With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.

The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

大多数现有的插槽填充模型倾向于记住实体的固有模式和培训数据中相应的上下文。但是，这些模型在暴露于口语语言扰动或实践中的变化时会导致系统故障或不良输出。我们提出了一种扰动的语义结构意识转移方法，用于训练扰动插槽填充模型。具体而言，我们介绍了两种基于传销的培训策略，以分别从无监督的语言扰动语料库中分别学习上下文语义结构和单词分布。然后，我们将从上游训练过程学到的语义知识转移到原始样本中，并通过一致性处理过滤生成的数据。这些程序旨在增强老虎机填充模型的鲁棒性。实验结果表明，我们的方法始终优于先前的基本方法，并获得强有力的概括，同时阻止模型记住实体和环境的固有模式。

降级扩散概率模型（DDPM）最近在许多生成任务中都取得了领先的性能。但是，继承的迭代采样过程成本阻碍了他们的应用程序到文本到语音部署。通过有关扩散模型参数化的初步研究，我们发现以前基于梯度的TTS模型需要数百或数千个迭代以保证高样本质量，这对加速采样带来了挑战。在这项工作中，我们提出了Prodiff的建议，以用于高质量文本到语音的渐进快速扩散模型。与以前的估计数据密度梯度的工作不同，Prodiff通过直接预测清洁数据来避免在加速采样时避免明显的质量降解来参数化denoising模型。为了通过减少扩散迭代来应对模型收敛挑战，Prodiff通过知识蒸馏减少目标位点的数据差异。具体而言，Denoising模型使用N-Step DDIM教师的生成的MEL光谱图作为训练目标，并将行为提炼成具有N/2步的新模型。因此，它允许TTS模型做出尖锐的预测，并通过数量级进一步减少采样时间。我们的评估表明，Prodiff仅需要两次迭代即可合成高保真性MEL光谱图，同时使用数百个步骤保持样本质量和多样性与最先进的模型竞争。 Prodiff在单个NVIDIA 2080TI GPU上的采样速度比实时快24倍，这使得扩散模型实际上是第一次适用于文本到语音综合部署。我们广泛的消融研究表明，Prodiff中的每种设计都是有效的，我们进一步表明，Prodiff可以轻松扩展到多扬声器设置。音频样本可在\ url {https://prodiff.github.io/。}上找到

阿尔茨海默氏病（AD）的早期诊断对于促进预防性护理以延迟进一步发展至关重要。本文介绍了建立在痴呆症Pitt copus上的基于最新的构象识别系统以自动检测的开发。通过纳入一组有目的设计的建模功能，包括基于域搜索的自动配置特异性构象异构体超参数除外，还包括基于速度扰动和基于规格的数据增强训练的基线构象体系统可显着改善。使用学习隐藏单位贡献（LHUC）的细粒度老年人的适应性；以及与混合TDNN系统的基于两次通行的跨系统逆转。在48位老年人的评估数据上获得了总体单词错误率（相对34.8％）的总体单词错误率（相对34.8％）。使用最终系统的识别输出来提取文本特征，获得了最佳的基于语音识别的AD检测精度为91.7％。

作为一种主动网络安全保护方案，入侵检测系统（IDS）承担以恶意网络流量形式检测网络攻击的重要责任。入侵检测技术是ID的重要组成部分。目前，许多学者已经对入侵检测技术进行了广泛的研究。但是，为大规模网络流量数据开发有效的入侵检测方法仍然很困难。由于生成的对抗网络（GAN）具有强大的建模功能，可用于复杂的高维数据，因此它们为解决此问题提供了新的想法。在本文中，我们提出了一种基于Ebgan的入侵检测方法IDS-Ebgan，该方法将网络记录归类为正常流量或恶意流量。 IDS-Ebgan中的发电机负责将培训中的原始恶意网络流量转换为对抗性恶意示例。这是因为我们想使用对抗性学习来提高歧视者检测恶意流量的能力。同时，鉴别器采用自动编码器模型。在测试过程中，IDS-Ebgan使用歧视器的重建错误来对流量记录进行分类。

大脑网络将大脑区域之间的复杂连接性描述为图形结构，这为研究脑连接素提供了强大的手段。近年来，图形神经网络已成为使用结构化数据的普遍学习范式。但是，由于数据获取的成本相对较高，大多数大脑网络数据集的样本量受到限制，这阻碍了足够的培训中的深度学习模型。受元学习的启发，该论文以有限的培训示例快速学习新概念，研究了在跨数据库中分析脑连接组的数据有效培训策略。具体而言，我们建议在大型样本大小的数据集上进行元训练模型，并将知识转移到小数据集中。此外，我们还探索了两种面向脑网络的设计，包括Atlas转换和自适应任务重新启动。与其他训练前策略相比，我们的基于元学习的方法实现了更高和稳定的性能，这证明了我们提出的解决方案的有效性。该框架还能够以数据驱动的方式获得有关数据集和疾病之间相似之处的新见解。

To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and have been successfully leveraged in many biomedical applications including drug repurposing. Our objective is to construct a knowledge graph from literature to study relations between Alzheimer's disease (AD) and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression. We collected biomedical annotations and extracted their relations using SemRep via SemMedDB. We used both a BERT-based classifier and rule-based methods during data preprocessing to exclude noise while preserving most AD-related semantic triples. The 1,672,110 filtered triples were used to train with knowledge graph completion algorithms (i.e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention. Among three knowledge graph completion models, TransE outperformed the other two (MR = 13.45, Hits@1 = 0.306). We leveraged the time-slicing technique to further evaluate the prediction results. We found supporting evidence for most highly ranked candidates predicted by our model which indicates that our approach can inform reliable new knowledge. This paper shows that our graph mining model can predict reliable new relationships between AD and other entities (i.e., dietary supplements, chemicals, and drugs). The knowledge graph constructed can facilitate data-driven knowledge discoveries and the generation of novel hypotheses.

联合学习是一个分布式机器学习机制，本地设备在中央服务器的编排中协作培训共享全局模型，同时保留所有私有数据分散。在系统中，传输模型参数及其更新而不是原始数据，因此通信瓶颈已成为一个关键挑战。此外，近期的较大和更深层次的机器学习模型也在将它们部署到联邦环境中的困难造成更多困难。在本文中，我们设计了一个联合的两阶段学习框架，即在设备上使用切割层增强了原型联合学习，并使用基于符号的随机梯度下降与大多数投票方法进行模型更新。剪切图层在设备上学习本地原始数据的信息和低维表示，有助于减少全局模型参数并防止数据泄漏。基于符号的SGD与大多数投票方式进行模型更新，也有助于缓解通信限制。凭经验，我们表明我们的系统是一种有效和隐私，保留联合学习计划和适用于一般应用方案的诉讼。

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.