How Supererogation Can Save Intrapersonal Permissivism

Rationality is intrapersonally permissive just in case there are multiple doxastic states that one agent may be rational in holding at a given time, given some body of evidence. One way for intrapersonal permissivism to be true is if there are epistemic supererogatory beliefs—beliefs that go beyond the call of epistemic duty. Despite this, there has been almost no discussion of epistemic supererogation in the permissivism literature. This paper shows that this is a mistake. It does this by arguing that the most popular ways of responding to one of the major obstacles to any intrapersonally permissive all fall prey to the same problem. This problem is most naturally solved by positing a category of epistemically supererogatory belief. So intrapersonal epistemic permissivists should embrace epistemic supererogation

Deterministic polarization-entanglement purification using spatial entanglement

We present an efficient entanglement purification protocol with hyperentanglement in which additional spatial entanglement is utilized to purify the two-particle polarization-entangled state. The bit-flip error and phase-flip error can be corrected and eliminated in one step. Two remote parties can obtainmaximally entangled polarization states deterministically and only passive linear optics are employed. We also discuss the protocol with practical quantum source and noisy channel.Comment: 4pages,1 figur

Bose-Fermi Mapping and Multi-Branch Spin Chain Model for Strongly Interacting Quantum Gases in One-Dimension: Dynamics and Collective Excitations

We show that the wave function of a one dimensional spinor gas with contact $s$-wave interaction, either bosonic or fermionic, can be mapped to the direct product of the wave function of a spinless Fermi gas with short-range $p$-wave interaction and that of a spin system governed by spin parity projection operators. Applying this mapping to strongly interacting spinor gases, we obtain a generalized spin chain model that captures both the static and dynamics properties of the system. Using this spin chain model, we investigate the breathing mode frequency and the quench dynamics of strongly interacting harmonically trapped spinor gases.Comment: 5 pages of main text + 5 pages of Supplemental Materia

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