人工智能尚未彻底改变材料和分子的设计。在这种观点中，我们确定了四个障碍，阻碍了原子深度学习，分子科学和高性能计算的整合。我们概述了重点的研究努力，解决这些挑战所提供的机会。

分子和材料科学的深度学习受应用科学，人工智能和高性能计算之间缺乏融合的限制。关于培训数据量，模型架构的规模和复杂程度以及计算基础设施的规模的瓶颈是限制分子和材料深度学习缩放的关键因素。在这里，我们呈现$ \ texit {litmatter} $，轻量级框架用于缩放分子深度学习方法。我们在超过400个GPU上培训四个图形神经网络架构，并调查这些方法的缩放行为。根据模型架构，可以看到高达60美元的培训时间加速。经验神经缩放关系量化模型依赖性缩放，使能最优计算资源分配和可伸缩分子几何深度学习模型实现的识别。

传统的基于频率的投影滤波器，或投影运算符（PO），通过一系列变换单独的信号和噪声，该变换除去存在噪声的频率。然而，该技术依赖于先验的频率包含信号和噪声的知识，并且这些频率不重叠，这难以在实践中实现。为了解决这些问题，我们介绍了PO-Neural网络混合模型，伪投影算子（PPO），其利用神经网络进行频率选择。我们将PPO，PO和DENOISING AUTOENCODER（DAE）的过滤功能与罗切斯特大学的多模态音乐性能数据集进行了各种添加的噪声类型。在大多数实验中，PPO都优于PO和DAE。根据这些结果，我们建议将PPO应用于身体和生物科学中的问题。

Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. That is, where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can significantly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation.

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

在过去的十年中，卷积神经网络（Convnets）主导了医学图像分析领域。然而，发现脉搏的性能仍然可以受到它们无法模拟图像中体素之间的远程空间关系的限制。最近提出了众多视力变压器来解决哀悼缺点，在许多医学成像应用中展示最先进的表演。变压器可以是用于图像配准的强烈候选者，因为它们的自我注意机制能够更精确地理解移动和固定图像之间的空间对应。在本文中，我们呈现透射帧，一个用于体积医学图像配准的混合变压器-Cromnet模型。我们还介绍了三种变速器的变形，具有两个散晶变体，确保了拓扑保存的变形和产生良好校准的登记不确定性估计的贝叶斯变体。使用来自两个应用的体积医学图像的各种现有的登记方法和变压器架构进行广泛验证所提出的模型：患者间脑MRI注册和幻影到CT注册。定性和定量结果表明，传输和其变体导致基线方法的实质性改进，展示了用于医学图像配准的变压器的有效性。

通过叶子检测和跟踪移动目标是困难的，并且在许多情况下甚至不可能在常规空中图像和视频中。我们提出了一种初始轻型和无人机操作的1D摄像头阵列，其支持并行合成孔径空中成像。我们的主要发现是，与传统的单个图像或视频帧相比，颜色异常检测效益显着从图像集成时（平均97％在我们的现场实验中的精确度）。我们展示，这两项贡献可能导致通过密集封闭森林的检测和跟踪移动人员

一个多世纪以前，伊万·P·帕夫洛夫（Ivan P. Pavlov）在经典实验中展示了狗如何学会将铃铛与食物联系起来，从而导致戒指导致唾液。如今，很少发现使用Pavlovian类型的关联学习用于人工智能（AI）应用程序，即使其他学习概念，尤其是对人工神经网络（ANN）的反向传播也蓬勃发展。但是，使用反向传播方法的训练在“常规” ANN上，尤其是现代深神经网络（DNNS）的形式，是计算和能量密集型的。在这里，我们在实验上展示了使用单个（或单一）关联硬件元素的无反向传播学习形式。我们使用相位变换材料与芯片级联方向耦合器相结合的集成光子平台上意识到这一点。然后，我们使用我们的Monadic Pavlovian光子硬件开发扩展的电路网络，该硬件可以基于单元素关联提供独特的机器学习框架，并且重要的是，重要的是，使用无反向传播的架构来解决一般学习任务。我们的方法通过在传统的神经网络方法中学习来减轻施加的计算负担，从而提高了速度，同时还提供了我们光子实现固有的更高带宽。

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .