Shwe Kokko city, a casino, entertainment, and tourism complex,from Thailand's side of the border after Bangkok said it would suspend electricity supply to some border areas with Myanmar to try to curb scam centers, in the Mae Sot district, Thailand, on Feb. 5, 2025 | REUTERS Criminals in Southeast Asia are harnessing inexpensive artificial intelligence tools to target bigger pools of potential victims at high speed, keeping scam centers humming even as governments try and crack down, senior officials at Interpol say. Previously, some scams were easy to spot -- from poor quality online ads luring people to work in such centers to the scams themselves, typically designed to make people part with their money through the promise of romance or investment returns. Now, scammers are using large language models and other AI tools to make their cons more sophisticated. Artificial intelligence also allows them to change course quickly, shifting to newer targets and from fresh locations. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

With the rapid development of web services, large amounts of time series data are generated and accumulated across various domains such as finance, healthcare, and online platforms. As such data often co-evolves with multiple variables interacting with each other, estimating the time-varying dependencies between variables (i.e., the dynamic network structure) has become crucial for accurate modeling. However, real-world data is often represented as tensor time series with multiple modes, resulting in large, entangled networks that are hard to interpret and computationally intensive to estimate. In this paper, we propose Kronecker Time-Varying Graphical Lasso (KTVGL), a method designed for modeling tensor time series. Our approach estimates mode-specific dynamic networks in a Kronecker product form, thereby avoiding overly complex entangled structures and producing interpretable modeling results. Moreover, the partitioned network structure prevents the exponential growth of computational time with data dimension. In addition, our method can be extended to stream algorithms, making the computational time independent of the sequence length. Experiments on synthetic data show that the proposed method achieves higher edge estimation accuracy than existing methods while requiring less computation time. To further demonstrate its practical value, we also present a case study using real-world data. Our source code and datasets are available at https://github.com/Higashiguchi-Shingo/KTVGL.

Can't wait to see the second movie! The first two lines are two demonstrations, and the third line is a test input.

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Text-driven image synthesis has made significant advancements with the development ofdiffusion models, transforming howvisual content isgenerated from text prompts. Despite these advances, text-driven image editing, a key area in computer graphics, faces unique challenges. A major challenge is making simultaneous edits across multiple objects or attributes.