对手示例可以容易地降低神经网络中的分类性能。提出了促进这些例子的稳健性的实证方法，但往往缺乏分析见解和正式担保。最近，一些稳健性证书在文献中出现了基于系统理论概念的文献。这项工作提出了一种基于增量的耗散性的稳健性证书，用于每个层的线性矩阵不等式形式的神经网络。我们还提出了对该证书的等效光谱标准，该证书可扩展到具有多个层的神经网络。我们展示了对在MNIST培训的前馈神经网络上的对抗对抗攻击的性能和使用CIFAR-10训练的亚历纳特人。

并行系统中的通信施加了显着的开销，这往往是并联机器学习中的瓶颈。为了减轻其中一些开销，在本文中，我们提出了Eventgrad - 一种具有事件触发通信的算法，用于并行机器学习中的随机梯度下降。该算法的主要思想是在并行机器学习中的随机梯度下降的标准实现中修改通信的需求，仅在某些迭代时仅在必要时进行通信。我们为我们所提出的算法的融合提供了理论分析。我们还实现了用于训练CiFar-10数据集的流行残余神经网络的数据并行培训的提议算法，并显示Evervgrad可以将通信负载降低到60％，同时保持相同的精度水平。此外，Evervgrad可以与其他方法（例如Top-K稀疏）组合，以进一步降低通信，同时保持精度。

Matrix factorization exploits the idea that, in complex high-dimensional data, the actual signal typically lies in lower-dimensional structures. These lower dimensional objects provide useful insight, with interpretability favored by sparse structures. Sparsity, in addition, is beneficial in terms of regularization and, thus, to avoid over-fitting. By exploiting Bayesian shrinkage priors, we devise a computationally convenient approach for high-dimensional matrix factorization. The dependence between row and column entities is modeled by inducing flexible sparse patterns within factors. The availability of external information is accounted for in such a way that structures are allowed while not imposed. Inspired by boosting algorithms, we pair the the proposed approach with a numerical strategy relying on a sequential inclusion and estimation of low-rank contributions, with data-driven stopping rule. Practical advantages of the proposed approach are demonstrated by means of a simulation study and the analysis of soccer heatmaps obtained from new generation tracking data.

We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their own future latent representations. Despite its recent empirical success, such algorithms have an apparent defect: trivial representations (such as constants) minimize the prediction error, yet it is obviously undesirable to converge to such solutions. Our central insight is that careful designs of the optimization dynamics are critical to learning meaningful representations. We identify that a faster paced optimization of the predictor and semi-gradient updates on the representation, are crucial to preventing the representation collapse. Then in an idealized setup, we show self-predictive learning dynamics carries out spectral decomposition on the state transition matrix, effectively capturing information of the transition dynamics. Building on the theoretical insights, we propose bidirectional self-predictive learning, a novel self-predictive algorithm that learns two representations simultaneously. We examine the robustness of our theoretical insights with a number of small-scale experiments and showcase the promise of the novel representation learning algorithm with large-scale experiments.

One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Control-Tutored Deep Q-Networks (CT-DQN) algorithm, a Deep Reinforcement Learning algorithm that leverages a control tutor, i.e., an exogenous control law, to reduce learning time. The tutor can be designed using an approximate model of the system, without any assumption about the knowledge of the system's dynamics. There is no expectation that it will be able to achieve the control objective if used stand-alone. During learning, the tutor occasionally suggests an action, thus partially guiding exploration. We validate our approach on three scenarios from OpenAI Gym: the inverted pendulum, lunar lander, and car racing. We demonstrate that CT-DQN is able to achieve better or equivalent data efficiency with respect to the classic function approximation solutions.

Shape displays are a class of haptic devices that enable whole-hand haptic exploration of 3D surfaces. However, their scalability is limited by the mechanical complexity and high cost of traditional actuator arrays. In this paper, we propose using electroadhesive auxetic skins as a strain-limiting layer to create programmable shape change in a continuous ("formable crust") shape display. Auxetic skins are manufactured as flexible printed circuit boards with dielectric-laminated electrodes on each auxetic unit cell (AUC), using monolithic fabrication to lower cost and assembly time. By layering multiple sheets and applying a voltage between electrodes on subsequent layers, electroadhesion locks individual AUCs, achieving a maximum in-plane stiffness variation of 7.6x with a power consumption of 50 uW/AUC. We first characterize an individual AUC and compare results to a kinematic model. We then validate the ability of a 5x5 AUC array to actively modify its own axial and transverse stiffness. Finally, we demonstrate this array in a continuous shape display as a strain-limiting skin to programmatically modulate the shape output of an inflatable LDPE pouch. Integrating electroadhesion with auxetics enables new capabilities for scalable, low-profile, and low-power control of flexible robotic systems.

