Depression has been the leading cause of mental-health illness worldwide.
Major depressive disorder (MDD), is a common mental health disorder that
affects both psychologically as well as physically which could lead to loss of
lives. Due to the lack of diagnostic tests and subjectivity involved in
detecting depression, there is a growing interest in using behavioural cues to
automate depression diagnosis and stage prediction. The absence of labelled
behavioural datasets for such problems and the huge amount of variations
possible in behaviour makes the problem more challenging. This paper presents a
novel multi-level attention based network for multi-modal depression prediction
that fuses features from audio, video and text modalities while learning the
intra and inter modality relevance. The multi-level attention reinforces
overall learning by selecting the most influential features within each
modality for the decision making. We perform exhaustive experimentation to
create different regression models for audio, video and text modalities.
Several fusions models with different configurations are constructed to
understand the impact of each feature and modality. We outperform the current
baseline by 17.52% in terms of root mean squared error.

本文介绍了一个新型的多级注意力网络用于多模式抑郁症预测，包括来自音频、视频和文本模式的特征融合，其在选择每种模式内最具影响力的特征进行决策时通过多级注意力加固了整体学习，实验中表现优于现有基线 17.52% 的均方根误差。

利用文本、音频和视频的多级注意力网络进行抑郁症预测

Multi-level Attention network using text, audio and video for Depression  Prediction

In many AI domains such as product configuration, a user should interactively
specify a solution that must satisfy a set of constraints. In such scenarios,
offline compilation of feasible solutions into a tractable representation is an
important approach to delivering efficient backtrack-free user interaction
online. In particular,binary decision diagrams (BDDs) have been successfully
used as a compilation target for product and service configuration. In this
paper we discuss how to extend BDD-based configuration to scenarios involving
cost functions which express user preferences.
We first show that an efficient, robust and easy to implement extension is
possible if the cost function is additive, and feasible solutions are
represented using multi-valued decision diagrams (MDDs). We also discuss the
effect on MDD size if the cost function is non-additive or if it is encoded
explicitly into MDD. We then discuss interactive configuration in the presence
of multiple cost functions. We prove that even in its simplest form,
multiple-cost configuration is NP-hard in the input MDD. However, for solving
two-cost configuration we develop a pseudo-polynomial scheme and a fully
polynomial approximation scheme. The applicability of our approach is
demonstrated through experiments over real-world configuration models and
product-catalogue datasets. Response times are generally within a fraction of a
second even for very large instances.

这篇论文讨论了如何将基于二进制决策图（BDD）的配置扩展到涉及表示用户偏好的成本函数的情景中，并证明即使在其最简单的形式下，多成本配置也是 NP - 难问题。