The Inconceivable Popularity of Conceivability Arguments

Famous examples of conceivability arguments include (i) Descartes’ argument for mind-body dualism, (ii) Kripke's ‘modal argument’ against psychophysical identity theory, (iii) Chalmers’ ‘zombie argument’ against materialism, and (iv) modal versions of the ontological argument for theism. In this paper, we show that for any such conceivability argument, C, there is a corresponding ‘mirror argument’, M. M is deductively valid and has a conclusion that contradicts C's conclusion. Hence, a proponent of C—henceforth, a ‘conceivabilist’—can be warranted in holding that C's premises are conjointly true only if she can find fault with one of M's premises. But M's premises are modelled on a pair of C's premises. The same reasoning that supports the latter supports the former. For this reason, a conceivabilist can repudiate M's premises only on pain of severely undermining C's premises. We conclude on this basis that all conceivability arguments, including each of (i)–(iv), are fallacious

Genomic Effects on Milk Fatty Acid Composition of Beef Cows and Its Influences on Calf Pre-weaning Growth

Research has shown that milk yield (MWT) accounts for only a moderate amount of variation in pre-weaning average daily gain (PRWADG). This study was proved that milk fatty acid methyl esters (FAME), alone and in combination with MWT, could improve accuracy of prediction of PRWADG using stepwise regression and partial least squares (PLS) models. The milk fatty acid composition of beef cows is markedly influenced by nutritional factors and also significantly controlled by a few major genes effects. In the second part of this study, three genes, diacylglycerol O-acyltransferase 1 (DGAT1), stearoyl-CoA desaturase 1 (SCD1), and fatty acid synthase (FASN), were selected to determine their associations with milk fatty acids of beef cows sired by 6 different breeds (Bonsmara, Brangus, Charolais, Gelbvieh, Hereford and Romosinuano) out of Brangus dams. Results showed genotypic differences in variants of the DGAT1 gene for saturated fatty acid (SFA), the ratio of omega-6 to omega-3 fatty acids (N6/N3), C14:0, C18:1n9c. C22:1n9 and C22:5n3 (P < 0.05), and for omega-3 fatty acids (N3) and the ratio of polyunsaturated fatty acid to saturated fatty acid (PUFA/SFA) (P < 0.10). The variation in the SCD1 gene also influenced vaccenic acid, C20:0 and C21:0 (P<0.10). For FASN gene, genotypic differences affected the composition of C22:1n9 (P<0.05) and C22:0 (P<0.10). However, genotypic differences for each fatty acid category were not consistent among the different sire breeds.Animal Scienc

XRoute Environment: A Novel Reinforcement Learning Environment for Routing

Routing is a crucial and time-consuming stage in modern design automation flow for advanced technology nodes. Great progress in the field of reinforcement learning makes it possible to use those approaches to improve the routing quality and efficiency. However, the scale of the routing problems solved by reinforcement learning-based methods in recent studies is too small for these methods to be used in commercial EDA tools. We introduce the XRoute Environment, a new reinforcement learning environment where agents are trained to select and route nets in an advanced, end-to-end routing framework. Novel algorithms and ideas can be quickly tested in a safe and reproducible manner in it. The resulting environment is challenging, easy to use, customize and add additional scenarios, and it is available under a permissive open-source license. In addition, it provides support for distributed deployment and multi-instance experiments. We propose two tasks for learning and build a full-chip test bed with routing benchmarks of various region sizes. We also pre-define several static routing regions with different pin density and number of nets for easier learning and testing. For net ordering task, we report baseline results for two widely used reinforcement learning algorithms (PPO and DQN) and one searching-based algorithm (TritonRoute). The XRoute Environment will be available at https://github.com/xplanlab/xroute_env.Comment: arXiv admin note: text overlap with arXiv:1907.11180 by other author

Scattering Analysis of Electromagnetic Materials Using Fast Dipole Method Based on Volume Integral Equation

The fast dipole method (FDM) is extended to analyze the scattering of dielectric and magnetic materials by solving the volume integral equation (VIE). The FDM is based on the equivalent dipole method (EDM) and can achieve the separation of the field dipole and source dipole, which reduces the complexity of interactions between two far groups (such as group i and group j) from O(NiNj) to O(Ni+Nj), where Ni and Nj are the numbers of dipoles in group i and group j, respectively. Targets including left-handed materials (LHMs), which are a kind of dielectric and magnetic materials, are calculated to demonstrate the merits of the FDM. Furthermore, in this study we find that the convergence may become much slower when the targets include LHMs compared with conventional electromagnetic materials. Numerical results about convergence characteristics are presented to show this property