Thailand, Cambodia agree to build on ceasefire in talks in China's Yunnan Thailand and Cambodia plan to rebuild mutual trust and consolidate a ceasefire, Beijing says at the end of two days of talks in southwestern China, despite new accusations from the Thai military that its Cambodian counterparts are violating the truce with drone flights. The foreign ministers of Thailand and Cambodia met with the Chinese foreign minister in Yunnan province on Monday for the scheduled two days of talks aimed at ending weeks of fierce fighting along their border that has killed more than 100 people and displaced more than half a million civilians in both countries. As part of the deal, Thailand has agreed to return 18 captured Cambodian soldiers on Tuesday if the ceasefire, which took effect at noon (05:00 GMT) on Saturday, is fully observed. Speaking to reporters after the meeting, Thai Foreign Minister Sihasak Phuangketkeow said he believed the parties were "moving in a positive direction". "We haven't resolved everything, but I think we are making progress in the right direction, and we have to keep up the momentum," he said.

Things to Do in L.A. Tap to enable a layout that focuses on the article. 'Memory manipulation is inevitable': How rewriting memory in the lab might one day heal humans Professor and neuroscientist Steve Ramirez, shown working with brain samples, is exploring the science of memory manipulation. This is read by an automated voice. Please report any issues or inconsistencies here . Scientists have found that memories are not static records but dynamic processes that change the brain's wiring each time they are recalled.

Capable of creating "nearly perfect" face swaps during live video chats, Hoatian has made millions, mainly via Telegram. But its main channel vanished after WIRED's inquiry into scammers using the app. The Chinese-language artificial intelligence app Haotian is so effective that it's made millions of dollars selling its face-swapping technology on Telegram . The service integrates easily with messaging platforms like WhatsApp and WeChat and claims that users can tweak up to 50 settings--including the ability to adjust things like cheekbone size and eye position--to help mimic the face they are impersonating. But while Haotian is a robust and versatile platform, researchers and WIRED's own analysis have found that the service has been marketing to so-called "pig butchering" scammers and those running online fraud operations in Southeast Asia.

Things to Do in L.A. Tap to enable a layout that focuses on the article. L.A. County gets a new tool to find and save vulnerable people with cognitive disabilities Jordan Wall, 27, of Chatsworth, -- an athlete, actor and global messenger for the Special Olympics -- wears her new GPS watch from the group L.A. Found on Oct. 15, 2025. The county program L.A. Found offers free tracking devices to residents with cognitive disabilities who are at risk of wandering away from home. Since launching seven years ago, more than 1,800 people have received devices through the program, with 29 successfully located after going missing. Janet Rivera cares for both her 79-year-old mother, who has dementia, and her 25-year-old son, who has a genetic condition called Fragile X syndrome.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Health Secretary Robert F. Kennedy Jr. speaks about autism in the White House on Monday as President Trump and Centers for Medicare & Medicaid Services Administrator Dr. Mehmet Oz look on. This is read by an automated voice. Please report any issues or inconsistencies here . On Monday, President Trump led a White House press event where he and many of his administration's health leaders told the public that taking Tylenol during pregnancy increases the risk of autism in children.

Things to Do in L.A. Tap to enable a layout that focuses on the article. A woman in the audience wears a red hat that reads Make America Healthy Again during a Senate Homeland Security and Government Affairs Subcommittee Hearing on Capitol Hill on September 9, 2025 in Washington, DC. The hearing was titled "how the corruption of science has impacted public perception and policies regarding vaccines." Voice comes from the use of AI. Please report any issues or inconsistencies here .

Things to Do in L.A. Tap to enable a layout that focuses on the article. Voice comes from the use of AI. Please report any issues or inconsistencies here . OpenAI will roll out parental controls within the month, allowing parents to link accounts and receive alerts when the system detects "acute distress." The changes follow a California family's lawsuit after their 16-year-old son died by suicide following intimate conversations with ChatGPT about his mental health struggles.

Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a continuous-time approximation as a Fokker-Planck equation for Langevin dynamics, a viewpoint that has motivated many theoretical studies. Within this framework, we study the relationship between the quasi-stationary distribution derived from this equation and the initial distribution through the Kullback-Leibler (KL) divergence. As the quasi-steady-state distribution depends on the expected cost function, the KL divergence eventually reveals the connection between the expected cost function and the initialization distribution. By applying this to deep neural network models (DNNs), we can express the bounds of the expected loss function explicitly in terms of the initialization parameters. Then, by minimizing this bound, we obtain an optimal condition of the initialization variance in the Gaussian case. This result provides a concrete mathematical criterion, rather than a heuristic approach, to select the scale of weight initialization in DNNs. In addition, we experimentally confirm our theoretical results by using the classical SGD to train fully connected neural networks on the MNIST and Fashion-MNIST datasets. The result shows that if the variance of the initialization distribution satisfies our theoretical optimal condition, then the corresponding DNN model always achieves lower final training loss and higher test accuracy than the conventional He-normal initialization. Our work thus supplies a mathematically grounded indicator that guides the choice of initialization variance and clarifies its physical meaning of the dynamics of parameters in DNNs.

Generating Wikipedia articles autonomously is a challenging task requiring the integration of accurate, comprehensive, and well-structured information from diverse sources. This paper introduces the Memory Organization-based Generation (MOG) framework, a novel approach to address these challenges by leveraging a hierarchical memory architecture. MOG extracts fine-grained memory units from web documents, recursively organizes them into a Wikipedia-style hierarchical structure, and uses this structure to guide the generation process. This ensures alignment between memory and the article outline, improving both informativeness and verifiability while minimizing hallucinations. Additionally, a citation module is implemented to enhance traceability by linking every generated sentence to specific memory units. Evaluations on our newly created WikiStart dataset demonstrate that MOG outperforms baseline methods in producing informative and reliable articles, making it particularly robust in real-world scenarios.

Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.