最初受生物神经网络（BNN）启发的人工神经网络（ANN）在许多任务（例如视觉表示学习）中取得了巨大的成功。但是，由于缺乏有效的工具来链接和互为两个不同的域，并且缺乏代表的一般有效的框架，ANN和BNN中的视觉表示之间是否存在语义相关性/连接仍然很大程度上尚未探索。 BNN中的视觉语义，例如人类功能性脑网络（FBN）。为了回答这个问题，我们提出了一个新颖的计算框架，即同步激活（同步性），以基于自然主义的功能磁共振成像（NFMRI）数据来对人脑中的ANN和BNN之间的视觉表示空间和语义进行。通过这种方法，我们能够在第一次以人类脑成像得出的生物学上有意义的描述中对神经元进行注释。我们在两个公开观看的NFMRI数据集上评估了同步操作框架。该实验证明了a）FBN中视觉表示与各种卷积神经网络（CNN）模型中的视觉表示之间的显着相关性和相似性； b）CNN的视觉表示与BNN的相似性与其在图像分类任务中的性能之间的紧密关系。总体而言，我们的研究介绍了一个一般有效的范式，以融入ANN和BNNS，并为未来的研究提供新的见解，例如脑启发的人工智能。

快捷方式学习对深度学习模型很常见，但导致了退化的特征表示形式，因此危害了该模型的可推广性和解释性。但是，在广泛使用的视觉变压器框架中的快捷方式学习在很大程度上是未知的。同时，引入特定领域的知识是纠正捷径的主要方法，捷径为背景相关因素。例如，在医学成像领域中，放射科医生的眼睛凝视数据是一种有效的人类视觉先验知识，具有指导深度学习模型的巨大潜力，可以专注于有意义的前景区域。但是，获得眼睛凝视数据是时必的，劳动密集型的，有时甚至是不切实际的。在这项工作中，我们提出了一种新颖而有效的显着性视觉变压器（SGT）模型，以在没有眼神数据的情况下在VIT中纠正快捷方式学习。具体而言，采用计算视觉显着性模型来预测输入图像样本的显着性图。然后，显着图用于散布最有用的图像贴片。在拟议的中士中，图像贴片之间的自我注意力仅集中于蒸馏的信息。考虑到这种蒸馏操作可能会导致全局信息丢失，我们在最后一个编码器层中进一步介绍了一个残留的连接，该连接捕获了所有图像贴片中的自我注意力。四个独立公共数据集的实验结果表明，我们的SGT框架可以有效地学习和利用人类的先验知识，而无需眼睛凝视数据，并且比基线更好。同时，它成功地纠正了有害的快捷方式学习并显着提高了VIT模型的解释性，证明了传递人类先验知识在纠正快捷方式学习方面传递人类先验知识的承诺

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. To the best of our knowledge, this is among the first attempts to study the complex heterogeneous progression of LLD based on task-oriented and handcrafted MRI features. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics can still occur due to potential looseness. In this study, deep learning is used to compensate for the imperfect interface and improve measurement accuracy. A set of one-dimensional convolutional neural network (1D-CNN) models was developed to denoise mouthguard kinematics measurements along three spatial axes of linear acceleration and angular velocity. The denoised kinematics had significantly reduced errors compared to reference kinematics, and reduced errors in brain injury criteria and tissue strain and strain rate calculated via finite element modeling. The 1D-CNN models were also tested on an on-field dataset of college football impacts and a post-mortem human subject dataset, with similar denoising effects observed. The models can be used to improve detection of head impacts and TBI risk evaluation, and potentially extended to other sensors measuring kinematics.

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.

This paper is a technical overview of DeepMind and Google's recent work on reinforcement learning for controlling commercial cooling systems. Building on expertise that began with cooling Google's data centers more efficiently, we recently conducted live experiments on two real-world facilities in partnership with Trane Technologies, a building management system provider. These live experiments had a variety of challenges in areas such as evaluation, learning from offline data, and constraint satisfaction. Our paper describes these challenges in the hope that awareness of them will benefit future applied RL work. We also describe the way we adapted our RL system to deal with these challenges, resulting in energy savings of approximately 9% and 13% respectively at the two live experiment sites.

We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extracted from relational databases (RDBs). Intuitively, this joint use of SSL and GNNs should allow to leverage more of the available data, which could translate to better results. However, we found that naively porting contrastive SSL techniques can cause ``negative transfer'': linear evaluation on fixed representations from a pretrained model performs worse than on representations from the randomly-initialized model. Based on the conjecture that contrastive SSL conflicts with the message passing layers of the GNN, we propose InfoNode: a contrastive loss aiming to maximize the mutual information between a node's initial- and final-layer representation. The primary empirical results support our conjecture and the effectiveness of InfoNode.

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.