中风康复旨在通过功能运动的重复实践来增加神经塑性，但由于重复不足，对恢复可能具有最小的影响。最佳培训内容和数量目前未知，因为不存在测量它们的实用工具。在这里，我们呈现Primseq，一个管道来分类和计算在笔划康复中培训的功能动作。我们的方法集成了可穿戴传感器来捕获上体运动，深度学习模型来预测运动序列，以及对Tally Motions的算法。训练有素的模型将康复活动分解成组件功能运动，优于竞争性机器学习方法。 Primseq进一步在人类专家的时间和劳动力成本的一小部分中量化了这些动作。我们展示了以前看不见的中风患者的Primseq的能力，这是一系列上肢电机损伤。我们预计这些进步将支持在中风康复中定量给药试验所需的严格测量。

从视频和动态数据自动活动识别是一种重要的机器学习问题，其应用范围从机器人到智能健康。大多数现有的作品集中在确定粗动作，如跑步，登山，或切割植物，其具有相对长的持续时间。这对于那些需要细微动作中的高时间分辨率识别应用的一个重要限制。例如，在中风恢复，定量康复剂量需要区分具有亚秒持续时间的运动。我们的目标是弥合这一差距。为此，我们引入了一个大规模，多数据集，StrokeRehab，为包括标记高时间分辨率微妙的短期操作的新动作识别基准。这些短期的行为被称为功能性原语和由河段，运输，重新定位，稳定作用，和空转的。所述数据集由高品质的惯性测量单元的传感器和执行的日常生活像馈送，刷牙等的活动41中风影响的病人的视频数据的，我们表明，基于分割产生嘈杂状态的最先进的现有机型预测时，对这些数据，这往往会导致行动超量。为了解决这个问题，我们提出了高分辨率的活动识别，通过语音识别技术的启发，它是基于一个序列到序列模型，直接预测的动作序列的新方法。这种方法优于国家的最先进的电流在StrokeRehab数据集的方法，以及对标准的基准数据集50Salads，早餐，和拼图。

Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback. As a principled way of incorporating market constraints and user incentives in the design, we consider our objectives to be two-fold: maximal social welfare with minimal instability. To maximize social welfare, our proposed framework enhances the quality of recommendations by exploring allocations that optimistically maximize the rewards. To minimize instability, a measure of users' incentives to deviate from recommended allocations, the algorithm prices the items based on a scheme derived from the Walrasian equilibria. Though it is known that these equilibria yield stable prices for markets with known user preferences, our approach accounts for the inherent uncertainty in the preferences and further ensures that the users accept their recommendations under offered prices. To the best of our knowledge, our approach is the first to integrate techniques from combinatorial bandits, optimal resource allocation, and collaborative filtering to obtain an algorithm that achieves sub-linear social welfare regret as well as sub-linear instability. Empirical studies on synthetic and real-world data also demonstrate the efficacy of our strategy compared to approaches that do not fully incorporate all these aspects.

Graph processing applications are severely bottlenecked by memory system performance due to low data reuse and irregular memory accesses. While state-of-the-art prefetchers using Machine Learning (ML) have made great progress, they do not perform well on graph analytics applications due to phase transitions in the execution and irregular data access that is hard to predict. We propose MPGraph: a novel ML-based Prefetcher for Graph analytics. MPGraph makes three novel optimizations based on domain knowledge of graph analytics. It detects the transition of graph processing phases during execution using a novel soft detection technique, predicts memory accesses and pages using phase-specific multi-modality predictors, and prefetches using a novel chain spatio-temporal prefetching strategy. We evaluate our approach using three widely-used graph processing frameworks and a variety of graph datasets. Our approach achieves 34.17%-82.15% higher precision in phase transition detection than the KSWIN and decision tree baselines. Our predictors achieve 6.80%-16.02% higher F1-score for access prediction and 11.68%-15.41% higher accuracy-at-10 for page prediction compared with the baselines LSTM-based and vanilla attention-based models. Simulations show that MPGraph achieves on the average 87.16% (prefetch accuracy) and 73.29% (prefetch coverage), leading to 12.52%-21.23% IPC improvement. It outperforms the widely-used non-ML prefetcher BO by 7.58%-12.03%, and outperforms state-of-the-art ML-based prefetchers Voyager by 3.27%-4.42% and TransFetch by 3.73%-4.58% with respect to IPC improvement.

