Peptides offer great biomedical potential and serve as promising drug
candidates. Currently, the majority of approved peptide drugs are directly
derived from well-explored natural human peptides. It is quite necessary to
utilize advanced deep learning techniques to identify novel peptide drugs in
the vast, unexplored biochemical space. Despite various in silico methods
having been developed to accelerate peptide early drug discovery, existing
models face challenges of overfitting and lacking generalizability due to the
limited size, imbalanced distribution and inconsistent quality of experimental
data. In this study, we propose PepGB, a deep learning framework to facilitate
peptide early drug discovery by predicting peptide-protein interactions
(PepPIs). Employing graph neural networks, PepGB incorporates a fine-grained
perturbation module and a dual-view objective with contrastive learning-based
peptide pre-trained representation to predict PepPIs. Through rigorous
evaluations, we demonstrated that PepGB greatly outperforms baselines and can
accurately identify PepPIs for novel targets and peptide hits, thereby
contributing to the target identification and hit discovery processes. Next, we
derive an extended version, diPepGB, to tackle the bottleneck of modeling
highly imbalanced data prevalent in lead generation and optimization processes.
Utilizing directed edges to represent relative binding strength between two
peptide nodes, diPepGB achieves superior performance in real-world assays. In
summary, our proposed frameworks can serve as potent tools to facilitate
peptide early drug discovery.

利用深度学习技术，本文提出了 PepGB 和 diPepGB 两个深度学习框架，用于预测肽蛋白相互作用关系，以促进肽类早期药物发现，提高目标识别和化合物发现过程中的效率。

PepGB: 通过图神经网络促进肽类药物发现

PepGB: Facilitating peptide drug discovery via graph neural networks

Unsupervised video object segmentation (UVOS) aims at detecting the primary
objects in a given video sequence without any human interposing. Most existing
methods rely on two-stream architectures that separately encode the appearance
and motion information before fusing them to identify the target and generate
object masks. However, this pipeline is computationally expensive and can lead
to suboptimal performance due to the difficulty of fusing the two modalities
properly. In this paper, we propose a novel UVOS model called SimulFlow that
simultaneously performs feature extraction and target identification, enabling
efficient and effective unsupervised video object segmentation. Concretely, we
design a novel SimulFlow Attention mechanism to bridege the image and motion by
utilizing the flexibility of attention operation, where coarse masks predicted
from fused feature at each stage are used to constrain the attention operation
within the mask area and exclude the impact of noise. Because of the
bidirectional information flow between visual and optical flow features in
SimulFlow Attention, no extra hand-designed fusing module is required and we
only adopt a light decoder to obtain the final prediction. We evaluate our
method on several benchmark datasets and achieve state-of-the-art results. Our
proposed approach not only outperforms existing methods but also addresses the
computational complexity and fusion difficulties caused by two-stream
architectures. Our models achieve 87.4% J & F on DAVIS-16 with the highest
speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow
also obtains competitive results on video salient object detection datasets.

本文提出了一种名为 SimulFlow 的新型无监督视频目标分割模型，它同时进行特征提取和目标识别，实现高效和有效的无监督视频目标分割，通过利用灵活的注意力机制来融合图像和运动信息，减少了计算复杂性和融合难度，并在多个基准数据集上取得了领先的性能。

SimulFlow：无监督视频目标分割中同时提取特征与识别目标

SimulFlow: Simultaneously Extracting Feature and Identifying Target for  Unsupervised Video Object Segmentation

Phishing is a major problem on the Web. Despite the significant attention it
has received over the years, there has been no definitive solution. While the
state-of-the-art solutions have reasonably good performance, they require a
large amount of training data and are not adept at detecting phishing attacks
against new targets. In this paper, we begin with two core observations: (a)
although phishers try to make a phishing webpage look similar to its target,
they do not have unlimited freedom in structuring the phishing webpage; and (b)
a webpage can be characterized by a small set of key terms; how these key terms
are used in different parts of a webpage is different in the case of legitimate
and phishing webpages. Based on these observations, we develop a phishing
detection system with several notable properties: it is language-independent,
can be implemented entirely on client-side, has excellent classification
performance and is fast. In addition, we developed a target identification
component that can identify the target website that a phishing webpage is
attempting to mimic. The target detection component is faster than previously
reported systems and can help minimize false positives in our phishing
detection system.

本文提出了一种基于网页核心术语的网络钓鱼检测系统，该系统具有良好的分类性能和较快的检测速度，并发展了一个可快速识别钓鱼网页目标的组件。