Persistent homology, a powerful mathematical tool for data analysis, summarizes the shape of data through tracking topological features across changes in different scales. Classical algorithms for persistent homology are often constrained by running times and memory requirements that grow exponentially on the number of data points. To surpass this problem, two quantum algorithms of persistent homology have been developed based on two different approaches. However, both of these quantum algorithms consider a data set in the form of a point cloud, which can be restrictive considering that many data sets come in the form of time series. In this paper, we alleviate this issue by establishing a quantum Takens's delay embedding algorithm, which turns a time series into a point cloud by considering a pertinent embedding into a higher dimensional space. Having this quantum transformation of time series to point clouds, then one may use a quantum persistent homology algorithm to extract the topological features from the point cloud associated with the original times series.

Only increasing accuracy without considering uncertainty may negatively impact Deep Neural Network (DNN) decision-making and decrease its reliability. This paper proposes five combined preprocessing and post-processing methods for time-series binary classification problems that simultaneously increase the accuracy and reliability of DNN outputs applied in a 5G UAV security dataset. These techniques use DNN outputs as input parameters and process them in different ways. Two methods use a well-known Machine Learning (ML) algorithm as a complement, and the other three use only confidence values that the DNN estimates. We compare seven different metrics, such as the Expected Calibration Error (ECE), Maximum Calibration Error (MCE), Mean Confidence (MC), Mean Accuracy (MA), Normalized Negative Log Likelihood (NLL), Brier Score Loss (BSL), and Reliability Score (RS) and the tradeoffs between them to evaluate the proposed hybrid algorithms. First, we show that the eXtreme Gradient Boosting (XGB) classifier might not be reliable for binary classification under the conditions this work presents. Second, we demonstrate that at least one of the potential methods can achieve better results than the classification in the DNN softmax layer. Finally, we show that the prospective methods may improve accuracy and reliability with better uncertainty calibration based on the assumption that the RS determines the difference between MC and MA metrics, and this difference should be zero to increase reliability. For example, Method 3 presents the best RS of 0.65 even when compared to the XGB classifier, which achieves RS of 7.22.

人工神经网络的扩展不断增加，在超功率边缘设备上不会停止。但是，这些通常具有很高的计算需求，并且需要专门的硬件加速器，以确保设计达到功率和性能限制。神经网络的手动优化以及相应的硬件加速器可能非常具有挑战性。本文介绍了Hannah（硬件加速器和神经网络搜索），该框架是针对深神经网络和硬件加速器的自动化和组合的硬件/软件共同设计，用于资源和功率受限的边缘设备。优化方法使用基于进化的搜索算法，一种神经网络模板技术以及可配置的Ultratrail硬件加速器模板的分析KPI模型，以找到优化的神经网络和加速器配置。我们证明，汉娜（Hannah）可以找到适合不同音频分类任务的功耗和高精度的合适神经网络，例如单级唤醒单词检测，多级关键字检测和语音活动检测，这些操作优于相关工作。

在这项工作中，我们考虑了限制在可渗透球体内部的氢原子。限制电位由深度$ \ omega_0 $，width $ \ sigma $的倒哥斯函数描述，并以$ r_c $为中心。特别是，该模型已用于研究$ C_ {60} $ Fullerene中的原子。对于角动量的最低值$ l = 0,1,2 $，系统的光谱是参数的函数（$ \ omega_0，\ sigma，r_c $）是使用三种不同的数值方法计算的：（i） Lagrange-mesh方法，（ii）第四阶有限差和（iii）有限元方法。显示了不少于11个重要数字的混凝土结果。同样，在Lagrange-Mesh方法中，分别提出了相应的本征函数和前六个州的$ r $的期望值，分别介绍了$ s，p $和$ d $ symmetries。我们的准确能量也被视为初始数据，以训练人工神经网络。它产生有效的数值插值。目前的数值结果改善并扩展了文献中报告的结果。