This guide is primarily aimed at researchers from AI and machine learning backgrounds who may not be familiar with neuroimaging methodology. Reconstruction from neuroimaging data has recently gained popularity at major AI conferences, but many approaches fall into common traps that are well known within neuroscience. These pitfalls can lead to misleading results, often due to misunderstandings about the nature of fMRI data or the limitations of datasets originally collected for other research questions. For a detailed discussion of such issues in recent reconstruction pipelines, see: Shirakawa, K. et al. (2025). Spurious reconstruction from brain activity, Neural Networks .
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文 | 巨潮WAVE，作者｜小卢鱼，编辑｜杨旭然
此外，高配版本标配了 MRC 磁流变悬架。这套系统能根据路况实时调节阻尼，在同价位的方盒子 SUV 中并不多见，对改善公路行驶质感大有裨益。
In designing Kanon 2 Enricher, we had to work around several hard constraints of ILGS such as that each entity must be anchored to a document through character-level spans corresponding to entity references and all such spans must be well-nested and globally laminar within a document (i.e., no two spans in a document can partially overlap). Wherever feasible, we tried to enforce our schematic constraints architecturally, whether by using masks or joint scoring, otherwise resorting to employing custom regularizing losses.