Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

阿里妹导读本文的核心思路是从Prompt、Context和Harness这三个维度展开，分析OpenClaw的设计思路，提炼出其中可复用的方法论，来思考如何将这些精华的设计哲学应用到我们自己的Agent系统设计和业务落地中去。（文章内容基于作者个人技 ...

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LIMU-BERT, a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. LIMU-BERT adopts the principle of natural language ...

Predicting interactions between microRNAs (miRNAs) and competing endogenous RNAs (ceRNAs), including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), is essential for understanding gene ...

Of 372 patients studied, 79.3% and 20.7% were in the completion group and the non-completion group, respectively. The final BERT model achieved average F1 scores of 0.91 and 0.98 for time to ...

Abstract: In this paper, we propose a fully quantized matrix arithmetic-only BERT (FQ MA-BERT) model to enable efficient natural language processing. Conventionally, the BERT model relies on floating ...

Pydantic has introduced an open-source server designed to let AI agents execute Python code within a secure, isolated environment. The new tool leverages the Model Context Protocol (MCP), an open ...