Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. In our eighth grade classroom, her name was Hanna. On AOL Instant Messenger, she was Banana3017. I was in love with both. At school, she was funny, and kind, and she had blue eyes that made my cheeks glow the same fiery color as her hair when she looked at me.

Large Language Models (LLMs) often retain inaccurate or outdated information from pre-training, leading to incorrect predictions or biased outputs during inference. While existing model editing methods can address this challenge, they struggle with editing large amounts of factual information simultaneously and may compromise the general capabilities of the models. In this paper, our empirical study demonstrates that it is feasible to edit the internal representations of LLMs and replace the entities in a manner similar to editing natural language inputs. Based on this insight, we introduce the Latent Knowledge Scalpel (LKS), an LLM editor that manipulates the latent knowledge of specific entities via a lightweight hypernetwork to enable precise and large-scale editing. Experiments conducted on Llama-2 and Mistral show even with the number of simultaneous edits reaching 10,000, LKS effectively performs knowledge editing while preserving the general abilities of the edited LLMs. Code is available at: https://github.com/Linuxin-xxx/LKS.

Building height is an important indicator for scientific research and practical application. However, building height products with a high spatial resolution (10m) are still very scarce. To meet the needs of high-resolution building height estimation models, this study established a set of spatial-spectral-temporal feature databases, combining SAR data provided by Sentinel-1, optical data provided by Sentinel-2, and shape data provided by building footprints. The statistical indicators on the time scale are extracted to form a rich database of 160 features. This study combined with permutation feature importance, Shapley Additive Explanations, and Random Forest variable importance, and the final stable features are obtained through an expert scoring system. This study took 12 large, medium, and small cities in the United States as the training data. It used moving windows to aggregate the pixels to solve the impact of SAR image displacement and building shadows. This study built a building height model based on a random forest model and compared three model ensemble methods of bagging, boosting, and stacking. To evaluate the accuracy of the prediction results, this study collected Lidar data in the test area, and the evaluation results showed that its R-Square reached 0.78, which can prove that the building height can be obtained effectively. The fast production of high-resolution building height data can support large-scale scientific research and application in many fields.

In each case it is a near-certainty that their quick thinking, powered by the uniquely human ability to combine high levels of training and proficiency with creativity at critical moments, saved lives. Specifically a total of 875 lives were saved by these airmen and their crew mates in these six headline-grabbing incidents. It also is a near-certainty that no technology available today, and none that is likely to be available for use in the next generation or two of commercial aircraft (which will be in service at least through the middle of this century) is or will be capable of the kind of rapid-fire, mentally elastic and way outside-the-box thinking that each of these pilots demonstrated. Significant technical, regulatory, spectrum/bandwidth, artificial intelligence, financial and insurance barriers are but the most obvious challenges.