Maximizing oil production from gas-lifted oil wells entails solving
Mixed-Integer Linear Programs (MILPs). As the parameters of the wells, such as
the basic-sediment-to-water ratio and the gas-oil ratio, are updated, the
problems must be repeatedly solved. Instead of relying on costly exact methods
or the accuracy of general approximate methods, in this paper, we propose a
tailor-made heuristic solution based on deep learning models trained to provide
values to all integer variables given varying well parameters, early-fixing the
integer variables and, thus, reducing the original problem to a linear program
(LP). We propose two approaches for developing the learning-based heuristic: a
supervised learning approach, which requires the optimal integer values for
several instances of the original problem in the training set, and a
weakly-supervised learning approach, which requires only solutions for the
early-fixed linear problems with random assignments for the integer variables.
Our results show a runtime reduction of 71.11% Furthermore, the
weakly-supervised learning model provided significant values for early fixing,
despite never seeing the optimal values during training.

通过深度学习模型训练并提供整数变量的值，我们提出了一种基于深度学习模型的量身定制启发式解决方案，将原始问题转化为线性规划问题，进而减少了问题求解时间，减少了运行时间约为 71.11%。尽管在训练期间未看到最优解值，但是弱监督学习模型在早期修复方面提供了显著的数值。

基于深度学习的气举采油优化的早期修复：监督和弱监督方法

Deep-learning-based Early Fixing for Gas-lifted Oil Production  Optimization: Supervised and Weakly-supervised Approaches

Remote heart rate estimation is the measurement of heart rate without any
physical contact with the subject and is accomplished using remote
photoplethysmography (rPPG) in this work. rPPG signals are usually collected
using a video camera with a limitation of being sensitive to multiple
contributing factors, e.g. variation in skin tone, lighting condition and
facial structure. End-to-end supervised learning approach performs well when
training data is abundant, covering a distribution that doesn't deviate too
much from the distribution of testing data or during deployment. To cope with
the unforeseeable distributional changes during deployment, we propose a
transductive meta-learner that takes unlabeled samples during testing
(deployment) for a self-supervised weight adjustment (also known as
transductive inference), providing fast adaptation to the distributional
changes. Using this approach, we achieve state-of-the-art performance on
MAHNOB-HCI and UBFC-rPPG.

本文提出了一种基于远程光电容积脉搏图（rPPG）的无接触式心率估计方法，通过自监督权重调整来实现在测试部署期间对未标记样本的快速适应以应对未知分布变化的能力，从而取得了 MAHNOB-HCI 和 UBFC-rPPG 方面的最优性能。