整个幻灯片图像（WSI）分类是诊断和治疗疾病的基本任务；但是，精确标签的策划是耗时的，并限制了完全监督的方法的应用。为了解决这个问题，多个实例学习（MIL）是一种流行的方法，它仅使用幻灯片级标签作为一个弱监督的学习任务。尽管当前的MIL方法将注意机制的变体应用于具有更强模型的重量实例特征，但注意力不足是对数据分布的属性的不足。在这项工作中，我们建议通过使用Max-Instance（关键）功能的统计数据来重新校准WSI袋（实例）的分布。我们假设在二进制MIL中，正面袋的特征幅度大于负面，因此我们可以强制执行该模型，以最大程度地利用公制特征损失的袋子之间的差异，该袋子将正面袋模型为未分布。为了实现这一目标，与使用单批训练模式的现有MIL方法不同，我们建议平衡批次采样以有效地使用功能丢失，即同时（+/-）袋子。此外，我们采用编码模块（PEM）的位置来建模空间/形态信息，并通过变压器编码器通过多头自我注意（PSMA）进行汇总。现有基准数据集的实验结果表明我们的方法是有效的，并且对最先进的MIL方法有所改善。

在偏置数据集上培训的分类模型通常在分发外部的外部样本上表现不佳，因为偏置的表示嵌入到模型中。最近，已经提出了各种脱叠方法来解除偏见的表示，但仅丢弃偏见的特征是具有挑战性的，而不会改变其他相关信息。在本文中，我们提出了一种新的扩展方法，该方法使用不同标记图像的纹理表示明确地生成附加图像来放大训练数据集，并在训练分类器时减轻偏差效果。每个新的生成图像包含来自源图像的类似内容信息，同时从具有不同标签的目标图像传送纹理。我们的模型包括纹理共发生损耗，该损耗确定生成的图像的纹理是否与目标的纹理类似，以及确定所生成和源图像之间的内容细节是否保留的内容细节的空间自相似性丢失。生成和原始训练图像都进一步用于训练能够改善抗偏置表示的鲁棒性的分类器。我们使用具有已知偏差的五个不同的人工设计数据集来展示我们的方法缓解偏差信息的能力。对于所有情况，我们的方法表现优于现有的现有最先进的方法。代码可用：https://github.com/myeongkyunkang/i2i4debias

We introduce an end-to-end computational framework that enables hyperparameter optimization with the DeepHyper library, accelerated training, and interpretable AI inference with a suite of state-of-the-art AI models, including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-Net. We use these AI models and the benchmark QM9, hMOF, and MD17 datasets to showcase the prediction of user-specified materials properties in modern computing environments, and to demonstrate translational applications for the modeling of small molecules, crystals and metal organic frameworks with a unified, stand-alone framework. We deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership class computing environments.

Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. However, recent unsupervised models are extractive, which remove words from texts and thus they are less flexible than abstractive summarization. In this work, we devise an abstractive model based on reinforcement learning without ground-truth summaries. We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality. To further enhance the summary quality, we develop a multi-summary learning mechanism that generates multiple summaries with varying lengths for a given text, while making the summaries mutually enhance each other. Experimental results show that the proposed model substantially outperforms both abstractive and extractive models, yet frequently generating new words not contained in input texts.

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery. Efforts have been made to generalize these principles to research software and other digital products. Artificial intelligence (AI) models -- algorithms that have been trained on data rather than explicitly programmed -- are an important target for this because of the ever-increasing pace with which AI is transforming scientific and engineering domains. In this paper, we propose a practical definition of FAIR principles for AI models and create a FAIR AI project template that promotes adherence to these principles. We demonstrate how to implement these principles using a concrete example from experimental high energy physics: a graph neural network for identifying Higgs bosons decaying to bottom quarks. We study the robustness of these FAIR AI models and their portability across hardware architectures and software frameworks, and report new insights on the interpretability of AI predictions by studying the interplay between FAIR datasets and AI models. Enabled by publishing FAIR AI models, these studies pave the way toward reliable and automated AI-driven scientific discovery.

Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in more accurate clinical decisions when they are properly combined. However, despite its significance, how to effectively fuse the multi-modal medical data into a unified framework has received relatively little attention. In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data. Specifically, we construct a multiplex network that incorporates multiple types of non-image features of patients to capture the complex relationship between patients in a systematic way, which leads to more accurate clinical decisions. Extensive experiments on various real-world datasets demonstrate the superiority and practicality of HetMed. The source code for HetMed is available at https://github.com/Sein-Kim/Multimodal-Medical.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.

随着机器学习变得普遍，减轻培训数据中存在的任何不公平性变得至关重要。在公平的各种概念中，本文的重点是众所周知的个人公平，该公平规定应该对类似的人进行类似的对待。虽然在训练模型（对处理）时可以提高个人公平性，但我们认为在模型培训（预处理）之前修复数据是一个更基本的解决方案。特别是，我们表明标签翻转是改善个人公平性的有效预处理技术。我们的系统IFLIPPER解决了限制了个人公平性违规行为的最小翻转标签的优化问题，当培训数据中的两个类似示例具有不同的标签时，发生违规情况。我们首先证明问题是NP-HARD。然后，我们提出了一种近似的线性编程算法，并提供理论保证其结果与标签翻转数量有关的结果与最佳解决方案有多近。我们还提出了使线性编程解决方案更加最佳的技术，而不会超过违规限制。实际数据集上的实验表明，在看不见的测试集的个人公平和准确性方面，IFLIPPER显着优于其他预处理基线。此外，IFLIPPER可以与处理中的技术结合使用，以获得更好的结果。

顺序推荐系统通过捕获用户的兴趣漂移来显示有效的建议。有两组现有的顺序模型：以用户和项目为中心的模型。以用户为中心的模型根据每个用户的顺序消费历史记录来捕获个性化的利息漂移，但没有明确考虑用户对项目的利益是否超出培训时间，即利息可持续性。另一方面，以项目为中心的模型考虑了用户在培训时间后的一般利益是否维持，但不是个性化的。在这项工作中，我们提出了一个推荐系统，将两类模型的优势占据优势。我们提出的模型捕获了个性化的利息可持续性，表明每个用户对物品的利益是否会超出培训时间。我们首先制定一项任务，该任务需要根据用户的消费历史记录预测培训时间中每个用户将消耗哪些项目。然后，我们提出简单而有效的方案，以增强用户的稀疏消费历史记录。广泛的实验表明，所提出的模型在11个现实世界数据集上的表现优于10个基线模型。这些代码可在https://github.com/dmhyun/peris上找到。