In this chapter, we argue that Du Châtelet’s account of motion is an important contribution to the history of the absolute versus relative motion debate. The arguments we lay out have two main strands. First, we clarify Du Châtelet’s threefold taxonomy of motion, using Musschenbroek as a useful Newtonian foil and showing that the terminological affinity between the two is only apparent. Then, we assess Du Châtelet’s account in light of the conceptual, epistemological, and ontological challenges posed by Newton to (...) any relational theory of motion. What we find is that, although Du Châtelet does not meet all the challenges to their full extent, her account of motion is adequate for the goal of the Principia: determining the true motions in our planetary system. (shrink)

The definitions of yu 宇 and zhou 宙 in the “Gengsang Chu 庚桑楚” chapter of the Zhuangzi have been cited as the earliest definitions of space and time in the Chinese philosophical tradition. However, careful analysis of chosen modern translations reveals that the definitions entail rather obscure relationships between the Dao and space and time. I argue that the obscurity is not inherent in the text, but arises from the practice of rendering yu and zhou as the conceptual counterparts of (...) space and time or related concepts. Instead, I propose that we leave the terms untranslated. This practice would not only forestall interpretive difficulties concerning the Dao’s relations to space and time, but it would also preserve the Zhuangzist yu and zhou as two distinctive, ontic notions that indicate the Dao’s indeterminate modes of existence, a unique feature of the Zhuangzi. (shrink)

Previous studies have shown that social categorization can induce an own-group face recognition bias. However, similar and better other-group face recognition emerged recently. In this research, we aimed to examine whether competitive cues and group status accompanied by social categorization can modulate the inter-group face recognition bias. Moreover, we investigated how the group identification of individuals with different statuses affected the inter-group face recognition bias. The results indicated that an own-group face recognition bias emerged for targets with in-group labels compared (...) to out-group labels. Moreover, when the group labels signaled competitive cues, the own-group face recognition bias was reversed. Furthermore, low-status and similar-status individuals exhibited out-group face recognition bias, but high-status individuals did not. In addition, the higher the in-group identification scores of participants from the low-status group, the stronger the out-group face recognition bias. These results suggested that competitive cues would reverse the own-group face recognition bias and the group status would play a modulating role in face recognition bias. (shrink)

This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are generated (...) closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems. (shrink)

Dynamic multiobjective optimization problems bring more challenges for multiobjective evolutionary algorithm due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types of (...) linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs. (shrink)