This paper presents a novel generative probabilistic forecasting approach
derived from the Wiener-Kallianpur innovation representation of nonparametric
time series. Under the paradigm of generative artificial intelligence, the
proposed forecasting architecture includes an autoencoder that transforms
nonparametric multivariate random processes into canonical innovation
sequences, from which future time series samples are generated according to
their probability distributions conditioned on past samples. A novel
deep-learning algorithm is proposed that constrains the latent process to be an
independent and identically distributed sequence with matching autoencoder
input-output conditional probability distributions. Asymptotic optimality and
structural convergence properties of the proposed generative forecasting
approach are established. Three applications involving highly dynamic and
volatile time series in real-time market operations are considered: (i)
locational marginal price forecasting for merchant storage participants, {(ii)
interregional price spread forecasting for interchange markets,} and (iii) area
control error forecasting for frequency regulations. Numerical studies based on
market data from multiple independent system operators demonstrate superior
performance against leading traditional and machine learning-based forecasting
techniques under both probabilistic and point forecast metrics.

本文提出了一种新颖的生成式概率预测方法，基于非参数时间序列的 Wiener-Kallianpur 创新表示。在生成人工智能的范式下，提出的预测架构包括一个自编码器，将非参数多变量随机过程转化为规范化的创新序列，根据过去样本的条件概率分布生成未来时间序列样本。提出了一种新颖的深度学习算法，使潜在过程成为具有匹配自编码器输入 - 输出条件概率分布的独立同分布序列。该生成式预测方法具有渐近最优性和结构收敛性质。研究了三个应用领域，包括实时市场操作中的高动态和高波动时间序列：(i) 商业储能参与者的位置边际价格预测，(ii) 互联市场的区域价格差预测，以及 (iii) 频率调节的区域控制误差预测。基于多个独立系统运营商的市场数据的数值研究表明，在概率和点预测指标下，与传统的和基于机器学习的预测技术相比，该方法具有更优越的性能。

市场运营中的生成概率预测应用

Generative Probabilistic Forecasting with Applications in Market  Operations

Despite recent advances in data-independent and deep-learning algorithms,
unstained live adherent cell instance segmentation remains a long-standing
challenge in cell image processing. Adherent cells' inherent visual
characteristics, such as low contrast structures, fading edges, and irregular
morphology, have made it difficult to distinguish from one another, even by
human experts, let alone computational methods. In this study, we developed a
novel deep-learning algorithm called dual-view selective instance segmentation
network (DVSISN) for segmenting unstained adherent cells in differential
interference contrast (DIC) images. First, we used a dual-view segmentation
(DVS) method with pairs of original and rotated images to predict the bounding
box and its corresponding mask for each cell instance. Second, we used a mask
selection (MS) method to filter the cell instances predicted by the DVS to keep
masks closest to the ground truth only. The developed algorithm was trained and
validated on our dataset containing 520 images and 12198 cells. Experimental
results demonstrate that our algorithm achieves an AP_segm of 0.555, which
remarkably overtakes a benchmark by a margin of 23.6%. This study's success
opens up a new possibility of using rotated images as input for better
prediction in cell images.

本研究提出了一种名为 DVSISN 的深度学习算法，利用差分干涉对比图像 (DIC) 中的旋转图像对，通过预测每个细胞实例的边界框及其相应的掩膜，并使用掩膜选择过滤，以更好地预测细胞图像。实验结果表明，这种方法可以显著提高细胞实例分割的准确性。

差分干涉对比成像中无染色活态贴壁细胞的双视图选择性实例分割网络

Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images

The existence of adversarial data examples has drawn significant attention in
the deep-learning community; such data are seemingly minimally perturbed
relative to the original data, but lead to very different outputs from a
deep-learning algorithm. Although a significant body of work on developing
defensive models has been considered, most such models are heuristic and are
often vulnerable to adaptive attacks. Defensive methods that provide
theoretical robustness guarantees have been studied intensively, yet most fail
to obtain non-trivial robustness when a large-scale model and data are present.
To address these limitations, we introduce a framework that is scalable and
provides certified bounds on the norm of the input manipulation for
constructing adversarial examples. We establish a connection between robustness
against adversarial perturbation and additive random noise, and propose a
training strategy that can significantly improve the certified bounds. Our
evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is
scalable to complicated models and large data sets, while providing competitive
robustness to state-of-the-art provable defense methods.

本文提出一种可扩展的用于构造对抗样本的方法，通过建立抵抗对抗扰动和加法噪声之间的联系，提出了一种训练策略，并在 MNIST 等数据集上进行了评估。