在许多应用程序（例如运动锦标赛或推荐系统）中，我们可以使用该数据，包括一组$ n $项目（或玩家）之间的成对比较。目的是使用这些数据来推断每个项目和/或其排名的潜在强度。此问题的现有结果主要集中在由单个比较图$ g $组成的设置上。但是，存在成对比较数据随时间发展的场景（例如体育比赛）。这种动态设置的理论结果相对有限，是本文的重点。我们研究\ emph {翻译同步}问题的扩展，到动态设置。在此设置中，我们给出了一系列比较图$（g_t）_ {t \ in \ mathcal {t}} $，其中$ \ nathcal {t} \ subset [0,1] $是代表时间的网格域，对于每个项目$ i $和time $ t \ in \ mathcal {t} $，有一个关联的未知强度参数$ z^*_ {t，i} \ in \ mathbb {r} $。我们的目标是恢复，以$ t \在\ Mathcal {t} $中，强度向量$ z^*_ t =（z^*_ {t，1}，\ cdots，z^*_ {t，n}） $从$ z^*_ {t，i} -z^*_ {t，j} $的噪声测量值中，其中$ \ {i，j \} $是$ g_t $中的边缘。假设$ z^*_ t $在$ t $中顺利地演变，我们提出了两个估计器 - 一个基于平滑度的最小二乘方法，另一个基于对合适平滑度操作员低频本质空间的投影。对于两个估计器，我们为$ \ ell_2 $估计错误提供有限的样本范围，假设$ g_t $已连接到\ mathcal {t} $中的所有$ t \网格尺寸$ | \ MATHCAL {T} | $。我们通过有关合成和真实数据的实验来补充理论发现。

现实世界网络经常具有侧面信息，可以帮助提高网络分析任务等群集的性能。尽管在过去十年中对网络聚类方法进行了大量的实证和理论研究，但侧面信息的附加值和用于在聚类算法中最佳地结合的方法的附加值相对较少理解。我们向群集网络提出了一种新的迭代算法，其中包含节点的侧面信息（以协调因子的形式）提出并表明我们的算法在上下文对称随机块模型下是最佳的。我们的算法可以应用于一般上下文随机块模型，并避免与先前提出的方法相比，避免了HyperParameter调整。我们在综合数据实验中确认我们的理论结果，其中我们的算法显着优于其他方法，并表明它也可以应用于签名的图表。最后，我们展示了我们对实际数据方法的实际兴趣。

在信号处理中，若干应用涉及给出噪声模型样本的函数的恢复。本文考虑的设置是由于模数运行，通过添加剂高斯噪声破坏的样本。该问题的典型示例在相位展开问题或在自复位模拟到数字转换器的上下文中出现。我们考虑一个固定的设计设置，其中在常规网格上给出了模态样本。然后，提出了三个阶段的恢复策略来将地面真理信号恢复到全局整数班次。第一阶段通过使用局部多项式估计器来剥夺模型样本。在第二阶段，将展开算法应用于网格上的去噪模式。最后，使用了一种样条曲的准立体算子来产生对地面真理函数的估计到全局整数偏移。对于H \“较旧的类，均匀的误差率具有高概率的恢复性能。这扩展了由Fanuel和Tyagi获得的最近结果，用于Lipschitz平滑功能，其中在去噪步骤中使用了$ K $ NN回归。

在许多应用中，我们获得了流畅的函数的嘈杂模态样本的访问，其目标是鲁棒地解开样本，即估计该功能的原始样本。在最近的工作中，Cucuringu和Tyagi通过首先将它们代表在单元复杂圆上，然后解决平滑度规则化最小二乘问题 - Laplacian的平滑度适用的Proximity Graph的平滑度$ G $ - ON单位圆的产品歧管。这个问题是二次受约束的二次程序（QCQP），其是非凸显的，因此提出解决其球形放松导致信任区域子问题（TRS）。就理论担保而言，派生$ \ ell_2 $错误界限（trs）。然而，这些界限通常弱，并且没有真正证明由（TRS）进行的去噪。在这项工作中，我们分析（TRS）以及（QCQP）的不受约束的放松。对于这些估算器，我们在高斯噪声的设置中提供了一种精致的分析，并导出了噪音制度，其中他们可否证明模数观察W.R.T $ \ ell_2 $常规。分析在$ G $是任何连接的图形中的常规设置中进行。

We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made. We provide an algorithm that outputs a mean estimate at every time instant $t$ such that the overall release is user-level $\varepsilon$-DP and has the following error guarantee: Denoting by $M_t$ the maximum number of samples contributed by a user, as long as $\tilde{\Omega}(1/\varepsilon)$ users have $M_t/2$ samples each, the error at time $t$ is $\tilde{O}(1/\sqrt{t}+\sqrt{M}_t/t\varepsilon)$. This is a universal error guarantee which is valid for all arrival patterns of the users. Furthermore, it (almost) matches the existing lower bounds for the single-release setting at all time instants when users have contributed equal number of samples.

