Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train models with differential privacy, which provides rigorous privacy guarantees by injecting random noise to the gradients. This paper focuses on the scenario where sensitive data are distributed among multiple participants, who jointly train a model through federated learning (FL), using both secure multiparty computation (MPC) to ensure the confidentiality of each gradient update, and differential privacy to avoid data leakage in the resulting model. A major challenge in this setting is that common mechanisms for enforcing DP in deep learning, which inject real-valued noise, are fundamentally incompatible with MPC, which exchanges finite-field integers among the participants. Consequently, most existing DP mechanisms require rather high noise levels, leading to poor model utility. Motivated by this, we propose Skellam mixture mechanism (SMM), an approach to enforce DP on models built via FL. Compared to existing methods, SMM eliminates the assumption that the input gradients must be integer-valued, and, thus, reduces the amount of noise injected to preserve DP. Further, SMM allows tight privacy accounting due to the nice composition and sub-sampling properties of the Skellam distribution, which are key to accurate deep learning with DP. The theoretical analysis of SMM is highly non-trivial, especially considering (i) the complicated math of differentially private deep learning in general and (ii) the fact that the mixture of two Skellam distributions is rather complex, and to our knowledge, has not been studied in the DP literature. Extensive experiments on various practical settings demonstrate that SMM consistently and significantly outperforms existing solutions in terms of the utility of the resulting model.

In the Metaverse, the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, efficient storage and data management and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.

Instahide是一种用于保护私人训练图像的最先进的机制，通过混合多个私人图像并修改它们，使得它们的视觉功能与肉眼无法区分。然而，最近的工作，Carlini等人。表明可以从Instahide生成的加密数据集重建私人图像。尽管如此，我们证明了Carlini等人。通过将数据增强纳入Instahide，可以轻松地击败攻击。这导致了自然问题：Instahide是否具有数据增强安全？在本文中，我们通过设计攻击即使在存在数据增强时，我们也通过设计用于从Instahide的输出中恢复私人图像的攻击来提供否定答案。基本思想是使用比较网络来识别可能对应于相同的私人图像的加密图像，然后采用融合去噪网络，用于从加密的私人图像恢复私人图像，考虑到数据增强的影响。广泛的实验表明，与Carlini等人相比，拟议的攻击的有效性。的攻击。

数值天气预报（NWP）和机器学习（ML）方法对于太阳能预测是流行的。但是，NWP模型具有多种可能的物理参数化，其需要特定于站点的NWP优化。当区域NWP模型与具有不同可能的参数化的全球气候模型一起使用时，这进一步复杂化。在该研究中，提出了一种替代方法，并评估了四种辐射模型。天气研究和预测（WRF）模型在全球和区域模式中运行，以提供太阳能辐照度的估计。然后使用ML后处理该估计以提供最终预测。该ML误差校正模型，来自WRF的归一化根均方误差高达40-50％。使用CAM，GFDL，新戈达德和RRTMG辐射模型获得的结果在此校正后可比，否定了WRF参数化调整的需求。还评估了包含附近地点和传感器数据的其他模型，后者是特别有前途的。

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Robust Markov decision processes (RMDPs) are promising models that provide reliable policies under ambiguities in model parameters. As opposed to nominal Markov decision processes (MDPs), however, the state-of-the-art solution methods for RMDPs are limited to value-based methods, such as value iteration and policy iteration. This paper proposes Double-Loop Robust Policy Gradient (DRPG), the first generic policy gradient method for RMDPs with a global convergence guarantee in tabular problems. Unlike value-based methods, DRPG does not rely on dynamic programming techniques. In particular, the inner-loop robust policy evaluation problem is solved via projected gradient descent. Finally, our experimental results demonstrate the performance of our algorithm and verify our theoretical guarantees.

This paper introduces the use of evolutionary algorithms for solving differential equations. The solution is obtained by optimizing a deep neural network whose loss function is defined by the residual terms from the differential equations. Recent studies have used stochastic gradient descent (SGD) variants to train these physics-informed neural networks (PINNs), but these methods can struggle to find accurate solutions due to optimization challenges. When solving differential equations, it is important to find the globally optimum parameters of the network, rather than just finding a solution that works well during training. SGD only searches along a single gradient direction, so it may not be the best approach for training PINNs with their accompanying complex optimization landscapes. In contrast, evolutionary algorithms perform a parallel exploration of different solutions in order to avoid getting stuck in local optima and can potentially find more accurate solutions. However, evolutionary algorithms can be slow, which can make them difficult to use in practice. To address this, we provide a set of five benchmark problems with associated performance metrics and baseline results to support the development of evolutionary algorithms for enhanced PINN training. As a baseline, we evaluate the performance and speed of using the widely adopted Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for solving PINNs. We provide the loss and training time for CMA-ES run on TensorFlow, and CMA-ES and SGD run on JAX (with GPU acceleration) for the five benchmark problems. Our results show that JAX-accelerated evolutionary algorithms, particularly CMA-ES, can be a useful approach for solving differential equations. We hope that our work will support the exploration and development of alternative optimization algorithms for the complex task of optimizing PINNs.

In recent years, deep-learning-based approaches have been introduced to solving time-series forecasting-related problems. These novel methods have demonstrated impressive performance in univariate and low-dimensional multivariate time-series forecasting tasks. However, when these novel methods are used to handle high-dimensional multivariate forecasting problems, their performance is highly restricted by a practical training time and a reasonable GPU memory configuration. In this paper, inspired by a change of basis in the Hilbert space, we propose a flexible data feature extraction technique that excels in high-dimensional multivariate forecasting tasks. Our approach was originally developed for the National Science Foundation (NSF) Algorithms for Threat Detection (ATD) 2022 Challenge. Implemented using the attention mechanism and Convolutional Neural Networks (CNN) architecture, our method demonstrates great performance and compatibility. Our models trained on the GDELT Dataset finished 1st and 2nd places in the ATD sprint series and hold promise for other datasets for time series forecasting.

持久图（PDS）通常以同源性类别的死亡和出生为特征，以提供图形结构的拓扑表示，通常在机器学习任务中有用。先前的作品依靠单个图形签名来构建PD。在本文中，我们探讨了多尺度图标志家族的使用，以增强拓扑特征的鲁棒性。我们提出了一个深度学习体系结构来处理该集合的输入。基准图分类数据集上的实验表明，与使用图神经网络的最新方法相比，我们所提出的架构优于其他基于同源的方法，并实现其他基于同源的方法，并实现竞争性能。此外，我们的方法可以轻松地应用于大尺寸的输入图，因为它不会遭受有限的可伸缩性，这对于图内核方法可能是一个问题。

事件传感是生物启发的飞行指导和控制系统中的主要组成部分。我们探讨了事件摄像机在腹侧着陆期间与表面进行时间接触（TTC）的用法。这是通过估计差异（逆TTC）的差异来实现的，即径向光流的速率，是从着陆期间产生的事件流。我们的核心贡献是针对基于事件的差异估计的一种新颖的对比度最大化公式，以及一种分支和结合算法，可准确地最大化对比度并找到最佳的差异值。进行GPU加速度以加快全球算法。另一个贡献是一个新的数据集，其中包含来自腹面着陆的真实事件流，该数据集用于测试和基准我们的方法。由于全局优化，与其他启发式差异估计器或基于事件的光流方法相比，我们的算法更有能力恢复真正的分歧。随着GPU加速，我们的方法还可以实现竞争性的运行时间。