Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit
spikes, a typical biomarker of the pathology. Detecting those spikes allows
accurate localization of brain regions triggering seizures. Spike detection is
often performed manually. However, it is a burdensome and error prone task due
to the complexity of MEG data. To address this problem, we propose a 1D
temporal convolutional neural network (Time CNN) coupled with a graph
convolutional network (GCN) to classify short time frames of MEG recording as
containing a spike or not. Compared to other recent approaches, our models have
fewer parameters to train and we propose to use a GCN to account for MEG
sensors spatial relationships. Our models produce clinically relevant results
and outperform deep learning-based state-of-the-art methods reaching a
classification f1-score of 76.7% on a balanced dataset and of 25.5% on a
realistic, highly imbalanced dataset, for the spike class.

利用具有更少参数的 1D 时间卷积神经网络 (Time CNN) 结合图卷积网络 (GCN) 来准确识别脑电信号中的尖峰，并通过脑电信号的短时间帧进行定位来取代手动检测。我们的模型在平衡数据集上达到了 76.7％的分类 f1 分数，在现实中高度不平衡的数据集上达到了 25.5％的分数。

时间 CNN 和图卷积网络用于 MEG 数据中的癫痫尖峰检测

Time CNN and Graph Convolution Network for Epileptic Spike Detection in  MEG Data

Temporal action proposal generation is an important task, akin to object
proposals, temporal action proposals are intended to capture "clips" or
temporal intervals in videos that are likely to contain an action. Previous
methods can be divided to two groups: sliding window ranking and actionness
score grouping. Sliding windows uniformly cover all segments in videos, but the
temporal boundaries are imprecise; grouping based method may have more precise
boundaries but it may omit some proposals when the quality of actionness score
is low. Based on the complementary characteristics of these two methods, we
propose a novel Complementary Temporal Action Proposal (CTAP) generator.
Specifically, we apply a Proposal-level Actionness Trustworthiness Estimator
(PATE) on the sliding windows proposals to generate the probabilities
indicating whether the actions can be correctly detected by actionness scores,
the windows with high scores are collected. The collected sliding windows and
actionness proposals are then processed by a temporal convolutional neural
network for proposal ranking and boundary adjustment. CTAP outperforms
state-of-the-art methods on average recall (AR) by a large margin on THUMOS-14
and ActivityNet 1.3 datasets. We further apply CTAP as a proposal generation
method in an existing action detector, and show consistent significant
improvements.

这篇论文提出了一种称为 CTAP 的新型时间行动提案生成器，该生成器将滑动窗口和行动得分分组结合起来进行提案生成，并使用时间卷积神经网络进行提案排名和边界调整，实验结果显示其在 THUMOS-14 和 ActivityNet 1.3 数据集上的平均召回率（AR）远远优于现有技术，并在现有行动检测器中的应用展现出显著的改进。

CTAP: 补充时间行为建议生成

CTAP: Complementary Temporal Action Proposal Generation

We report an implementation of a clinical information extraction tool that
leverages deep neural network to annotate event spans and their attributes from
raw clinical notes and pathology reports. Our approach uses context words and
their part-of-speech tags and shape information as features. Then we hire
temporal (1D) convolutional neural network to learn hidden feature
representations. Finally, we use Multilayer Perceptron (MLP) to predict event
spans. The empirical evaluation demonstrates that our approach significantly
outperforms baselines.

本研究利用深度神经网络实现一个临床信息提取工具，该工具可以从原始的临床笔记和病理报告中标注事件跨度及其属性，并通过使用时间卷积神经网络和多层感知器来提高性能。