Soft actuators have attracted a great deal of interest in the context of rehabilitative and assistive robots for increasing safety and lowering costs as compared to rigid-body robotic systems. During actuation, soft actuators experience high levels of deformation, which can lead to microscale fractures in their elastomeric structure, which fatigues the system over time and eventually leads to macroscale damages and eventually failure. This paper reports finite element modeling (FEM) of pneu-nets at high angles, along with repetitive experimentation at high deformation rates, in order to study the effect and behavior of fatigue in soft robotic actuators, which would result in deviation from the ideal behavior. Comparing the FEM model and experimental data, we show that FEM can model the performance of the actuator before fatigue to a bending angle of 167 degrees with ~96% accuracy. We also show that the FEM model performance will drop to 80% due to fatigue after repetitive high-angle bending. The results of this paper objectively highlight the emergence of fatigue over cyclic activation of the system and the resulting deviation from the computational FEM model. Such behavior can be considered in future controllers to adapt the system with time-variable and non-autonomous response dynamics of soft robots.

As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy sensitive applications such as autonomous driving and biometric authentication, it is critical to understand the fault-tolerance nature of DNNs. Prior work primarily focuses on metrics such as Failures In Time (FIT) rate and the Silent Data Corruption (SDC) rate, which quantify how often a device fails. Instead, this paper focuses on quantifying the DNN accuracy given that a transient error has occurred, which tells us how well a network behaves when a transient error occurs. We call this metric Resiliency Accuracy (RA). We show that existing RA formulation is fundamentally inaccurate, because it incorrectly assumes that software variables (model weights/activations) have equal faulty probability under hardware transient faults. We present an algorithm that captures the faulty probabilities of DNN variables under transient faults and, thus, provides correct RA estimations validated by hardware. To accelerate RA estimation, we reformulate RA calculation as a Monte Carlo integration problem, and solve it using importance sampling driven by DNN specific heuristics. Using our lightweight RA estimation method, we show that transient faults lead to far greater accuracy degradation than what todays DNN resiliency tools estimate. We show how our RA estimation tool can help design more resilient DNNs by integrating it with a Network Architecture Search framework.

We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets. Feature duplication arises because DLRM datasets are generated from interactions. While each user session can generate multiple training samples, many features' values do not change across these samples. We demonstrate how RecD exploits this property, end-to-end, across a deployed training pipeline. RecD optimizes data generation pipelines to decrease dataset storage and preprocessing resource demands and to maximize duplication within a training batch. RecD introduces a new tensor format, InverseKeyedJaggedTensors (IKJTs), to deduplicate feature values in each batch. We show how DLRM model architectures can leverage IKJTs to drastically increase training throughput. RecD improves the training and preprocessing throughput and storage efficiency by up to 2.49x, 1.79x, and 3.71x, respectively, in an industry-scale DLRM training system.

本文研究了与可解释的AI（XAI）实践有关的两个不同但相关的问题。机器学习（ML）在金融服务中越来越重要，例如预批准，信用承销，投资以及各种前端和后端活动。机器学习可以自动检测培训数据中的非线性和相互作用，从而促进更快，更准确的信用决策。但是，机器学习模型是不透明的，难以解释，这是建立可靠技术所需的关键要素。该研究比较了各种机器学习模型，包括单个分类器（逻辑回归，决策树，LDA，QDA），异质集合（Adaboost，随机森林）和顺序神经网络。结果表明，整体分类器和神经网络的表现优于表现。此外，使用基于美国P2P贷款平台Lending Club提供的开放式访问数据集评估了两种先进的事后不可解释能力 - 石灰和外形来评估基于ML的信用评分模型。对于这项研究，我们还使用机器学习算法来开发新的投资模型，并探索可以最大化盈利能力同时最大程度地降低风险的投资组合策略。

执法和城市安全受到监视系统中的暴力事件的严重影响。尽管现代（智能）相机广泛可用且负担得起，但在大多数情况下，这种技术解决方案无能为力。此外，监测CCTV记录的人员经常显示出迟来的反应，从而导致对人和财产的灾难。因此，对迅速行动的暴力自动检测至关重要。拟议的解决方案使用了一种新颖的端到端深度学习视频视觉变压器（Vivit），可以在视频序列中熟练地辨别战斗，敌对运动和暴力事件。该研究提出了利用数据增强策略来克服较弱的电感偏见的缺点，同时在较小的培训数据集中训练视觉变压器。评估的结果随后可以发送给当地有关当局，可以分析捕获的视频。与最先进的（SOTA）相比，所提出的方法在某些具有挑战性的基准数据集上实现了吉祥的性能。