In our study, we apply different statistical models to nowcast quarterly GDP
growth for the US economy. Using the monthly FRED-MD database, we compare the
nowcasting performance of the dynamic factor model (DFM) and four artificial
neural networks (ANNs): the multilayer perceptron (MLP), the one-dimensional
convolutional neural network (1D CNN), the long short-term memory network
(LSTM), and the gated recurrent unit (GRU). The empirical analysis presents the
results from two distinctively different evaluation periods. The first (2010:Q1
-- 2019:Q4) is characterized by balanced economic growth, while the second
(2010:Q1 -- 2022:Q3) also includes periods of the COVID-19 recession. According
to our results, longer input sequences result in more accurate nowcasts in
periods of balanced economic growth. However, this effect ceases above a
relatively low threshold value of around six quarters (eighteen months). During
periods of economic turbulence (e.g., during the COVID-19 recession), longer
training sequences do not help the models' predictive performance; instead,
they seem to weaken their generalization capability. Our results show that 1D
CNN, with the same parameters, generates accurate nowcasts in both of our
evaluation periods. Consequently, first in the literature, we propose the use
of this specific neural network architecture for economic nowcasting.

通过比较动态因子模型（DFM）和四种人工神经网络（ANNs）的预测表现，本文应用不同的统计模型来实时预测美国经济的季度 GDP 增长，并使用 FRED-MD 数据库进行实证研究。结果表明，虽然在经济增长平衡期间，较长的输入序列会产生更准确的预测，但是在经济动荡期间（如 COVID-19 衰退期间），较长的训练序列并不能提高模型的预测性能；在这种情况下，1D CNN 则具有较高的准确率，并被提出作为一种经济实时预测的有效方法。

采用人工神经网络进行 GDP 现值预测：长期记忆的重要性如何？

GDP nowcasting with artificial neural networks: How much does long-term  memory matter?

Modern indoor localization techniques are essential to overcome the weak GPS
coverage in indoor environments. Recently, considerable progress has been made
in Channel State Information (CSI) based indoor localization with signal
fingerprints. However, CSI signal patterns can be complicated in the large and
highly dynamic indoor spaces with complex interiors, thus a solution for
solving this issue is urgently needed to expand the applications of CSI to a
broader indoor space. In this paper, we propose an end-to-end solution
including data collection, pattern clustering, denoising, calibration and a
lightweight one-dimensional convolutional neural network (1D CNN) model with
CSI fingerprinting to tackle this problem. We have also created and plan to
open source a CSI dataset with a large amount of data collected across complex
indoor environments at Colorado State University. Experiments indicate that our
approach achieves up to 68.5% improved performance (mean distance error) with
minimal number of parameters, compared to the best-known deep machine learning
and CSI-based indoor localization works.

本文提出了一个包括数据收集，模式聚类，降噪，校准和轻量级一维卷积神经网络模型的端到端解决方案，并使用信号指纹技术解决复杂室内环境下通道状态信息的检测问题，实验表明该方法性能达到最佳神经网络和信道状态信息室内定位作品的 68.5％，参数极少。

一维卷积神经网络 CSI 室内定位框架

A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks

A new collaborative learning, called split learning, was recently introduced,
aiming to protect user data privacy without revealing raw input data to a
server. It collaboratively runs a deep neural network model where the model is
split into two parts, one for the client and the other for the server.
Therefore, the server has no direct access to raw data processed at the client.
Until now, the split learning is believed to be a promising approach to protect
the client's raw data; for example, the client's data was protected in
healthcare image applications using 2D convolutional neural network (CNN)
models. However, it is still unclear whether the split learning can be applied
to other deep learning models, in particular, 1D CNN.
In this paper, we examine whether split learning can be used to perform
privacy-preserving training for 1D CNN models. To answer this, we first design
and implement an 1D CNN model under split learning and validate its efficacy in
detecting heart abnormalities using medical ECG data. We observed that the 1D
CNN model under split learning can achieve the same accuracy of 98.9\% like the
original (non-split) model. However, our evaluation demonstrates that split
learning may fail to protect the raw data privacy on 1D CNN models. To address
the observed privacy leakage in split learning, we adopt two privacy leakage
mitigation techniques: 1) adding more hidden layers to the client side and 2)
applying differential privacy. Although those mitigation techniques are helpful
in reducing privacy leakage, they have a significant impact on model accuracy.
Hence, based on those results, we conclude that split learning alone would not
be sufficient to maintain the confidentiality of raw sequential data in 1D CNN
models.

本文研究了在 1D CNN 模型下使用分裂学习进行隐私保护的技术，发现分裂学习技术对保护数据隐私存在一定的泄露，提供了两种隐私泄露缓解技术：客户端增加隐藏层和应用差分隐私，但这些技术都会对模型准确性产生重要影响，因此，我们得出一个结论，分裂学习单独使用不能保持 1D CNN 模型下的原始序列数据的机密性。