A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.
College of Electromechanical Engineering, Anyang Vocational and Technical College, Anyang, China Introduction: Fault diagnosis analysis of mechanical equipment is greatly significant for maintaining ...
ABSTRACT: In this article, we propose developing a reliable and sensitive method for determining tryptamine (TRYP) levels in canned fish samples using spectrofluorimetric analysis. The analytical ...
ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...
Artist’s impression of a binary neutron star merger, emitting gravitational waves and electromagnetic radiation. Detection and analysis of these signals can provide profound insights into the ...
Abstract: In minimax phase error infinite impulse response filters, the group-delay error is typically much larger at the passband edges than elsewhere. In this ...
The fatal shooting of UnitedHealthcare CEO Brian Thompson on Wednesday, which New York City police have described as "brazen" and "targeted," occurred after the insurance provider has faced legal ...
Abstract: SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the ...
MIT this week showcased a new model for training robots. Rather than the standard set of focused data used to teach robots new tasks, the method goes big, mimicking the massive troves of information ...
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