计算机视觉和机器学习的进步使机器人能够以强大的新方式感知其周围环境，但是这些感知模块具有众所周知的脆弱性。我们考虑了合成尽管有知觉错误的安全控制器的问题。所提出的方法基于具有输入依赖性噪声的高斯过程构建状态估计器。该估计器为给定状态计算实际状态的高信心集。然后，合成了可证明可以处理状态不确定性的强大神经网络控制器。此外，提出了一种自适应采样算法来共同改善估计器和控制器。模拟实验，包括Carla中基于逼真的巷道示例，说明了提出方法在与基于深度学习的感知合成强大控制器中提出的方法的希望。

现代自动驾驶汽车系统使用复杂的感知和控制组件，必须应对从传感器收到的不确定数据。为了估计此类车辆保持安全状态的可能性，开发人员经常采用耗时的模拟方法。本文提出了一种基于广义多项式混乱（GPC）的车辆系统中自治管道的替代方法。我们还提出了气体，这是第一种用于创建和使用复杂车辆系统的GPC模型的算法。气体用感知模型代替了复杂的感知成分，以降低复杂性。然后，它构建了GPC模型，并将其用于估计状态分布和/或输入不安全状态的概率。我们在农作物管理车辆，自动驾驶汽车和空中无人机中使用的五种情况下评估气体 - 每个系统都使用至少一个复杂的感知或控制组件。我们表明，气体计算的状态分布与蒙特卡洛模拟所产生的分布非常匹配，同时也提供2.3倍-3.0倍的加速。

对象检测，车道检测和分割的卷积神经网络（CNN）现在坐在大多数自主管道的头部，然而，他们的安全分析仍然是一个重要的挑战。对感知模型的正式分析是根本困难的，因为他们的正确性是难以指定的，如果不是不可能指定。我们提出了一种从系统级安全要求，数据和从感知下游的模块的模块的识字模型推断出可理解和安全抽象的技术。该技术可以帮助在创建抽象和随后的验证方面进行权衡安全性，大小和精度。我们将该方法应用于基于高保真仿真（a）用于自主车辆的视觉的车道保持控制器的两个重要案例研究，并且（b）用于农业机器人的控制器。我们展示了所生成的抽象如何与下游模块组成，然后可以使用像CBMC等程序分析工具验证所产生的抽象系统。详细评估规模，安全要求和环境参数（例如，照明，路面，植物类型）对所产生的抽象精度的影响表明，该方法可以帮助指导寻找角落案例和安全操作包围。

统计模型检查是一类顺序算法，可以验证网络物理系统集合中感兴趣的规格（例如，来自批处理的99％的汽车是否符合其能源效率的要求）。这些算法通过绘制足够数量的独立和相同分布的样本来推断具有可证明的统计保证的系统满足给定规范的概率。在统计模型检查过程中，可能会推断出样品的值（例如，用户的汽车能源效率），从而在消费者级别的应用程序（例如自闭症和医疗设备）中引起隐私问题。本文从差异隐私的角度介绍了统计模型检查算法的隐私。这些算法是顺序的，绘制样品直到满足其值的条件。我们表明，揭示绘制的样品数量可能侵犯隐私。我们还表明，在顺序算法的背景下，将算法的输出随机输出的标准指数机制无法实现。取而代之的是，我们放宽了差异隐私的保守要求，即该算法的输出的灵敏度应与任何数据集的任何扰动界定。我们提出了一个新的差异隐私概念，我们称之为预期的差异隐私。然后，我们提出了对顺序算法的新型预期灵敏度分析，并提出了一种相应的指数机制，该机制将终止时间随机，以实现预期的差异隐私。我们将提出的机制应用于统计模型检查算法，以保留其绘制样品的隐私。在案例研究中证明了所提出算法的效用。

In large-scale machine learning, recent works have studied the effects of compressing gradients in stochastic optimization in order to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in large-scale, multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? In this paper, we investigate this question by studying a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction, where a general compression operator is used to model the perturbation. Our main technical contribution is to show that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic theoretical guarantees as their SGD counterparts. We then extend our results significantly to nonlinear stochastic approximation algorithms and multi-agent settings. In particular, we prove that for multi-agent TD learning, one can achieve linear convergence speedups in the number of agents while communicating just $\tilde{O}(1)$ bits per agent at each time step. Our work is the first to provide finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling. Our analysis hinges on studying the drift of a novel Lyapunov function that captures the dynamics of a memory variable introduced by error feedback.

The devastation caused by the coronavirus pandemic makes it imperative to design automated techniques for a fast and accurate detection. We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The Attention module combines contextual with local information, at multiple scales, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of EAMC, even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics. Its clinical significance lies in its unprecedented scope in providing low-cost decision-making for patients lacking specialized healthcare at remote locations.

A hallmark of human intelligence is the ability to learn new concepts purely from language. Several recent approaches have explored training machine learning models via natural language supervision. However, these approaches fall short in leveraging linguistic quantifiers (such as 'always' or 'rarely') and mimicking humans in compositionally learning complex tasks. Here, we present LaSQuE, a method that can learn zero-shot classifiers from language explanations by using three new strategies - (1) modeling the semantics of linguistic quantifiers in explanations (including exploiting ordinal strength relationships, such as 'always' > 'likely'), (2) aggregating information from multiple explanations using an attention-based mechanism, and (3) model training via curriculum learning. With these strategies, LaSQuE outperforms prior work, showing an absolute gain of up to 7% in generalizing to unseen real-world classification tasks.

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results. Data processing systems often execute the queries on samples to produce results with low latency. Different downsampling strategy preserves different statistics of the data and have different magnitude of latency reductions. The optimum choice of sampling strategy often depends on the particular context of the analysis flow and the hidden intent of the analyst. In this paper, we are the first to consider the impact of sampling in interactive data exploration settings as they introduce approximation errors. We propose a Deep Reinforcement Learning (DRL) based framework which can optimize the sample selection in order to keep the analysis and insight generation flow intact. Evaluations with 3 real datasets show that our technique can preserve the original insight generation flow while improving the interaction latency, compared to baseline methods.

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.