Datathon是一项涉及应用于特定问题的数据科学的时间限制的竞争。在过去的十年中，DATATHON已被证明是领域和专业知识之间的宝贵桥梁。生物医学数据分析是一个具有挑战性的领域，需要工程师，生物学家和医生之间的合作，以更好地了解患者生理学以及指导诊断，预后和治疗干预措施以改善护理实践的指导决策过程。在这里，我们反思了我们在2022年3月底在MIT关键数据组，Rambam Health Care Campus（Rambam）和Haifa技术以色列技术研究所（Technion Institute of Haifa）在以色列组织的活动的结果。要求参与者完成有关他们的技能和兴趣的调查，这使我们能够确定机器学习培训对医疗问题应用的最新需求。这项工作描述了以色列背景下医学数据科学的机会和局限性。

顺序决策的违规政策评估方法可用于帮助识别拟议的决策政策优于当前基线政策。但是，新的决策政策可能比某些人的基线政策更好，但不是其他人。这有动力推动个性化和准确的单态治疗效果估算（HTES）。鉴于许多重要应用中存在的有限数据，个体预测可以以准确性和在这种预测中的准确性和置信度的成本。通过识别子组，我们开发一种平衡对个人化的需求，以通过识别相对于基线的新决策政策中的预期差异来自信地估计预期估计。我们提出了一种新的损失函数，用于在子组分区阶段期间的不确定性。在实验中，我们表明我们的方法可用于形成其他方法斗争的HTES的准确预测。

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

尽管在利用深度学习来自动化胸部X光片解释和疾病诊断任务方面取得了进展，但顺序胸部X射线（CXR）之间的变化受到了有限的关注。监测通过胸部成像可视化的病理的进展在解剖运动估计和图像注册中构成了几个挑战，即在空间上对齐这两个图像并在变化检测中对时间动力学进行建模。在这项工作中，我们提出了Chexrelnet，这是一种可以跟踪两个CXR之间纵向病理关系的神经模型。Chexrelnet结合了局部和全球视觉特征，利用图像间和图像内的解剖信息，并学习解剖区域属性之间的依赖性，以准确预测一对CXR的疾病变化。与基准相比，胸部成像组数据集的实验结果显示下游性能提高。代码可从https://github.com/plan-lab/chexrelnet获得

Cohn and Umans proposed a framework for developing fast matrix multiplication algorithms based on the embedding computation in certain groups algebras. In subsequent work with Kleinberg and Szegedy, they connected this to the search for combinatorial objects called strong uniquely solvable puzzles (strong USPs). We begin a systematic computer-aided search for these objects. We develop and implement constraint-based algorithms build on reductions to $\mathrm{SAT}$ and $\mathrm{IP}$ to verify that puzzles are strong USPs, and to search for large strong USPs. We produce tight bounds on the maximum size of a strong USP for width $k \le 5$, construct puzzles of small width that are larger than previous work, and improve the upper bounds on strong USP size for $k \le 12$. Although our work only deals with puzzles of small-constant width, the strong USPs we find imply matrix multiplication algorithms that run in $O(n^\omega)$ time with exponent $\omega \le 2.66$. While our algorithms do not beat the fastest algorithms, our work provides evidence and, perhaps, a path to finding families of strong USPs that imply matrix multiplication algorithms that are more efficient than those currently known.

In this paper we take the first steps in studying a new approach to synthesis of efficient communication schemes in multi-agent systems, trained via reinforcement learning. We combine symbolic methods with machine learning, in what is referred to as a neuro-symbolic system. The agents are not restricted to only use initial primitives: reinforcement learning is interleaved with steps to extend the current language with novel higher-level concepts, allowing generalisation and more informative communication via shorter messages. We demonstrate that this approach allow agents to converge more quickly on a small collaborative construction task.

Nonconvex-nonconcave minimax optimization has been the focus of intense research over the last decade due to its broad applications in machine learning and operation research. Unfortunately, most existing algorithms cannot be guaranteed to converge and always suffer from limit cycles. Their global convergence relies on certain conditions that are difficult to check, including but not limited to the global Polyak-\L{}ojasiewicz condition, the existence of a solution satisfying the weak Minty variational inequality and $\alpha$-interaction dominant condition. In this paper, we develop the first provably convergent algorithm called doubly smoothed gradient descent ascent method, which gets rid of the limit cycle without requiring any additional conditions. We further show that the algorithm has an iteration complexity of $\mathcal{O}(\epsilon^{-4})$ for finding a game stationary point, which matches the best iteration complexity of single-loop algorithms under nonconcave-concave settings. The algorithm presented here opens up a new path for designing provable algorithms for nonconvex-nonconcave minimax optimization problems.

Customers are rapidly turning to social media for customer support. While brand agents on these platforms are motivated and well-intentioned to help and engage with customers, their efforts are often ignored if their initial response to the customer does not match a specific tone, style, or topic the customer is aiming to receive. The length of a conversation can reflect the effort and quality of the initial response made by a brand toward collaborating and helping consumers, even when the overall sentiment of the conversation might not be very positive. Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction. This paper demonstrates that we can predict engagement using initial customer and brand posts.

Objective. The impact of social determinants of health (SDoH) on patients' healthcare quality and the disparity is well-known. Many SDoH items are not coded in structured forms in electronic health records. These items are often captured in free-text clinical notes, but there are limited methods for automatically extracting them. We explore a multi-stage pipeline involving named entity recognition (NER), relation classification (RC), and text classification methods to extract SDoH information from clinical notes automatically. Materials and Methods. The study uses the N2C2 Shared Task data, which was collected from two sources of clinical notes: MIMIC-III and University of Washington Harborview Medical Centers. It contains 4480 social history sections with full annotation for twelve SDoHs. In order to handle the issue of overlapping entities, we developed a novel marker-based NER model. We used it in a multi-stage pipeline to extract SDoH information from clinical notes. Results. Our marker-based system outperformed the state-of-the-art span-based models at handling overlapping entities based on the overall Micro-F1 score performance. It also achieved state-of-the-art performance compared to the shared task methods. Conclusion. The major finding of this study is that the multi-stage pipeline effectively extracts SDoH information from clinical notes. This approach can potentially improve the understanding and tracking of SDoHs in clinical settings. However, error propagation may be an issue, and further research is needed to improve the extraction of entities with complex semantic meanings and low-resource entities using external knowledge.

Objective. Chemical named entity recognition (NER) models have the potential to impact a wide range of downstream tasks, from identifying adverse drug reactions to general pharmacoepidemiology. However, it is unknown whether these models work the same for everyone. Performance disparities can potentially cause harm rather than the intended good. Hence, in this paper, we measure gender-related performance disparities of chemical NER systems. Materials and Methods. We develop a framework to measure gender bias in chemical NER models using synthetic data and a newly annotated dataset of over 92,405 words with self-identified gender information from Reddit. We applied and evaluated state-of-the-art biomedical NER models. Results. Our findings indicate that chemical NER models are biased. The results of the bias tests on the synthetic dataset and the real-world data multiple fairness issues. For example, for synthetic data, we find that female-related names are generally classified as chemicals, particularly in datasets containing many brand names rather than standard ones. For both datasets, we find consistent fairness issues resulting in substantial performance disparities between female- and male-related data. Discussion. Our study highlights the issue of biases in chemical NER models. For example, we find that many systems cannot detect contraceptives (e.g., birth control). Conclusion. Chemical NER models are biased and can be harmful to female-related groups. Therefore, practitioners should carefully consider the potential biases of these models and take steps to mitigate them.