Explainable recommendation is far from being well solved partly due to three challenges. The first is the personalization of preference learning, which requires that different items/users have different contributions to the learning of user preference or item quality. The second one is dynamic explanation, which is crucial for the timeliness of recommendation explanations. The last one is the granularity of explanations. In practice, aspect-level explanations are more persuasive than item-level or user-level ones. In this paper, to address these challenges simultaneously, we propose a novel model called Hybrid Deep Embedding (HDE) for aspect-based explainable recommendations, which can make recommendations with dynamic aspect-level explanations. The main idea of HDE is to learn the dynamic embeddings of users and items for rating prediction and the dynamic latent aspect preference/quality vectors for the generation of aspect-level explanations, through fusion of the dynamic implicit feedbacks extracted from reviews and the attentive user-item interactions. Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item. The extensive experiments conducted on real datasets verify the recommending performance and explainability of HDE. The source code of our work is available at \url{https://github.com/lola63/HDE-Python}

In the task of Autonomous aerial filming of a moving actor (e.g. a person or a vehicle), it is crucial to have a good heading direction estimation for the actor from the visual input. However, the models obtained in other similar tasks, such as pedestrian collision risk analysis and human-robot interaction, are very difficult to generalize to the aerial filming task, because of the difference in data distributions. Towards improving generalization with less amount of labeled data, this paper presents a semi-supervised algorithm for heading direction estimation problem. We utilize temporal continuity as the unsupervised signal to regularize the model and achieve better generalization ability. This semi-supervised algorithm is applied to both training and testing phases, which increases the testing performance by a large margin. We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced. We also discuss several important details on improving the performance by balancing labeled and unlabeled loss, and making good combinations. Experimental results show that our approach robustly outputs the heading direction for different types of actor. The aesthetic value of the video is also improved in the aerial filming task.

Molecular dynamics (MD) simulations have long been an important tool for studying molecular systems by providing atomistic insight into physicochemical processes that cannot be easily obtained through experimentation. A key step in extracting kinetic information from molecular simulation is the recovery of the slow dynamical modes that govern the longtime evolution of system coordinates within a low-dimensional latent space. The variational approach to conformational dynamics (V AC) [1, 2] has been successful in providing a mathematical framework through which the eigenfunctions of the underlying transfer operator can be estimated [3, 4]. A special case of V AC which estimates linearly optimal slow modes from mean-free input coordinates is known as time-lagged independent component analysis (TICA) [1, 2, 4-9]. TICA is a widely used approach that has become a standard step in the Markov state modeling pipeline [10]. However, it is restricted to form linear combinations of the input coordinates and is unable to learn nonlinear transformations that are typically required to recover high resolution kinetic models of all but the simplest molecular systems. Schwantes et al. address this limitation by applying the kernel trick with TICA to learn nonlinear functions of the input coordinates [11]. A special case of a radial basis function kernels was realized by No e and Nuske in the direct application of V AC using Gaussian functions [1]. Kernel TICA (kTICA), however, suffers from a number of drawbacks that have precluded its broad adoption.