本文考虑了层次多标签分类（HMC）的问题，其中（i）每个示例都可以存在几个标签，并且（ii）标签通过特定于域的层次结构相关。在直觉的指导下，所有错误都不相等，我们提出了全面的层次结构意识到多标签预测（Champ），该框架会根据其严重性根据层次结构树惩罚错误预测。据我们所知，有一些作品将这种想法应用于单标签分类，但对于多标签分类，有限的作品侧重于错误的严重性。关键原因是没有明确的方法可以在多标签设置中量化错误预测的严重性。在这项工作中，我们提出了一个简单但有效的指标，以量化HMC中错误的严重性，自然会导致冠军。在跨模态六个公共HMC数据集（图像，音频和文本）上进行的广泛实验表明，纳入层次信息会带来可观的增长，因为Champ提高了AUPRC（2.6％的中位数改善）和层次指标（2.85％的中位数提高百分比）（超过2.85％）独立分层或多标签分类方法。与标准的多标记基线相比，Champ在鲁棒性（平均提高百分比8.87％）和数据制度更少的稳健性（8.87％）方面提供了改进的AUPRC。此外，我们的方法提供了一个框架来增强具有更好错误的现有多标签分类算法（平均百分比增量为18.1％）。

Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.

多项研究表明，从孕妇中期超声检查（USG）检查获得标准化的胎儿脑生物特征？获得这些测量值是高度主观的，专业驱动的，需要多年的培训经验，从而限制了所有怀孕母亲的优质产前护理。在这项研究中，我们提出了一种深度学习方法（DL）方法，以通过准确和自动化的卡钳放置（每次生物测量法）将其作为地标建模，从而从跨炉平面（TC）的2D USG图像（TC）计算3个关键的胎儿脑生物特征。检测问题。我们利用了临床相关的生物识别约束（卡尺点之间的关系）和与域相关的数据增强，以提高U-NET DL模型的准确性（经过训练/测试：596张图像，473个受试者/143张图像，143个受试者）。我们进行了多个实验，证明了DL主链，数据增强，推广性和基准测试，通过广泛的临床验证（DL与7位经验丰富的临床医生）对最新的最新方法进行了测试。在所有情况下，单个卡尺点和计算生物特征的放置的平均误差都与临床医生之间的错误率相当。所提出的框架的临床翻译可以帮助新手用户在可靠和标准化的胎儿大脑超声图评估中的新手使用者。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.

KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

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.