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

Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model's ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt Engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://github.com/CVI-SZU/QA-CLIMSComment: ACM MM 202

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

SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds

Modern graph neural networks (GNNs) can be sensitive to changes in the input graph structure and node features, potentially resulting in unpredictable behavior and degraded performance. In this work, we introduce a spectral framework known as SAGMAN for examining the stability of GNNs. This framework assesses the distance distortions that arise from the nonlinear mappings of GNNs between the input and output manifolds: when two nearby nodes on the input manifold are mapped (through a GNN model) to two distant ones on the output manifold, it implies a large distance distortion and thus a poor GNN stability. We propose a distance-preserving graph dimension reduction (GDR) approach that utilizes spectral graph embedding and probabilistic graphical models (PGMs) to create low-dimensional input/output graph-based manifolds for meaningful stability analysis. Our empirical evaluations show that SAGMAN effectively assesses the stability of each node when subjected to various edge or feature perturbations, offering a scalable approach for evaluating the stability of GNNs, extending to applications within recommendation systems. Furthermore, we illustrate its utility in downstream tasks, notably in enhancing GNN stability and facilitating adversarial targeted attacks