In a mixed generalized linear model, the objective is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one. We consider the prototypical problem of estimating two statistically independent signals in a mixed generalized linear model with Gaussian covariates. Spectral methods are a popular class of estimators which output the top two eigenvectors of a suitable data-dependent matrix. However, despite the wide applicability, their design is still obtained via heuristic considerations, and the number of samples $n$ needed to guarantee recovery is super-linear in the signal dimension $d$. In this paper, we develop exact asymptotics on spectral methods in the challenging proportional regime in which $n, d$ grow large and their ratio converges to a finite constant. By doing so, we are able to optimize the design of the spectral method, and combine it with a simple linear estimator, in order to minimize the estimation error. Our characterization exploits a mix of tools from random matrices, free probability and the theory of approximate message passing algorithms. Numerical simulations for mixed linear regression and phase retrieval display the advantage enabled by our analysis over existing designs of spectral methods.

Pictionary, the popular sketch-based guessing game, provides an opportunity to analyze shared goal cooperative game play in restricted communication settings. However, some players occasionally draw atypical sketch content. While such content is occasionally relevant in the game context, it sometimes represents a rule violation and impairs the game experience. To address such situations in a timely and scalable manner, we introduce DrawMon, a novel distributed framework for automatic detection of atypical sketch content in concurrently occurring Pictionary game sessions. We build specialized online interfaces to collect game session data and annotate atypical sketch content, resulting in AtyPict, the first ever atypical sketch content dataset. We use AtyPict to train CanvasNet, a deep neural atypical content detection network. We utilize CanvasNet as a core component of DrawMon. Our analysis of post deployment game session data indicates DrawMon's effectiveness for scalable monitoring and atypical sketch content detection. Beyond Pictionary, our contributions also serve as a design guide for customized atypical content response systems involving shared and interactive whiteboards. Code and datasets are available at https://drawm0n.github.io.

Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples for each target task, thus annotating multiple datasets and training these models on various downstream tasks becomes time consuming and expensive. In this work, we propose a simple extension of the Prototypical Networks for few-shot text classification. Our main idea is to replace the class prototypes by Gaussians and introduce a regularization term that encourages the examples to be clustered near the appropriate class centroids. Experimental results show that our method outperforms various strong baselines on 13 public and 4 internal datasets. Furthermore, we use the class distributions as a tool for detecting potential out-of-distribution (OOD) data points during deployment.

最近的AI算法是黑框模型，其决策难以解释。可解释的AI（XAI）试图通过向客户解释其AI决定，例如决定拒绝贷款申请，以解决缺乏AI的解释性和信任。普遍的智慧是，通过规定完全透明的XAI来调节AI会导致更大的社会福利。本文通过游戏理论模型对一个最大化社会福利的决策制定者，在最大化利润最大化的双重垄断竞争和异性消费者的政策制定者中挑战了这一概念。结果表明XAI调节可能是多余的。实际上，要求完全透明的XAI可能会使公司和客户变得更糟。这揭示了最大化福利和获得可解释的AI输出之间的权衡。我们还讨论了对政策制定者和公司的管理意义。

当我们对优化模型中的不确定参数进行观察以及对协变量的同时观察时，我们研究了数据驱动决策的优化。鉴于新的协变量观察，目标是选择一个决定以此观察为条件的预期成本的决定。我们研究了三个数据驱动的框架，这些框架将机器学习预测模型集成在随机编程样本平均值近似（SAA）中，以近似解决该问题的解决方案。 SAA框架中的两个是新的，并使用了场景生成的剩余预测模型的样本外残差。我们研究的框架是灵活的，并且可以容纳参数，非参数和半参数回归技术。我们在数据生成过程，预测模型和随机程序中得出条件，在这些程序下，这些数据驱动的SaaS的解决方案是一致且渐近最佳的，并且还得出了收敛速率和有限的样本保证。计算实验验证了我们的理论结果，证明了我们数据驱动的公式比现有方法的潜在优势（即使预测模型被误解了），并说明了我们在有限的数据制度中新的数据驱动配方的好处。

增量学习是一种范式，可以通过流数据大规模构建模型构建和更新。对于端到端的自动语音识别（ASR）任务，缺乏人类注释的标签，以及需要保留模型建设政策的隐私政策，这使其成为艰巨的挑战。受这些挑战的激励，在本文中，我们使用基于云的框架为生产系统展示了从隐私保存自动语音识别（ILASR）的增量学习中的见解。我们的意思是，通过保留隐私性，对没有人类注释的短暂数据使用。该系统是用于增量/持续学习的生产LevelAsASR模型的一步，该模型提供了接近实时测试床，以在云中进行端到端ASR实验，同时遵守保留隐私的政策。我们表明，即使在没有人类注释的标签的情况下，拟议的系统也可以在六个月的新时间内显着改善生产模型（3％），而在增量学习中，较弱的监督和大批量大小。在新时期，这种改进比测试集的新单词和短语相比为20％。我们在ASR的同时进一步探讨了拥有有效的教师模型和使用大批量大小的实用性的同时，以保护隐私的增量方式展示了模型构建的有效性。