每年有超过500万五岁以下的儿童死于大部分可预防或可治疗的医疗状况，而在疫苗接种率低的欠发达国家中，死亡人数大部分大部分发生。联合国可持续发展目标之一（SDG 3）旨在结束五岁以下的新生儿和儿童的可预防死亡。我们专注于尼日利亚，在尼日利亚，婴儿死亡率令人震惊。我们与尼日利亚的大型非营利组织Helpmum合作设计和优化了不确定性下的异质健康干预措施的分配，以增加疫苗接种的吸收，这是尼日利亚的首次此类合作。我们的框架，顾问：AI驱动的疫苗接种干预优化器基于整数线性程序，该计划旨在最大程度地提高成功疫苗接种的累积概率。我们的优化公式在实践中是棘手的。我们提出了一种启发式方法，使我们能够解决现实世界中用例的问题。我们还为启发式方法提出了理论界限。最后，我们表明，通过实验评估，所提出的方法在疫苗接种方面优于基线方法。 Helpmum目前正在计划基于我们在最大的尼日利亚城市部署的方法，这将是该国AI驱动的疫苗接种吸收计划的首次部署，并希望为其他数据驱动计划铺平道路改善尼日利亚的健康状况。

强化学习（RL）使代理能够学习如何从头开始执行各种任务。在自动驾驶，推荐系统等域中，更优化的RL政策如果政策记住私人奖励的任何部分，则会促使隐私违约。我们研究了来自各种RL算法的现有差异私有RL策略，例如价值迭代，深Q网络和vanilla近端策略优化。我们提出了一种新的隐私感知逆RL（PRIL）分析框架，该框架执行奖励重建作为代理人可以部署的私人政策的对抗攻击。为此，我们介绍了奖励重建攻击，其中我们寻求使用逆RL算法从隐私保留策略重建原始奖励。如果代理商使用紧密私人政策，对手必须在重建原始奖励函数时进行糟糕。使用本框架，我们经验测试私有算法提供的隐私保证的有效性在不同复杂性的冻结领域的多个实例上。基于进行的分析，我们推断了所提供的目前的隐私标准与保护奖励函数所需的标准之间的差距。我们通过量化每个私营策略通过测量原始和重建奖励之间的距离来保护奖励功能的程度来实现。

Reliable and cost-effective counting of people in large indoor spaces is a significant challenge with many applications. An emerging approach is to deploy multiple fisheye cameras mounted overhead to monitor the whole space. However, due to the overlapping fields of view, person re-identificaiton (PRID) is critical for the accuracy of counting. While PRID has been thoroughly researched for traditional rectilinear cameras, few methods have been proposed for fisheye cameras and their performance is comparatively lower. To close this performance gap, we propose a multi-feature framework for fisheye PRID where we combine deep-learning, color-based and location-based features by means of novel feature fusion. We evaluate the performance of our framework for various feature combinations on FRIDA, a public fisheye PRID dataset. The results demonstrate that our multi-feature approach outperforms recent appearance-based deep-learning methods by almost 18% points and location-based methods by almost 3% points in accuracy.

With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO2 storage has shown promising results in its ability to monitor the growth of the CO2 plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO2 concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO2 plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO2 plumes by leveraging Class Activation Mapping methods.

Explainable Artificial Intelligence (AI) in the form of an interpretable and semiautomatic approach to stage grading ocular pathologies such as Diabetic retinopathy, Hypertensive retinopathy, and other retinopathies on the backdrop of major systemic diseases. The experimental study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. Many current CNN-based deep neural networks used for diagnosing retinal disorders might have appreciable performance but fail to pinpoint the basis driving their decisions. To improve these decisions' transparency, we have proposed a clinician-in-the-loop assisted intelligent workflow that performs a retinal vascular assessment on the fundus images to derive quantifiable and descriptive parameters. The retinal vessel parameters meta-data serve as hyper-parameters for better interpretation and explainability of decisions. The semiautomatic methodology aims to have a federated approach to AI in healthcare applications with more inputs and interpretations from clinicians. The baseline process involved in the machine learning pipeline through image processing techniques for optic disc detection, vessel segmentation, and arteriole/venule identification.

Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i.e., opioid use), and evaluate the extraction rate of SDoH using cancer populations. Methods: We identified SDoH categories and attributes and developed an SDoH corpus using clinical notes from a general cancer cohort. We compared four transformer-based NLP models to extract SDoH, examined the generalizability of NLP models to a cohort of patients prescribed with opioids, and explored customization strategies to improve performance. We applied the best NLP model to extract 19 categories of SDoH from the breast (n=7,971), lung (n=11,804), and colorectal cancer (n=6,240) cohorts. Results and Conclusion: We developed a corpus of 629 cancer patients notes with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH. The Bidirectional Encoder Representations from Transformers (BERT) model achieved the best strict/lenient F1 scores of 0.9216 and 0.9441 for SDoH concept extraction, 0.9617 and 0.9626 for linking attributes to SDoH concepts. Fine-tuning the NLP models using new annotations from opioid use patients improved the strict/lenient F1 scores from 0.8172/0.8502 to 0.8312/0.8679. The extraction rates among 19 categories of SDoH varied greatly, where 10 SDoH could be extracted from >70% of cancer patients, but 9 SDoH had a low extraction rate (<70% of cancer patients). The SODA package with pre-trained transformer models is publicly available at https://github.com/uf-hobiinformatics-lab/SDoH_SODA.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

This work proposes Multi-task Meta Learning (MTML), integrating two learning paradigms Multi-Task Learning (MTL) and meta learning, to bring together the best of both worlds. In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks with fewer data, a quality of meta learning. It is important to highlight that we focus on heterogeneous tasks, which are of distinct kind, in contrast to typically considered homogeneous tasks (e.g., if all tasks are classification or if all tasks are regression tasks). The fundamental idea is to train a multi-task model, such that when an unseen task is introduced, it can learn in fewer steps whilst offering a performance at least as good as conventional single task learning on the new task or inclusion within the MTL. By conducting various experiments, we demonstrate this paradigm on two datasets and four tasks: NYU-v2 and the taskonomy dataset for which we perform semantic segmentation, depth estimation, surface normal estimation, and edge detection. MTML achieves state-of-the-art results for most of the tasks. Although semantic segmentation suffers quantitatively, our MTML method learns to identify segmentation classes absent in the pseudo labelled ground truth of the taskonomy dataset.

在软件开发过程中，开发人员需要回答有关代码语义方面的查询。即使已经用神经方法进行了广泛的自然语言研究，但尚未探索使用神经网络对代码回答语义查询的问题。这主要是因为没有现有的数据集，具有提取性问答和答案对，涉及复杂概念和较长推理的代码。我们通过构建一个名为Codequeries的新的，策划的数据集并提出了一种关于代码的神经问题方法来弥合这一差距。我们基于最先进的预训练的代码模型，以预测答案和支持事实跨度。给定查询和代码，只有一些代码可能与回答查询有关。我们首先在理想的环境下进行实验，其中仅给出了模型的相关代码，并表明我们的模型做得很好。然后，我们在三个务实的考虑因素下进行实验：（1）扩展到大尺寸的代码，（2）从有限数量的示例中学习，（3）代码中对次要语法错误的鲁棒性。我们的结果表明，虽然神经模型可以抵御代码中的次要语法错误，代码的大小增加，与查询无关的代码的存在以及减少的培训示例数量限制了模型性能。我们正在释放数据和模型，以促进未来关于回答代码语义查询的问题的工作。

抽象已被广泛研究，以提高增强学习算法的效率和概括。在本文中，我们研究了连续控制环境中的抽象。我们将MDP同态的定义扩展到连续状态空间中的连续作用。我们在抽象MDP上得出了策略梯度定理，这使我们能够利用环境的近似对称性进行策略优化。基于该定理，我们提出了一种能够使用Lax Bisimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation Mimulation。我们证明了我们方法对DeepMind Control Suite中基准任务的有效性。我们的方法利用MDP同态来表示学习的能力会导致从像素观测中学习时的性能。