Observational data have been actively used to estimate treatment effect,
driven by the growing availability of electronic health records (EHRs).
However, EHRs typically consist of longitudinal records, often introducing
time-dependent confoundings that hinder the unbiased estimation of treatment
effect. Inverse probability of treatment weighting (IPTW) is a widely used
propensity score method since it provides unbiased treatment effect estimation
and its derivation is straightforward. In this study, we aim to utilize IPTW to
estimate treatment effect in the presence of time-dependent confounding using
claims records. Previous studies have utilized propensity score methods with
features derived from claims records through feature processing, which
generally requires domain knowledge and additional resources to extract
information to accurately estimate propensity scores. Deep sequence models,
particularly recurrent neural networks and self-attention-based architectures,
have demonstrated good performance in modeling EHRs for various downstream
tasks. We propose that these deep sequence models can provide accurate IPTW
estimation of treatment effect by directly estimating the propensity scores
from claims records without the need for feature processing. We empirically
demonstrate this by conducting comprehensive evaluations using synthetic and
semi-synthetic datasets.

利用倒数概率处理权重的方法和深度序列模型通过渗透记录直接估计治疗效果，解决时间相关混杂对结果估计的偏差问题。

深度序列模型的治疗反向概率加权使得能够从电子健康记录中准确估计治疗效应

Inverse Probability of Treatment Weighting with Deep Sequence Models  Enables Accurate treatment effect Estimation from Electronic Health Records

Transformers have become one of the foundational architectures in point cloud
analysis tasks due to their excellent global modeling ability. However, the
attention mechanism has quadratic complexity and is difficult to extend to long
sequence modeling due to limited computational resources and so on. Recently,
state space models (SSM), a new family of deep sequence models, have presented
great potential for sequence modeling in NLP tasks. In this paper, taking
inspiration from the success of SSM in NLP, we propose PointMamba, a framework
with global modeling and linear complexity. Specifically, by taking embedded
point patches as input, we proposed a reordering strategy to enhance SSM's
global modeling ability by providing a more logical geometric scanning order.
The reordered point tokens are then sent to a series of Mamba blocks to
causally capture the point cloud structure. Experimental results show our
proposed PointMamba outperforms the transformer-based counterparts on different
point cloud analysis datasets, while significantly saving about 44.3%
parameters and 25% FLOPs, demonstrating the potential option for constructing
foundational 3D vision models. We hope our PointMamba can provide a new
perspective for point cloud analysis. The code is available at
this https URL

基于状态空间模型 (SSM) 成功在 NLP 任务中进行序列建模的启发，我们提出了具有全局建模和线性复杂度的 PointMamba 框架，通过重新排序策略提供更合理的几何扫描顺序来增强 SSM 的全局建模能力，实验证明我们的 PointMamba 在不同的点云分析数据集上胜过基于 transformer 的对应模型，同时节省了约 44.3% 的参数和 25% 的 FLOPs，展示了构建基础 3D 视觉模型的潜在选择，并为点云分析提供了新的视角。

PointMamba：一种用于点云分析的简单状态空间模型

PointMamba: A Simple State Space Model for Point Cloud Analysis

Internet traffic in the real world is susceptible to various external and
internal factors which may abruptly change the normal traffic flow. Those
unexpected changes are considered outliers in traffic. However, deep sequence
models have been used to predict complex IP traffic, but their comparative
performance for anomalous traffic has not been studied extensively. In this
paper, we investigated and evaluated the performance of different deep sequence
models for anomalous traffic prediction. Several deep sequences models were
implemented to predict real traffic without and with outliers and show the
significance of outlier detection in real-world traffic prediction. First, two
different outlier detection techniques, such as the Three-Sigma rule and
Isolation Forest, were applied to identify the anomaly. Second, we adjusted
those abnormal data points using the Backward Filling technique before training
the model. Finally, the performance of different models was compared for
abnormal and adjusted traffic. LSTM_Encoder_Decoder (LSTM_En_De) is the best
prediction model in our experiment, reducing the deviation between actual and
predicted traffic by more than 11\% after adjusting the outliers. All other
models, including Recurrent Neural Network (RNN), Long Short-Term Memory
(LSTM), LSTM_En_De with Attention layer (LSTM_En_De_Atn), Gated Recurrent Unit
(GRU), show better prediction after replacing the outliers and decreasing
prediction error by more than 29%, 24%, 19%, and 10% respectively. Our
experimental results indicate that the outliers in the data can significantly
impact the quality of the prediction. Thus, outlier detection and mitigation
assist the deep sequence model in learning the general trend and making better
predictions.

本文介绍了在预测实际交通时检测和纠正异常值对深度序列模型的影响，探究了多个模型在异常数据预测方面的表现，实验结果表明 LSTM-En-De（长短时记忆编码器 - 解码器）是最佳的预测模型，并能通过清除异常值降低预测误差。