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

Influence of rivet to sheet edge distance on fatigue strength of self-piercing riveted aluminium joints

Self-piercing riveting (SPR) is one of the main joining methods for lightweight aluminium automotive body structures due to its advantages. In order to further optimise the structure design and reduce the weight but without compromising strength, reduction of redundant materials in the joint flange area can be considered. For this reason, the influence of rivet to sheet edge distance on the fatigue strengths of self-piercing riveted joints was studied. Five edge distances, 5 mm, 6 mm, 8 mm, 11.5 mm and 14.5 mm, were considered. The results showed that the SPR joints studied in this research had high fatigue resistance and all specimens failed in sheet material along joint buttons or next to rivet heads. For lap shear fatigue tests, specimens failed in the bottom sheet at low load amplitudes and in the top sheet at high load amplitudes except for specimens with very short edge distance of 5 and 6 mm; whereas, for coach-peel fatigue tests, all specimens failed in the top sheet. For both lap shear and coach-peel fatigue tests, specimens with an edge distance of 11.5 mm had the best fatigue resistance. It was found that for coach-peel fatigue, length of crack developing path before specimens lost their strengths was the main factor that determined the fatigue life of different specimens; for lap shear fatigue, the level of stress concentration and subsequent crack initiation time was the main factor that determined the fatigue life

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