therefore, the rejection of the null hypothesis implies that information useful for prediction remains in the feature vectors of the target data and thus calls for further exploration. To this end, we develop a novel testing procedure and a centralized and standardized test statistic, whose asymptotic null distribution is analytically derived. Simulation studies are presented to demonstrate the finite sample performance of the proposed method. A deep learning related real data example is presented for illustration purpose.