Neural Collaborative Ranking

Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in. With the unprecedented success of deep learning in computer vision and speech recognition, recently it has been a hot topic to bridge the gap between recommender systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and items into some shared low-dimensional latent feature space, and then employs neural nets to model the interaction between the user and item latent features to obtain state-of-the-art performance on the recommendation tasks. NeuMF assumes that the non-interacted items are inherent negative and uses negative sampling to relax this assumption. In this paper, we examine an alternative approach which does not assume that the non-interacted items are necessarily negative, just that they are less preferred than interacted items. Specifically, we develop a new classification strategy based on the widely used pairwise ranking assumption. We combine our classification strategy with the recently proposed neural collaborative filtering framework, and propose a general collaborative ranking framework called Neural Network based Collaborative Ranking (NCR). We resort to a neural network architecture to model a user's pairwise preference between items, with the belief that neural network will effectively capture the latent structure of latent factors. The experimental results on two real-world datasets show the superior performance of our models in comparison with several state-of-the-art approaches.Comment: Proceedings of the 2018 ACM on Conference on Information and Knowledge Managemen

b→cτνb\to c\tau\nu Transitions in the Standard Model Effective Field Theory

The $R(D^{(\ast)})$ anomalies observed in $B\to D^{(\ast)}\tau\nu$ decays have attracted much attention in recent years. In this paper, we study the $B\to D^{(\ast)}\tau\nu$, $\Lambda_b\to\Lambda_c\tau\nu$, $B_c\to (J/\psi,\,\eta_c)\tau\nu$, $B\to X_c\tau\nu$, and $B_c\to\tau\nu$ decays, all being mediated by the same quark-level $b\to c\tau\nu$ transition, in the Standard Model Effective Field Theory. The most relevant dimension-six operators for these processes are $Q_{lq}^{(3)}$, $Q_{ledq}$, $Q^{(1)}_{lequ}$, and $Q^{(3)}_{lequ}$ in the Warsaw basis. Evolution of the corresponding Wilson coefficients from the new physics scale $\Lambda=1$~TeV down to the characteristic scale $\mu_b\simeq m_b$ is performed at three-loop in QCD and one-loop in EW/QED. It is found that, after taking into account the constraint ${\cal B}(B_c\to\tau\nu)\lesssim 10\%$, a single $\left[C_{lq}^{(3)}\right]_{3323}(\Lambda)$ or $\left[C^{(3)}_{lequ}\right]_{3332}(\Lambda)$ can still be used to resolve the $R(D^{(\ast)})$ anomalies at $1\sigma$, while a single $\left[C^{(1)}_{lequ}\right]_{3332}(\Lambda)$ is already ruled out by the measured $R(D^{(\ast)})$ at more than $3\sigma$. By minimizing the $\chi^2(C_i)$ function constructed based on the current data on $R(D)$, $R(D^\ast)$, $P_\tau(D^\ast)$, $R(J/\psi)$, and $R(X_c)$, we obtain eleven most trustworthy scenarios, each of which can provide a good explanation of the $R(D^{(\ast)})$ anomalies at $1\sigma$. To further discriminate these different scenarios, we predict thirty-one observables associated with the processes considered under each NP scenario. It is found that most of the scenarios can be differentiated from each other by using these observables and their correlations.Comment: 43 pages, 3 figures and 5 tables; references updated and more discussions added, final version to be published in the journa

The distillable entanglement of multiple copies of Bell states

It is impossible to discriminate four Bell states through local operations and classical communication (LOCC), if only one copy is provided. To complete this task, two copies will suffice and be necessary. When $n$ copies are provided, we show that the distillable entanglement is exactly $n-2$.Comment: An argument in the original paper is replaced by a procedure of strict proo

Genetic incorporation of D-Lysine into diketoreductase in Escherichia coli cells

D-Lysine has been genetically introduced into diketoreductase in E. coli cells by utilization of an orthogonal Ph tRNA /Lysyl-tRNA synthetase pair. This is the first report on the genetic incoporation of D-amino acids into proteins, which may be generally applicable to a wide variety of applications

Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation

Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations (including shapes and intensities) of different unlabeled MRI images with respect to the atlas via 3D variational autoencoders (VAEs). In this manner, our method is able to exploit the learned distributions of image deformations to generate new authentic brain MRI images, and the number of generated samples will be sufficient to train a deep segmentation network. Furthermore, we introduce a new standard segmentation benchmark to evaluate the generalization performance of a segmentation network through a cross-dataset setting (collected from different sources). Extensive experiments demonstrate that our method outperforms the state-of-the-art one-shot medical segmentation methods. Our code has been released at https://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data.Comment: AAAI 202