This brief addresses the design of a Nonlinear Model Predictive Control
(NMPC) strategy for exponentially incremental Input-to-State Stable (ISS)
systems. In particular, a novel formulation is devised, which does not
necessitate the onerous computation of terminal ingredients, but rather relies
on the explicit definition of a minimum prediction horizon ensuring closed-loop
stability. The designed methodology is particularly suited for the control of
systems learned by Recurrent Neural Networks (RNNs), which are known for their
enhanced modeling capabilities and for which the incremental ISS properties can
be studied thanks to simple algebraic conditions. The approach is applied to
Gated Recurrent Unit (GRU) networks, providing also a method for the design of
a tailored state observer with convergence guarantees. The resulting control
architecture is tested on a benchmark system, demonstrating its good control
performances and efficient applicability.

设计了一种新颖的非线性模型预测控制策略，适用于具有增量输入输出稳定性的系统，特别适用于递归神经网络学习的系统，并应用于门控循环单元网络的控制架构，通过基于简单的代数条件实现闭环稳定和状态观察器设计。

增量 ISS 系统的非线性 MPC 设计及应用于 GRU 网络

Nonlinear MPC design for incrementally ISS systems with application to  GRU networks

Spatio-temporal kriging is an important problem in web and social
applications, such as Web or Internet of Things, where things (e.g., sensors)
connected into a web often come with spatial and temporal properties. It aims
to infer knowledge for (the things at) unobserved locations using the data from
(the things at) observed locations during a given time period of interest. This
problem essentially requires \emph{inductive learning}. Once trained, the model
should be able to perform kriging for different locations including newly given
ones, without retraining. However, it is challenging to perform accurate
kriging results because of the heterogeneous spatial relations and diverse
temporal patterns. In this paper, we propose a novel inductive graph
representation learning model for spatio-temporal kriging. We first encode
heterogeneous spatial relations between the unobserved and observed locations
by their spatial proximity, functional similarity, and transition probability.
Based on each relation, we accurately aggregate the information of most
correlated observed locations to produce inductive representations for the
unobserved locations, by jointly modeling their similarities and differences.
Then, we design relation-aware gated recurrent unit (GRU) networks to
adaptively capture the temporal correlations in the generated sequence
representations for each relation. Finally, we propose a multi-relation
attention mechanism to dynamically fuse the complex spatio-temporal information
at different time steps from multiple relations to compute the kriging output.
Experimental results on three real-world datasets show that our proposed model
outperforms state-of-the-art methods consistently, and the advantage is more
significant when there are fewer observed locations. Our code is available at
this https URL.

本文提出了一种基于图表示学习的时空克里金模型，通过编码不同观察位置间的空间相似性、功能相似性和转移概率，结合门控循环神经网络和多种关系注意机制，从多个时间步骤中动态融合不同关系的时空信息，成功解决了基于异质空间关系的时空克里金问题。

INCREASE: 基于感应的空间时间克里金图表示学习

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

We propose Imaginet, a model of learning visually grounded representations of
language from coupled textual and visual input. The model consists of two Gated
Recurrent Unit networks with shared word embeddings, and uses a multi-task
objective by receiving a textual description of a scene and trying to
concurrently predict its visual representation and the next word in the
sentence. Mimicking an important aspect of human language learning, it acquires
meaning representations for individual words from descriptions of visual
scenes. Moreover, it learns to effectively use sequential structure in semantic
interpretation of multi-word phrases.

该研究提出了 Imaginet 模型，该模型可以从耦合文本和视觉输入中学习语言表达的视觉基础表示，采用多任务目标，并从视觉场景描述中获取单词的含义表达，并学习有效地利用语义解释多词短语的顺序结构。