Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.

Like The New Yorker, I was born in 1925. Somewhat to my surprise, I decided to keep a journal of my hundredth year. The author, who was born on December 17, 1925, notes that the magazine's first issue came out ten months before he did. Old age is no joke, but it can feel like one. You look everywhere for your glasses, until your wife points out that you're wearing them. I turn a hundred this year. People act as though this is an achievement, and I suppose it is, sort of. Nobody in my family has lived this long, and I've been lucky. I'm still in pretty good health, no wasting diseases or Alzheimer's, and friends and strangers comment on how young I look, which cues me to cite the three ages of man: Youth, Maturity, and You Look Great. On the other hand, I've lost so many useful abilities that my wife, Dodie, and I have taken to calling me Feebleman. Look, up in the sky! No, it's Dodie doesn't want me to know how old she is, but she's nearly three decades younger than I am, and I become ...

Things to Do in L.A. Tap to enable a layout that focuses on the article. State Sen. Scott Wiener stands in front of a mural at Oasis, a drag show he helped the owners launch in San Francisco. He intends to run for Nancy Pelosi's long-held congressional seat. The former House speaker has not said whether she will seek another term. This is read by an automated voice.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Among the evidence collected from the digital devices of Jonathan Rinderknecht of Florida, who was arrested in the Palisades fire, were images he generated on ChatGPT depicting a burning city, said acting U.S. Atty. This is read by an automated voice. Please report any issues or inconsistencies here . Jonathan Rinderknecht, 29, a one-time L.A. Uber driver and now Florida resident, was arrested by the FBI on Oct. 7 and charged with destruction of property for allegedly starting a Jan. 1 blaze known as the Lachman fire that smoldered for six days until it became the most destructive wildfire in Los Angeles history.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency

Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data.

Operator learning has emerged as a promising tool for accelerating the solution of partial differential equations (PDEs). The Deep Operator Networks (DeepONets) represent a pioneering framework in this area: the "vanilla" DeepONet is valued for its simplicity and efficiency, while the modified DeepONet achieves higher accuracy at the cost of increased training time. In this work, we propose a series of Transformer-inspired DeepONet variants that introduce bidirectional cross-conditioning between the branch and trunk networks in DeepONet. Query-point information is injected into the branch network and input-function information into the trunk network, enabling dynamic dependencies while preserving the simplicity and efficiency of the "vanilla" DeepONet in a non-intrusive manner. Experiments on four PDE benchmarks -- advection, diffusion-reaction, Burgers', and Korteweg-de Vries equations -- show that for each case, there exists a variant that matches or surpasses the accuracy of the modified DeepONet while offering improved training efficiency. Moreover, the best-performing variant for each equation aligns naturally with the equation's underlying characteristics, suggesting that the effectiveness of cross-conditioning depends on the characteristics of the equation and its underlying physics. To ensure robustness, we validate the effectiveness of our variants through a range of rigorous statistical analyses, among them the Wilcoxon Two One-Sided Test, Glass's Delta, and Spearman's rank correlation.

Building damage identification shortly after a disaster is crucial for guiding emergency response and recovery efforts. Although optical satellite imagery is commonly used for disaster mapping, its effectiveness is often hampered by cloud cover or the absence of pre-event acquisitions. To overcome these challenges, we introduce a novel multimodal deep learning (DL) framework for detecting building damage using single-date very high resolution (VHR) Synthetic Aperture Radar (SAR) imagery from the Italian Space Agency (ASI) COSMO SkyMed (CSK) constellation, complemented by auxiliary geospatial data. Our method integrates SAR image patches, OpenStreetMap (OSM) building footprints, digital surface model (DSM) data, and structural and exposure attributes from the Global Earthquake Model (GEM) to improve detection accuracy and contextual interpretation. Unlike existing approaches that depend on pre and post event imagery, our model utilizes only post event data, facilitating rapid deployment in critical scenarios. The framework effectiveness is demonstrated using a new dataset from the 2023 earthquake in Turkey, covering multiple cities with diverse urban settings. Results highlight that incorporating geospatial features significantly enhances detection performance and generalizability to previously unseen areas. By combining SAR imagery with detailed vulnerability and exposure information, our approach provides reliable and rapid building damage assessments without the dependency from available pre-event data. Moreover, the automated and scalable data generation process ensures the framework's applicability across diverse disaster-affected regions, underscoring its potential to support effective disaster management and recovery efforts. Code and data will be made available upon acceptance of the paper.

Neural networks are very effective machine learning tools that prove extremely valuable in unwinding the best representation of the data at hand. To improve the ability of neural networks to automatically perform the tasks assigned, innovative architectures have been proposed and thoroughly tested. Employed architectures have been customarily developed by human experts, with manual, time-consuming, and error-prone processes. To go beyond manual design, novel algorithmic strategies for automated discovery of optimal neural architectures have been developed. Consequently, architecture engineering has become a relevant field of active research [1,2]. Neural Architecture Search (NAS), the process that seeks to optimize network architecture, has been successfully applied on tasks as image classification [3,4], object detection [3], or semantic segmentation [5], yielding remarkable performance, as compared to manually designed benchmarks. According to [1], NAS is a subfield of Automated Machine Learning (AutoML) [6], the process that aims at automating the steps propaedeutic to applying machine learning to real-world problems. It also shows a notable overlap with hyperparameter optimization, a critical process in machine learning that involves selecting the optimal set of hyperparameters for a learning algorithm.