Learning Tree-based Deep Model for Recommender Systems

Model-based methods for recommender systems have been studied extensively in recent years. In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult. To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., model user-item preference as the inner product of user, item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor searches. However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, because of the calculation cost. In this paper, we focus on the problem of introducing arbitrary advanced models to recommender systems with large corpus. We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. Our main idea is to predict user interests from coarse to fine by traversing tree nodes in a top-down fashion and making decisions for each user-node pair. We also show that the tree structure can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Experimental evaluations with two large-scale real-world datasets show that the proposed method significantly outperforms traditional methods. Online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201

Wind-induced vibration control of power transmission tower using pounding tuned mass damper

Wind-induced vibration control of power transmission tower using pounding tuned mass damper is studied in the paper. The power transmission tower, which is often a high and flexible structure, is very susceptible to wind-induced vibrations. To reduce the wind-induced vibration of a transmission tower, a new type of vibration control device that pounding tuned mass damper (PTMD) is proposed in the PTMD, a limiting collar with viscoelastic material laced on the inner rim is installed to restrict the stroke of the tuned mass and to dissipate energy through collision. The pounding force is modeled based on the Hertz contact law. A 55 m tower is selected to verify the effectiveness of the PTMD. The wind field is generated based on Kaimal spectrum using harmonic superposition method. The power transmission towers without control and with the PTMD are analyzed, respectively. Results show that the PTMD is very effective in reducing the wind-induced vibration and the vibration control performance improves as the external wind load increases

Heteroepitaxial growth of ZnO branches selectively on TiO2 nanorod tips with improved light harvesting performance

A seeded heteroepitaxial growth of ZnO nanorods selectively on TiO2 nanorod tips was achieved by restricting crystal growth on highly hydrophobic TiO2 nanorod film surfaces. Intriguing light harvesting performance and efficient charge transport efficiency has been found, which suggest potential applications in photovoltaics and optoelectronics

Integrable deformations of the Bogoyavlenskij-Itoh Lotka-Volterra systems

We construct a family of integrable deformations of the Bogoyavlenskij-Itoh systems and construct a Lax operator with spectral parameter for it. Our approach is based on the construction of a family of compatible Poisson structures for the undeformed system, whose Casimirs are shown to yield a generating function for the integrals in involution of the deformed systems. We show how these deformations are related to the Veselov-Shabat systems.Comment: 23 pages, 14 reference

Offline Experience Replay for Continual Offline Reinforcement Learning

The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline tasks likely leads to the catastrophic forgetting issue under resource-limited scenarios. In this paper, we formulate a new setting, continual offline reinforcement learning (CORL), where an agent learns a sequence of offline reinforcement learning tasks and pursues good performance on all learned tasks with a small replay buffer without exploring any of the environments of all the sequential tasks. For consistently learning on all sequential tasks, an agent requires acquiring new knowledge and meanwhile preserving old knowledge in an offline manner. To this end, we introduced continual learning algorithms and experimentally found experience replay (ER) to be the most suitable algorithm for the CORL problem. However, we observe that introducing ER into CORL encounters a new distribution shift problem: the mismatch between the experiences in the replay buffer and trajectories from the learned policy. To address such an issue, we propose a new model-based experience selection (MBES) scheme to build the replay buffer, where a transition model is learned to approximate the state distribution. This model is used to bridge the distribution bias between the replay buffer and the learned model by filtering the data from offline data that most closely resembles the learned model for storage. Moreover, in order to enhance the ability on learning new tasks, we retrofit the experience replay method with a new dual behavior cloning (DBC) architecture to avoid the disturbance of behavior-cloning loss on the Q-learning process. In general, we call our algorithm offline experience replay (OER). Extensive experiments demonstrate that our OER method outperforms SOTA baselines in widely-used Mujoco environments.Comment: 9 pages, 4 figure

De-biasing interferometric visibilities in VLTI-AMBER data of low SNR observations

AIMS: We have found that the interferometric visibilities of VLTI-AMBER observations, extracted via the standard reduction package, are significantly biased when faint targets are concerned. The visibility biases derive from a time variable fringing effect (correlated noise) appearing on the detector. METHODS: We have developed a method to correct this bias that consists in a subtraction of the extra power due to such correlated noise, so that the real power spectrum at the spatial frequencies of the fringing artifact can be restored. RESULTS: This pre-processing procedure is implemented in a software, called AMDC and available to the community, to be run before the standard reduction package. Results obtained on simulated and real observations are presented and discussed.Comment: 7 pages, 9 figure