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

Magneto-elastic coupling model of deformable anisotropic superconductors

We develop a magneto-elastic (ME) coupling model for the interaction between the vortex lattice and crystal elasticity. The theory extends the Kogan-Clem's anisotropic Ginzburg-Landau (GL) model to include the elasticity effect. The anisotropies in superconductivity and elasticity are simultaneously considered in the GL theory frame. We compare the field and angular dependences of the magnetization to the relevant experiments. The contribution of the ME interaction to the magnetization is comparable to the vortex-lattice energy, in materials with relatively strong pressure dependence of the critical temperature. The theory can give the appropriate slope of the field dependence of magnetization near the upper critical field. The magnetization ratio along different vortex frame axes is independent with the ME interaction. The theoretical description of the magnetization ratio is applicable only if the applied field moderately close to the upper critical field

Experiments and Models of Thermo-Induced Shape Memory Polymers

Recent advances in experiments and models of thermo-induced shape memory polymers (TSMPs) were reviewed. Some important visco-elastic and visco-plastic features, such as rate-dependent and temperature-dependent stress-strain curves and nonuniform temperature distribution were experimentally investigated, and the interaction between the mechanical deformation and the internal heat generation was discussed. The influences of loading rate and peak strain on the shape memory effect (SME) and shape memory degeneration of TSMPs were revealed under monotonic and cyclic thermo-mechanical loadings, respectively. Based on experimental observations, the capability of recent developed visco-elastic and visco-plastic models for predicting the SME was evaluated, and the thermo-mechanically coupled models were used to reasonably predict the thermo-mechanical responses of TSMPs

Normalization Enhances Generalization in Visual Reinforcement Learning

Recent advances in visual reinforcement learning (RL) have led to impressive success in handling complex tasks. However, these methods have demonstrated limited generalization capability to visual disturbances, which poses a significant challenge for their real-world application and adaptability. Though normalization techniques have demonstrated huge success in supervised and unsupervised learning, their applications in visual RL are still scarce. In this paper, we explore the potential benefits of integrating normalization into visual RL methods with respect to generalization performance. We find that, perhaps surprisingly, incorporating suitable normalization techniques is sufficient to enhance the generalization capabilities, without any additional special design. We utilize the combination of two normalization techniques, CrossNorm and SelfNorm, for generalizable visual RL. Extensive experiments are conducted on DMControl Generalization Benchmark and CARLA to validate the effectiveness of our method. We show that our method significantly improves generalization capability while only marginally affecting sample efficiency. In particular, when integrated with DrQ-v2, our method enhances the test performance of DrQ-v2 on CARLA across various scenarios, from 14% of the training performance to 97%

Neural computation of visual imaging based on Kronecker product in the primary visual cortex

Background: What kind of neural computation is actually performed by the primary visual cortex and how is this represented mathematically at the system level? It is an important problem in the visual information processing, but has not been well answered. In this paper, according to our understanding of retinal organization and parallel multi-channel topographical mapping between retina and primary visual cortex V1, we divide an image into orthogonal and orderly array of image primitives (or patches), in which each patch will evoke activities of simple cells in V1. From viewpoint of information processing, this activated process, essentially, involves optimal detection and optimal matching of receptive fields of simple cells with features contained in image patches. For the reconstruction of the visual image in the visual cortex V1 based on the principle of minimum mean squares error, it is natural to use the inner product expression in neural computation, which then is transformed into matrix form. Results: The inner product is carried out by using Kronecker product between patches and function architecture (or functional column) in localized and oriented neural computing. Compared with Fourier Transform, the mathematical description of Kronecker product is simple and intuitive, so is the algorithm more suitable for neural computation of visual cortex V1. Results of computer simulation based on two-dimensional Gabor pyramid wavelets show that the theoretical analysis and the proposed model are reasonable. Conclusions: Our results are: 1. The neural computation of the retinal image in cortex V1 can be expressed to Kronecker product operation and its matrix form, this algorithm is implemented by the inner operation between retinal image primitives and primary visual cortex's column. It has simple, efficient and robust features, which is, therefore, such a neural algorithm, which can be completed by biological vision. 2. It is more suitable that the function of cortical column in cortex V1 is considered as the basic unit of visual image processing (such unit can implement basic multiplication of visual primitives, such as contour, line, and edge), rather than a set of tiled array filter. Fourier Transformation is replaced with Kronecker product, which greatly reduces the computational complexity. The neurobiological basis of this idea is that a visual image can be represented as a linear combination of orderly orthogonal primitive image containing some local feature. In the visual pathway, the image patches are topographically mapped onto cortex V1 through parallel multi-channels and then are processed independently by functional columns. Clearly, the above new perspective has some reference significance to exploring the neural mechanisms on the human visual information processing.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000277524600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701NeurosciencesSCI(E)0ARTICLEnull1

A Comparative Study on Damage Mechanism of Sandwich Structures with Different Core Materials under Lightning Strikes

Wind turbine blades are easily struck by lightning, a phenomenon that has attracted more and more attention in recent years. On this subject a large current experiment was conducted on three typical blade sandwich structures to simulate the natural lightning-induced arc effects. The resulting damage to different composite materials has been compared: polyvinyl chloride (PVC) and polyethylene terephthalate (PET) suffered pyrolysis and cracks inside, while the damage to balsa wood was fibers breaking off and large delamination between it and the resin layer, and only a little chemical pyrolysis. To analyze the damage mechanism on sandwich structures of different materials, a finite element method (FEM) model to calculate the temperature and pressure distribution was built, taking into consideration heat transfer and flow expansion due to impulse currents. According to the simulation results, PVC had the most severe temperature and pressure distribution, while PET and balsa wood were in the better condition after the experiments. The temperature distribution results explained clearly why balsa wood suffered much less chemical pyrolysis than PVC. Since balsa wood had better thermal stability than PET, the pyrolysis area of PET was obviously larger than that of balsa wood too. Increasing the volume fraction of solid components of porous materials can efficiently decrease the heat transfer velocity in porous materials. Permeability didn’t influence that much. The findings provide support for optimum material selection and design in blade manufacturing