最近的研究利用稀疏的分类来预测高维大脑活动信号的分类变量，以暴露人类的意图和精神状态，从而自动选择模型训练过程中的相关特征。但是，现有的稀疏分类模型可能会容易出现由大脑记录固有的噪声引起的性能降解。为了解决这个问题，我们旨在在本研究中提出一种新的健壮和稀疏分类算法。为此，我们将CorrentRopy学习框架引入基于自动相关性的稀疏分类模型，并提出了一种新的基于Correntropy的鲁棒稀疏逻辑回归算法。为了证明所提出算法的上等大脑活性解码性能，我们在合成数据集，脑电图（EEG）数据集和功能磁共振成像（FMRI）数据集上对其进行了评估。广泛的实验结果证实，不仅提出的方法可以在嘈杂和高维分类任务中实现更高的分类精度，而且还将为解码方案选择那些更有信息的功能。将Correntropy学习方法与自动相关性测定技术相结合，将显着提高噪声的鲁棒性，从而导致更足够的稳健稀疏脑解码算法。它在现实世界中的大脑活动解码和脑部计算机界面中提供了一种更强大的方法。

Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. However, most of them deform or generate the whole facial area, leading to non-realistic results. In this work, we delve into the formulation of altering only the mouth shapes of the target person. This requires masking a large percentage of the original image and seamlessly inpainting it with the aid of audio and reference frames. To this end, we propose the Audio-Visual Context-Aware Transformer (AV-CAT) framework, which produces accurate lip-sync with photo-realistic quality by predicting the masked mouth shapes. Our key insight is to exploit desired contextual information provided in audio and visual modalities thoroughly with delicately designed Transformers. Specifically, we propose a convolution-Transformer hybrid backbone and design an attention-based fusion strategy for filling the masked parts. It uniformly attends to the textural information on the unmasked regions and the reference frame. Then the semantic audio information is involved in enhancing the self-attention computation. Additionally, a refinement network with audio injection improves both image and lip-sync quality. Extensive experiments validate that our model can generate high-fidelity lip-synced results for arbitrary subjects.

Mutation-based fuzzing has become one of the most common vulnerability discovery solutions over the last decade. Fuzzing can be optimized when targeting specific programs, and given that, some studies have employed online optimization methods to do it automatically, i.e., tuning fuzzers for any given program in a program-agnostic manner. However, previous studies have neither fully explored mutation schemes suitable for online optimization methods, nor online optimization methods suitable for mutation schemes. In this study, we propose an optimization framework called SLOPT that encompasses both a bandit-friendly mutation scheme and mutation-scheme-friendly bandit algorithms. The advantage of SLOPT is that it can generally be incorporated into existing fuzzers, such as AFL and Honggfuzz. As a proof of concept, we implemented SLOPT-AFL++ by integrating SLOPT into AFL++ and showed that the program-agnostic optimization delivered by SLOPT enabled SLOPT-AFL++ to achieve higher code coverage than AFL++ in all of ten real-world FuzzBench programs. Moreover, we ran SLOPT-AFL++ against several real-world programs from OSS-Fuzz and successfully identified three previously unknown vulnerabilities, even though these programs have been fuzzed by AFL++ for a considerable number of CPU days on OSS-Fuzz.

在本文中，我们开发了一种使用深神经网络（DNNS）的非组织和非线性时间序列的自适应非参数估计的一般理论。我们首先考虑两种类型的DNN估计量，非含糖和稀疏的DNN估计器，并为一般非平稳时间序列建立其泛化误差界限。然后，我们得出最小值下限，以估计属于一类非线性自回旋（AR）模型的平均功能，这些功能包括非线性通用添加剂AR，单个索引和阈值AR模型。在结果的基础上，我们表明稀疏的DNN估计量具有自适应性，并达到了许多非线性AR模型的最小最佳速率，直至多构型因子。通过数值模拟，我们证明了DNN方法在估计具有内在的低维结构和不连续或粗糙平均功能的非线性AR模型的有用性，这与我们的理论一致。