尽管人工智能（AI）有望支持医疗保健提供者并提高医疗诊断的准确性，但数据集组成的缺乏透明度会使AI模型暴露于无意识和可避免的错误的可能性。特别是，皮肤病学条件的公共图像数据集很少包含有关肤色的信息。作为提高透明度的开始，AI研究人员已经从患者光敏性的度量到估算计算机视觉应用算法审核的肤色估算肤色（包括面部识别和皮肤病学诊断）的肤色估算肤色的度量来使用Fitzpatrick皮肤类型（FST）。为了了解图像上估计的FST注释的可变性，我们比较了来自教科书和在线皮肤病学试图的460张皮肤条件图像的多种FST注释方法。我们发现，三位经过董事会认证的皮肤科医生之间的评估者间可靠性与经过董事会认证的皮肤科医生和两种众包方法之间的评估者间可靠性相媲美。相比之下，我们发现转换为FST（ITA-FST）方法的单个类型学角度与专家注释相比，与专家的注释相关的注释相关的注释明显少于彼此相关。这些结果表明，基于ITA-FST的算法对于注释大规模图像数据集并不可靠，但是以人为本的，基于人群的协议可以可靠地将皮肤类型透明度添加到皮肤病学数据集中。此外，我们介绍了具有可调参数的动态共识协议的概念，包括专家审查，以提高人群的可见性并为未来的大型图像数据集的众包注释提供指导。

英语自然语言理解（NLU）系统已经取得了出色的表现，甚至在胶水和超级胶水等基准上表现出色。但是，这些基准仅包含教科书标准美国英语（SAE）。在NLP社区中，其他方言在很大程度上被忽略了。这导致偏见且不平等的NLU系统，仅服务于说话者的子人群。为了了解当前模型的差异并促进了更多的语言功能性的NLU系统，我们介绍了白话语言理解评估（Value）基准，这是我们使用一套词汇和形态句法转换规则创建的具有挑战性的胶水变体。在此最初版本（v.1）中，我们为非裔美国人白话英语（AAVE）的11个特征构建规则，并招募流利的AAVE扬声器，以通过参与性设计方式通过语言可接受性判断来验证每个功能转换。实验表明，这些新的方言功能可以导致模型性能下降。要运行转换代码并下载合成和金标准的方言胶水标准，请参见https://github.com/salt-nlp/value

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].

Dialect differences caused by regional, social, and economic barriers cause performance discrepancies for many groups of users of language technology. Fair, inclusive, and equitable language technology must critically be dialect invariant, meaning that performance remains constant over dialectal shifts. Current English systems often fall significantly short of this ideal since they are designed and tested on a single dialect: Standard American English. We introduce Multi-VALUE -- a suite of resources for evaluating and achieving English dialect invariance. We build a controllable rule-based translation system spanning 50 English dialects and a total of 189 unique linguistic features. Our translation maps Standard American English text to synthetic form of each dialect, which uses an upper-bound on the natural density of features in that dialect. First, we use this system to build stress tests for question answering, machine translation, and semantic parsing tasks. Stress tests reveal significant performance disparities for leading models on non-standard dialects. Second, we use this system as a data augmentation technique to improve the dialect robustness of existing systems. Finally, we partner with native speakers of Chicano and Indian English to release new gold-standard variants of the popular CoQA task.

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.

The biomedical imaging world is notorious for working with small amounts of data, frustrating state-of-the-art efforts in the computer vision and deep learning worlds. With large datasets, it is easier to make progress we have seen from the natural image distribution. It is the same with microscopy videos of neuron cells moving in a culture. This problem presents several challenges as it can be difficult to grow and maintain the culture for days, and it is expensive to acquire the materials and equipment. In this work, we explore how to alleviate this data scarcity problem by synthesizing the videos. We, therefore, take the recent work of the video diffusion model to synthesize videos of cells from our training dataset. We then analyze the model's strengths and consistent shortcomings to guide us on improving video generation to be as high-quality as possible. To improve on such a task, we propose modifying the denoising function and adding motion information (dense optical flow) so that the model has more context regarding how video frames transition over time and how each pixel changes over time.

Tendon-driven robots, where one or more tendons under tension bend and manipulate a flexible backbone, can improve minimally invasive surgeries involving difficult-to-reach regions in the human body. Planning motions safely within constrained anatomical environments requires accuracy and efficiency in shape estimation and collision checking. Tendon robots that employ arbitrarily-routed tendons can achieve complex and interesting shapes, enabling them to travel to difficult-to-reach anatomical regions. Arbitrarily-routed tendon-driven robots have unintuitive nonlinear kinematics. Therefore, we envision clinicians leveraging an assistive interactive-rate motion planner to automatically generate collision-free trajectories to clinician-specified destinations during minimally-invasive surgical procedures. Standard motion-planning techniques cannot achieve interactive-rate motion planning with the current expensive tendon robot kinematic models. In this work, we present a 3-phase motion-planning system for arbitrarily-routed tendon-driven robots with a Precompute phase, a Load phase, and a Supervisory Control phase. Our system achieves an interactive rate by developing a fast kinematic model (over 1,000 times faster than current models), a fast voxel collision method (27.6 times faster than standard methods), and leveraging a precomputed roadmap of the entire robot workspace with pre-voxelized vertices and edges. In simulated experiments, we show that our motion-planning method achieves high tip-position accuracy and generates plans at 14.8 Hz on average in a segmented collapsed lung pleural space anatomical environment. Our results show that our method is 17,700 times faster than popular off-the-shelf motion planning algorithms with standard FK and collision detection approaches. Our open-source code is available online.

Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently developed framework called Conformal Prediction (CP) to the multi-label learning setting. CP complements the predictions of machine learning algorithms with reliable measures of confidence. As a result the proposed approach instead of just predicting the most likely subset of classes for a new unseen instance, also indicates the likelihood of each predicted subset being correct. This additional information is especially valuable in the multi-label setting where the overall uncertainty is extremely high.

Despite the popularity of Vision Transformers (ViTs) and eXplainable AI (XAI), only a few explanation methods have been proposed for ViTs thus far. They use attention weights of the classification token on patch embeddings and often produce unsatisfactory saliency maps. In this paper, we propose a novel method for explaining ViTs called ViT-CX. It is based on patch embeddings, rather than attentions paid to them, and their causal impacts on the model output. ViT-CX can be used to explain different ViT models. Empirical results show that, in comparison with previous methods, ViT-CX produces more meaningful saliency maps and does a better job at revealing all the important evidence for prediction. It is also significantly more faithful to the model as measured by deletion AUC and insertion AUC.