Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Finally, we present extensive experiments and case studies to illustrate 24.7\% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.

We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combining diverse visual / textual features and Bidirectional Negation Learning (BNL) for addressing queries that contain negation cues. In particular, LAFF performs feature fusion at both early and late stages and at both text and video ends to exploit diverse (off-the-shelf) features. Compared to multi-head self attention, LAFF is much more compact yet more effective. Its attentional weights can also be used for selecting fewer features, with the retrieval performance mostly preserved. BNL trains a negation-aware video retrieval model by minimizing a bidirectionally constrained loss per triplet, where a triplet consists of a given training video, its original description and a partially negated description. For video feature extraction, we use pre-trained CLIP, BLIP, BEiT, ResNeXt-101 and irCSN. As for text features, we adopt bag-of-words, word2vec, CLIP and BLIP. Our training data consists of MSR-VTT, TGIF and VATEX that were used in our previous participation. In addition, we automatically caption the V3C1 collection for pre-training. The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team. Our best run, with an infAP of 0.262, is ranked at the second place teamwise.

Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples. Though visual imperceptibility is the desired property of adversarial examples, conventional adversarial attacks still generate traceable adversarial perturbations. In this paper, we introduce a novel Adversarial Attack via Invertible Neural Networks (AdvINN) method to produce robust and imperceptible adversarial examples. Specifically, AdvINN fully takes advantage of the information preservation property of Invertible Neural Networks and thereby generates adversarial examples by simultaneously adding class-specific semantic information of the target class and dropping discriminant information of the original class. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-1K demonstrate that the proposed AdvINN method can produce less imperceptible adversarial images than the state-of-the-art methods and AdvINN yields more robust adversarial examples with high confidence compared to other adversarial attacks.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

图对比度学习（GCL）一直是图形自学学习的新兴解决方案。 GCL的核心原理是在正视图中降低样品之间的距离，但在负视图中增加样品之间的距离。在实现有希望的性能的同时，当前的GCL方法仍然受到两个局限性：（1）增强的不可控制的有效性，该图扰动可能会产生针对语义和图形数据的特征流程的无效视图； （2）不可靠的二进制对比理由，对于非欧几里得图数据而言，难以确定构造观点的积极性和负面性。为了应对上述局限性，我们提出了一个新的对比度学习范式，即图形软对比度学习（GSCL），该范例通过排名的社区无需任何增强和二进制对比符合性，在较细性的范围内进行对比度学习。 GSCL建立在图接近的基本假设上，即连接的邻居比遥远的节点更相似。具体而言，我们在配对和列表的封闭式排名中，以保留附近的相对排名关系。此外，随着邻里规模的指数增长，考虑了更多的啤酒花，我们提出了提高学习效率的邻里抽样策略。广泛的实验结果表明，我们提出的GSCL可以始终如一地在各种公共数据集上实现与GCL相当复杂的各种公共数据集的最新性能。

The peer merit review of research proposals has been the major mechanism for deciding grant awards. However, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign interdisciplinary proposals to appropriate reviewers, so proposals are fairly evaluated. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposal-reviewer matching. Existing systems mainly collect topic labels manually generated by principal investigators. However, such human-reported labels can be non-accurate, incomplete, labor intensive, and time costly. What role can AI play in developing a fair and precise proposal reviewer assignment system? In this study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). Specifically, we first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs for learning representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

有丝分裂细胞的描述是肿瘤诊断的关键特征。但是，由于有丝分裂细胞形态的变异性，检测肿瘤组织中有丝分裂细胞是一项高度挑战的任务。同时，尽管先进的深度学习方法在细胞检测方面取得了巨大成功，但从另一个域（即不同的肿瘤类型和不同的扫描仪）测试数据时，性能通常是不令人满意的。因此，有必要开发用于检测域中稳健性的有丝分裂细胞的算法。我们的工作进一步提出了基于基线（视网膜）的前景检测和肿瘤分类任务，并利用数据扩展来改善模型的域泛化性能。我们在具有挑战性的前测试数据集上实现了最先进的性能（F1分数：0.5809）。

近年来，Experts（MOE）的混合物已成为一种有前途的深度学习技术，可以将模型能力扩展为万亿多个参数，同时通过稀疏计算降低计算成本。虽然MoE开设了一个非常大的模型的新领域，但由于MOE的动态性质与系统的静态平行性/管道层之间的不匹配，因此其数以千计的GPU的实现受到限制。我们提出了Tutel，这是一种具有动态自适应并行性和管道的高度可扩展的堆栈设计和实现。 TUTEL在运行时提供自适应并行性切换和自适应管道，分别达到1.74倍和2.00倍的单MOE层加速度。我们还提出了一种用于MOE通信速度的新颖的二维层次结构算法，该算法的表现超过了2,048 GPU的先前最先前的最新时间。 Tutel汇总了所有技术，最终在16 GPU和2,048 GPU上分别提供了4.96倍和5.75倍的加速度，分别通过Fairseq：Meta的Facebook AI AI研究序列到序列工具Kit（Tutel（Tutel）（Tutel）（Tutel）（现在由Fairseq部分采用）。 Tutel源代码可在公共场所获得：https：//github.com/microsoft/tutel。我们的评估表明，Tutel有效，有效地运行了一个基于现实的MOE模型，名为Swinv2-Moe，建立在Swin Transformer V2上，这是一种最先进的计算机视觉体系结构。在效率方面，Tutel加速了Swinv2-MoE，在FairSeq的训练和推理中分别达到1.55倍和2.11倍的速度。关于有效性，SWINV2-MOE模型在预训练和下游计算机视觉任务（例如可可对象检测）方面都比对应的密度密度模型都达到了卓越的精度，这表明Tutel准备对端到端现实世界模型训练的准备就绪和推理。 Swinv2-Moe在https://github.com/microsoft/swin-transformer中开放。

否定是一种常见的语言技能，使人类能够表达我们不想要的东西。自然，人们可能会期望视频检索能够以否定的方式支持自然语言查询，例如，发现坐在地板上而不是和狗一起玩的孩子的照片。但是，最先进的基于深度学习的视频检索模型缺乏这种能力，因为它们通常在视频说明数据集中受过培训，例如MSR-VTT和VATEX，而缺乏否定的描述。他们的检索结果基本上忽略了示例查询中的否定器，错误地返回的视频显示了孩子们玩狗。本文介绍了关于学习视频检索中否定的第一个研究，并做出如下的贡献。通过重新修复两个现有数据集（MSR-VTT和VATEX），我们提出了一个新的评估协议，以进行否定。我们建议一种基于学习的方法来培训否定视频检索模型。关键的想法是首先通过部分否定其原始标题来为特定的培训视频构造软性标题，然后对三胞胎进行双向约束损失。这种辅助损失将重量添加到标准检索损失中。重新组合基准的实验表明，通过拟议的方法重新训练剪辑（对比语言图像预训练）模型清楚地提高了其用否定处理查询的能力。此外，原始基准测试的模型性能也得到了改进。

常规的自我监督单眼深度预测方法基于静态环境假设，这导致由于对象运动引入的不匹配和遮挡问题而导致动态场景的准确性降解。现有的以动态对象为中心的方法仅部分解决了训练损失级别的不匹配问题。在本文中，我们因此提出了一种新型的多帧单眼预测方法，以在预测和监督损失水平上解决这些问题。我们的方法称为DynamicDepth，是一个新框架，该框架是通过自我监督周期一致的学习方案训练的。提出了动态对象运动解开（DOMD）模块以解开对象运动以解决不匹配问题。此外，新颖的闭塞成本量和重新投射损失旨在减轻对象运动的闭塞作用。对CityScapes和Kitti数据集进行的广泛分析和实验表明，我们的方法显着优于最先进的单眼深度预测方法，尤其是在动态对象的领域。代码可从https://github.com/autoailab/dynamicdepth获得