Abstract: Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this ...

Landlords could no longer rely on rent-pricing software to quietly track each other's moves and push rents higher using confidential data, under a settlement between RealPage Inc. and federal ...

Stock returns exhibit nonlinear dynamics and volatility clustering. It is well known that we cannot forecast the movements of stock prices under the condition that market is efficient. In most ...

In structural health monitoring (SHM), uncertainties from environmental noise and modeling errors affect damage detection accuracy. This paper introduces a new concept: the Fast Fourier Transform ...

There's a familiar TV discourse taking shape online right now, the kind that I suspect will look awfully familiar to you if you remember the way Game of Thrones crashed and burned in its eighth and ...

Abstract: Soft clustering algorithms based on fuzzy C-means (FCM) have been extensively applied to complex data analysis. However, existing FCM variants still encounter key limitations: a large number ...

Feeling the Squeeze? It Might Be the High Price of Ignoring the Deficit Audio By Carbonatix One user even said it was 'weird, dystopian, and privacy invasive' due to the facial-recognition technology.

Researchers have developed a new AI algorithm, called Torque Clustering, that significantly improves how AI systems independently learn and uncover patterns in data, without human guidance.

Aligning large language models (LLMs) with human values remains difficult due to unclear goals, weak training signals, and the complexity of human intent. Direct Alignment Algorithms (DAAs) offer a ...