The introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving their performance. In lattice gauge theories, such information can be identified with gauge symmetries, which are incorporated into the network layers of our recently proposed Lattice Gauge Equivariant Convolutional Neural Networks (L-CNNs). L-CNNs can generalize better to differently sized lattices than traditional neural networks and are by construction equivariant under lattice gauge transformations. In these proceedings, we present our progress on possible applications of L-CNNs to Wilson flow or continuous normalizing flow. Our methods are based on neural ordinary differential equations which allow us to modify link configurations in a gauge equivariant manner. For simplicity, we focus on simple toy models to test these ideas in practice.

高能物理和晶格田理论的潜在对称发挥的至关重要作用要求在应用于所考虑的物理系统的神经网络架构中实施此类对称性。在这些程序中，我们专注于在网络属性之间纳入翻译成价的后果，特别是在性能和​​泛化方面。通过研究复杂的标量场理论，举例说明了等级网络的益处，其中检查了各种回归和分类任务。对于有意义的比较，通过系统搜索识别有前途的等效和非等效架构。结果表明，在大多数任务中，我们最好的设备架构可以明显更好地表现和概括，这不仅适用于超出培训集中所示的物理参数，还适用于不同的晶格尺寸。

近年来，在格子田地理论的背景下，使用机器学习越来越受欢迎。这些理论的基本要素由对称表示，其包含在神经网络属性中可以在性能和概括性方面导致高奖励。通常在具有周期性边界条件的晶格上表征物理系统的基本对称性是在空间翻译下的增义。在这里，我们调查采用翻译成分的神经网络，以支持非等价的优势。我们考虑的系统是一个复杂的标量字段，其在磁通表示中的二维格子上的四分之一交互，网络在其上执行各种回归和分类任务。有前途的等效和非成型架构被识别有系统搜索。我们证明，在大多数这些任务中，我们最好的体现架构可以比其非等效对应物更好地表现和概括，这不仅适用于训练集中所示的物理参数，还适用于不同的格子尺寸。

在这些诉讼中，我们呈现了格子仪表的卷积神经网络（L-CNNS），其能够从格子仪表理论模拟处理数据，同时完全保留仪表对称性。我们审查了架构的各个方面，并展示了L-CNNS如何代表晶格上的大类仪表不变性和设备的等效功能。我们使用非线性回归问题进行比较L-CNN和非等效网络的性能，并展示用于非等级模型的仪表不变性如何破坏。

我们审查了一种名为晶格计的新颖的神经网络架构，称为格子仪表的卷积神经网络（L-CNNS），可以应用于格子仪表理论中的通用机器学习问题，同时完全保留了规格对称性。我们讨论了用于明确构建规格的规范的衡量标准的概念，该卷大式卷积层和双线性层。使用看似简单的非线性回归任务比较L-CNNS和非成型CNN的性能，其中L-CNNS在与其非成型对应物相比，L-CNNS展示了概括性并在预测中实现了高度的准确性。

我们为晶格计上的普通机器学习应用提出了格子仪表的卷积卷积神经网络（L-CNNS）。在该网络结构的核心，是一种新颖的卷积层，其保留了规范设备，同时在连续的双线性层形成任意形状的威尔逊环。与拓扑信息一起，例如来自Polyakov环路，这样的网络原则上可以近似晶格上的任何仪表协调功能。我们展示了L-CNN可以学习和概括仪表不变的数量，传统的卷积神经网络无法找到。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded seven, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18 /100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the six common SDOH as covariates, NLP-extracted SDOH, on average, covered 84.38% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.67, 95% CI=2.46-2.89), followed by violence (aOR=2.26, 95% CI=2.11-2.43). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.

Objective: Evictions are involved in a cascade of negative events that can lead to unemployment, homelessness, long-term poverty, and mental health problems. In this study, we developed a natural language processing system to automatically detect eviction incidences and their attributes from electronic health record (EHR) notes. Materials and Methods: We annotated eviction status in 5000 EHR notes from the Veterans Health Administration. We developed a novel model, called Knowledge Injection based on Ripple Effects of Social and Behavioral Determinants of Health (KIRESH), that has shown to substantially outperform other state-of-the-art models such as fine-tuning pre-trained language models like BioBERT and Bio_ClinicalBERT. Moreover, we designed a prompt to further improve the model performance by using the intrinsic connection between the two sub-tasks of eviction presence and period prediction. Finally, we used the Temperature Scaling-based Calibration on our KIRESH-Prompt method to avoid over-confidence issues arising from the imbalance dataset. Results: KIRESH-Prompt achieved a Macro-F1 of 0.6273 (presence) and 0.7115 (period), which was significantly higher than 0.5382 (presence) and 0.67167 (period) for just fine-tuning Bio_ClinicalBERT model. Conclusion and Future Work: KIRESH-Prompt has substantially improved eviction status classification. In future work, we will evaluate the generalizability of the model framework to other applications.

当个人指出或谈论其他人的话语时，语言永久不平等的能力最为明显。尽管当前对NLP中偏见的研究主要依赖于对特定群体的仇恨言论或偏见，但我们认为我们可以通过建模说话者，文本和目标来对偏见与语言使用之间的相互作用的相互作用更加微妙和细微的理解在文字中。在本文中，我们介绍了一个由美国国会议员注释的3033个英语推文的数据集，并介绍了人际情绪的注释，并对人际关系成员标签进行了“找到监督”。我们发现，诸如愤怒和厌恶之类的负面情绪主要用于群体外部情况，主要针对对方领导人。虽然人类可以表现出色，而不是鉴定人际群体成员资格的机会，但神经模型的表现要好得多。此外，人际关系成员资格和人际关系情感之间的共同编码使后者有一些表现的提高。这项工作旨在将NLP中偏见的研究从特定的偏见中重新调整为封装说话者，文本，目标和社会动态之间关系的偏见。本文的数据和代码可从https://github.com/venkatasg/interpersonal-dynamics获得