Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

This work proposes a framework developed to generalize Critical Heat Flux (CHF) detection classification models using an Unsupervised Image-to-Image (UI2I) translation model. The framework enables a typical classification model that was trained and tested on boiling images from domain A to predict boiling images coming from domain B that was never seen by the classification model. This is done by using the UI2I model to transform the domain B images to look like domain A images that the classification model is familiar with. Although CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used as the UI2I model, the framework is model agnostic. Meaning, that the framework can generalize any image classification model type, making it applicable to a variety of similar applications and not limited to the boiling crisis detection problem. It also means that the more the UI2I models advance, the better the performance of the framework.

Oxidation states are the charges of atoms after their ionic approximation of their bonds, which have been widely used in charge-neutrality verification, crystal structure determination, and reaction estimation. Currently only heuristic rules exist for guessing the oxidation states of a given compound with many exceptions. Recent work has developed machine learning models based on heuristic structural features for predicting the oxidation states of metal ions. However, composition based oxidation state prediction still remains elusive so far, which is more important in new material discovery for which the structures are not even available. This work proposes a novel deep learning based BERT transformer language model BERTOS for predicting the oxidation states of all elements of inorganic compounds given only their chemical composition. Our model achieves 96.82\% accuracy for all-element oxidation states prediction benchmarked on the cleaned ICSD dataset and achieves 97.61\% accuracy for oxide materials. We also demonstrate how it can be used to conduct large-scale screening of hypothetical material compositions for materials discovery.

Learning effective joint embedding for cross-modal data has always been a focus in the field of multimodal machine learning. We argue that during multimodal fusion, the generated multimodal embedding may be redundant, and the discriminative unimodal information may be ignored, which often interferes with accurate prediction and leads to a higher risk of overfitting. Moreover, unimodal representations also contain noisy information that negatively influences the learning of cross-modal dynamics. To this end, we introduce the multimodal information bottleneck (MIB), aiming to learn a powerful and sufficient multimodal representation that is free of redundancy and to filter out noisy information in unimodal representations. Specifically, inheriting from the general information bottleneck (IB), MIB aims to learn the minimal sufficient representation for a given task by maximizing the mutual information between the representation and the target and simultaneously constraining the mutual information between the representation and the input data. Different from general IB, our MIB regularizes both the multimodal and unimodal representations, which is a comprehensive and flexible framework that is compatible with any fusion methods. We develop three MIB variants, namely, early-fusion MIB, late-fusion MIB, and complete MIB, to focus on different perspectives of information constraints. Experimental results suggest that the proposed method reaches state-of-the-art performance on the tasks of multimodal sentiment analysis and multimodal emotion recognition across three widely used datasets. The codes are available at \url{https://github.com/TmacMai/Multimodal-Information-Bottleneck}.

基于DNN的视频对象检测（VOD）为自动驾驶和视频监视行业提供了重要的重要性和有希望的机会。但是，由于其实用性，可行性和强大的攻击效果，对抗贴片攻击在现场视觉任务中产生了巨大的关注。这项工作提出了Themis，这是一种软件/硬件系统，可防止对抗贴片，以实时稳健的视频对象检测。我们观察到，对抗斑块在具有非稳定预测的小区域中表现出极为局部的表面特征，因此提出了对抗区域检测算法，以消除对抗性效应。Themis还提出了一种系统的设计，以通过消除冗余计算和记忆运输来有效地支持该算法。实验结果表明，提出的方法可以有效地从可忽略的硬件开销中从对抗性攻击中恢复系统。

深度卷积神经网络（DCNNS）在面部识别方面已经达到了人类水平的准确性（Phillips等，2018），尽管目前尚不清楚它们如何准确地区分高度相似的面孔。在这里，人类和DCNN执行了包括相同双胞胎在内的具有挑战性的面貌匹配任务。参与者（n = 87）查看了三种类型的面孔图像：同一身份，普通冒名顶替对（来自相似人口组的不同身份）和双胞胎冒名顶替对（相同的双胞胎兄弟姐妹）。任务是确定对是同一个人还是不同的人。身份比较在三个观点区分条件下进行了测试：额叶至额叶，额叶至45度，额叶为90度。在每个观点 - 差异条件下评估了从双胞胎突变器和一般冒险者区分匹配的身份对的准确性。人类对于一般撞击对比双重射手对更准确，准确性下降，一对图像之间的观点差异增加。通过介绍给人类的同一图像对测试了经过训练的面部识别的DCNN（Ranjan等，2018）。机器性能反映了人类准确性的模式，但除了一种条件以外，所有人的性能都处于或尤其是所有人的表现。在所有图像对类型中，比较了人与机器的相似性得分。该项目级别的分析表明，在九种图像对类型中的六种中，人类和机器的相似性等级显着相关[范围r = 0.38至r = 0.63]，这表明人类对面部相似性的感知和DCNN之间的一般协议。这些发现还有助于我们理解DCNN的表现，以区分高度介绍面孔，表明DCNN在人类或以上的水平上表现出色，并暗示了人类和DCNN使用的特征之间的均等程度。

