Self-evolution is indispensable to realize full autonomous driving. This
paper presents a self-evolving decision-making system based on the Integrated
Decision and Control (IDC), an advanced framework built on reinforcement
learning (RL). First, an RL algorithm called constrained mixed policy gradient
(CMPG) is proposed to consistently upgrade the driving policy of the IDC. It
adapts the MPG under the penalty method so that it can solve constrained
optimization problems using both the data and model. Second, an attention-based
encoding (ABE) method is designed to tackle the state representation issue. It
introduces an embedding network for feature extraction and a weighting network
for feature fusion, fulfilling order-insensitive encoding and importance
distinguishing of road users. Finally, by fusing CMPG and ABE, we develop the
first data-driven decision and control system under the IDC architecture, and
deploy the system on a fully-functional self-driving vehicle running in daily
operation. Experiment results show that boosting by data, the system can
achieve better driving ability over model-based methods. It also demonstrates
safe, efficient and smart driving behavior in various complex scenes at a
signalized intersection with real mixed traffic flow.

本文提出了一种基于 RL 算法的自我进化决策系统，包含 CMPG 和 ABE 技术，该系统被部署在一辆自动驾驶汽车上，实现了智能、高效、安全的驾驶行为。实验表明，该系统可显著提升驾驶能力和模型方法相比具有更好的驾驶环境适应性。

混合策略梯度及其实验验证下的高级自动驾驶一体化决策与控制

Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification

Although stance detection has made great progress in the past few years, it
is still facing the problem of unseen targets. In this study, we investigate
the domain difference between targets and thus incorporate attention-based
conditional encoding with adversarial domain generalization to perform unseen
target stance detection. Experimental results show that our approach achieves
new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the
importance of domain difference between targets in unseen target stance
detection.

本研究探讨了目标领域差异对于对未见目标进行立场检测的影响，并采用基于注意力机制的条件编码以及对抗领域泛化技术进行研究。实验结果表明，我们的方法在 SemEval-2016 数据集上取得了新的最先进性能，从而证明了目标领域差异在未见目标立场检测中的重要性。

使用对抗域泛化实现未见目标立场检测

Unseen Target Stance Detection with Adversarial Domain Generalization

We present a novel approach for the prediction of anticancer compound
sensitivity by means of multi-modal attention-based neural networks (PaccMann).
In our approach, we integrate three key pillars of drug sensitivity, namely,
the molecular structure of compounds, transcriptomic profiles of cancer cells
as well as prior knowledge about interactions among proteins within cells. Our
models ingest a drug-cell pair consisting of SMILES encoding of a compound and
the gene expression profile of a cancer cell and predicts an IC50 sensitivity
value. Gene expression profiles are encoded using an attention-based encoding
mechanism that assigns high weights to the most informative genes. We present
and study three encoders for SMILES string of compounds: 1) bidirectional
recurrent 2) convolutional 3) attention-based encoders. We compare our devised
models against a baseline model that ingests engineered fingerprints to
represent the molecular structure. We demonstrate that using our
attention-based encoders, we can surpass the baseline model. The use of
attention-based encoders enhance interpretability and enable us to identify
genes, bonds and atoms that were used by the network to make a prediction.

本文提出了一种新颖的基于多模态注意力神经网络（PaccMann）预测抗癌化合物敏感性的方法，集成了化合物的分子结构、癌细胞的转录组文件以及关于细胞内蛋白质相互作用的先前知识，通过用 SMILES 编码的化合物和癌细胞的基因表达谱预测 IC50 敏感性值，其中基因表达谱采用注意力编码机制编码，通过比较我们设计的模型和基准模型，证明了使用我们的注意力编码器可以超越基准模型，增强了可解释性并使我们能够识别网络用于进行预测的基因、键和原子。