一个多世纪以前，伊万·P·帕夫洛夫（Ivan P. Pavlov）在经典实验中展示了狗如何学会将铃铛与食物联系起来，从而导致戒指导致唾液。如今，很少发现使用Pavlovian类型的关联学习用于人工智能（AI）应用程序，即使其他学习概念，尤其是对人工神经网络（ANN）的反向传播也蓬勃发展。但是，使用反向传播方法的训练在“常规” ANN上，尤其是现代深神经网络（DNNS）的形式，是计算和能量密集型的。在这里，我们在实验上展示了使用单个（或单一）关联硬件元素的无反向传播学习形式。我们使用相位变换材料与芯片级联方向耦合器相结合的集成光子平台上意识到这一点。然后，我们使用我们的Monadic Pavlovian光子硬件开发扩展的电路网络，该硬件可以基于单元素关联提供独特的机器学习框架，并且重要的是，重要的是，使用无反向传播的架构来解决一般学习任务。我们的方法通过在传统的神经网络方法中学习来减轻施加的计算负担，从而提高了速度，同时还提供了我们光子实现固有的更高带宽。

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.

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low $(0<z<0.25)$, medium $(0.25<z<0.5)$, and high $(0.5<z<1.0)$. By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin. Specifically, our models achieve disk precision of $96\%/82\%/79\%$ and bulge precision of $90\%/90\%/80\%$ (for the 3 redshift bins), at thresholds corresponding to indeterminate fractions of $30\%/43\%/42\%$. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging survey.

A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast MCMC sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

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.

Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patterns in these types of data. However, existing methods usually ignore the negative influence of imbalanced data, or severe events, on model training. Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines and exhibits superior generalization ability on data with diverse distributions.

AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making. Moreover, many settings are dynamic, with populations responding to sequential decision policies. We introduce the study of reinforcement learning (RL) with stepwise fairness constraints, requiring group fairness at each time step. Our focus is on tabular episodic RL, and we provide learning algorithms with strong theoretical guarantees in regard to policy optimality and fairness violation. Our framework provides useful tools to study the impact of fairness constraints in sequential settings and brings up new challenges in RL.

在许多情况下，更简单的模型比更复杂的模型更可取，并且该模型复杂性的控制是机器学习中许多方法的目标，例如正则化，高参数调整和体系结构设计。在深度学习中，很难理解复杂性控制的潜在机制，因为许多传统措施并不适合深度神经网络。在这里，我们开发了几何复杂性的概念，该概念是使用离散的dirichlet能量计算的模型函数变异性的量度。使用理论论据和经验结果的结合，我们表明，许多常见的训练启发式方法，例如参数规范正规化，光谱规范正则化，平稳性正则化，隐式梯度正则化，噪声正则化和参数初始化的选择，都可以控制几何学复杂性，并提供一个统一的框架，以表征深度学习模型的行为。

了解强化学习（RL）代理的新兴行为可能很困难，因为这种代理通常使用高度复杂的决策程序在复杂的环境中进行训练。这引起了RL中解释性的多种方法，旨在调和可能在主体行为与观察者预期的行为之间产生的差异。最近的方法取决于域知识，这可能并非总是可用的，分析代理商的策略，或者是对基础环境的特定要素的分析，通常被建模为马尔可夫决策过程（MDP）。我们的主要主张是，即使基本的MDP尚不完全了解（例如，尚未准确地了解过渡概率），也没有由代理商维护（即，在使用无模型方法时），但仍可以利用它为自动生成解释。为此，我们建议使用以前在文献中使用的正式MDP抽象和转换来加快寻找最佳策略的搜索，以自动产生解释。由于这种转换通常基于环境的符号表示，因此它们可能代表了预期和实际代理行为之间差距的有意义的解释。我们正式定义了这个问题，建议一类可用于解释新兴行为的转换，并提出了有效搜索解释的方法。我们演示了一组标准基准测试的方法。

在神经网络中，与任务相关的信息由神经元组共同表示。但是，对信息分布在单个神经元之间的特定方式尚不清楚：虽然部分只能从特定的单个神经元中获得，但其他部分是由多个神经元冗余或协同携带的。我们展示了部分信息分解（PID）是信息理论的最新扩展，可以解散这些贡献。由此，我们介绍了“代表性复杂性”的度量，该量度量化了访问跨多个神经元信息的难度。我们展示了这种复杂性如何直接适用于较小的层。对于较大的层，我们提出了子采样和粗粒程序，并证明了后者的相应边界。从经验上讲，为了量化解决MNIST任务的深度神经网络，我们观察到，代表性复杂性通过连续的隐藏层和过度训练都会降低。总体而言，我们建议代表性复杂性作为分析神经表示结构的原则且可解释的摘要统计量。