语音情绪识别（SER）是人类计算机互动的重要组成部分。在本文中，我们提出了一个基于图形同构网络的SER网络，具有加权多个聚合器（WMA-GIN），当邻居节点的特征在杜松子酒结构中聚集在一起时，该网络可以有效地解决信息困惑的问题。此外，采用了一个全粘的（FA）层来减轻过度方面的问题，该问题在包括杜松子酒在内的所有图神经网络（GNN）结构中都存在。此外，采用多相注意机制和多损失训练策略来避免缺少堆叠的WMA轴层中有用的情感信息。我们评估了我们在流行的Iemocap数据集中提出的WMA-GIN的性能。实验结果表明，WMA-GIN的表现优于其他基于GNN的方法，并且可以通过达到72.48％的加权准确性（WA）和67.72％的未加权准确性（UA）来与某些基于高级的基于非冲突的方法相媲美。

自动放射学报告生成对于计算机辅助诊断至关重要。通过图像字幕的成功，可以实现医疗报告的生成。但是，缺乏注释的疾病标签仍然是该地区的瓶颈。此外，图像文本数据偏差问题和复杂的句子使生成准确的报告变得更加困难。为了解决这些差距，我们预定了一个自我引导的框架（SGF），这是一套无监督和监督的深度学习方法，以模仿人类的学习和写作过程。详细说明，我们的框架从具有额外的疾病标签的医学报告中获得了域知识，并指导自己提取与文本相关的罚款谷物视觉特征。此外，SGF通过纳入相似性比较机制，成功地提高了医疗报告生成的准确性和长度，该机制通过比较实践模仿了人类自我完善的过程。广泛的实验证明了我们在大多数情况下我们的SGF的实用性，表明其优于最先进的甲基动物。我们的结果突出了提议的框架的能力，以区分单词之间有罚的粒度视觉细节并验证其在生成医疗报告中的优势。

图像BERT使用掩盖图像建模（MIM）预训练成为应对自我监督的表示学习的一种流行实践。一项开创性的作品将MIM作为一个视觉词汇作为分类任务，将连续的视觉信号用于离散的视觉令牌，并使用预先学习的DVAE将其标记为离散的视觉令牌。尽管有可行的解决方案，但不当离散化仍阻碍了图像预训练的进一步改善。由于图像离散化没有基本真相的答案，因此我们认为，即使可以获得更好的``令牌'''，也不应使用唯一的令牌ID分配蒙面的补丁。在这项工作中，我们引入了改进的BERT风格图像预训练方法，即MC-BEIT，该方法执行MIM代理任务，以放松和精致的多选择培训目标。 Specifically, the multi-choice supervision for the masked image patches is formed by the soft probability vectors of the discrete token ids, which are predicted by the off-the-shelf image ``tokenizer'' and further refined by high-level inter-补丁感知诉诸于观察到类似的补丁应该分享其选择。关于分类，分割和检测任务的广泛实验证明了我们方法的优势，例如，预先培训的VIT-B在Imagenet-1K分类上达到了84.1％的TOP-1微调精度，49.2％AP^B和44.0％对象检测和可可的实例分割的AP^m，在ADE20K语义分段上为50.8％，表现优于竞争性对应物。该代码将在https://github.com/lixiaotong97/mc-beit上找到。

随机梯度下降（SGDA）及其变体一直是解决最小值问题的主力。但是，与研究有差异隐私（DP）约束的经过良好研究的随机梯度下降（SGD）相反，在理解具有DP约束的SGDA的概括（实用程序）方面几乎没有工作。在本文中，我们使用算法稳定性方法在不同的设置中建立DP-SGDA的概括（实用程序）。特别是，对于凸 - 凸环设置，我们证明DP-SGDA可以在平滑和非平滑案例中都可以根据弱原始二元人群风险获得最佳的效用率。据我们所知，这是在非平滑案例中DP-SGDA的第一个已知结果。我们进一步在非convex-rong-concave环境中提供了实用性分析，这是原始人口风险的首个已知结果。即使在非私有设置中，此非convex设置的收敛和概括结果也是新的。最后，进行了数值实验，以证明DP-SGDA在凸和非凸病例中的有效性。