Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Ukrainian officials have left for Geneva, Switzerland, where another round of negotiations aimed at ending the war with Russia is set to take place. The next round of negotiations is ahead.

Fears over a drug cartel drone over Texas sparked a recent airspace shutdown in El Paso and New Mexico, highlighting just how tricky it can be to deploy anti-drone weapons near cities. A shocking but ultimately brief airspace closure over El Paso, Texas, and parts of New Mexico last week is stoking unease among pilots and the broader public about the status of United States anti-drone defenses. As low-cost UAV equipment proliferates around the world, analysts have repeatedly warned that destructive attacks perpetrated using drones are inevitable . It is challenging to develop nimble and safe countermeasures, though, given that things like jamming or attempting to shoot down a drone are difficult--or even impossible--to carry out safely in populated areas, much less densely populated cities. In the case of the El Paso incident, the Federal Aviation Administration originally set the airspace closure to last 10 days, but ultimately lifted it after eight hours.

In hacker spaces and at their homes, creative protesters are laser-cutting and 3D-printing tools to resist an occupation. As the US government's immigration crackdown expands across the country, anxious residents have mobilized to look out for each other. One way they're doing that is by finding ways to build the tools they need to be resilient against the surge of Immigration and Customs Enforcement agents empowered to kill with impunity . All over the country, makers are 3D-printing thousands of whistles to help people on the ground alert others to nearby ICE activity. But the whistles are far from the only tools being used to respond to the surge of federal agents.

A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective In this appendix, we will derive Eq. 4. Analogously to Eq. 3, we optimize the following objective: max

In the United States, health insurance is a critical component of individuals' ability to access health care.

Transformer-based architectures have recently exhibited remarkable successes across different domains beyond just powering large language models. However, existing approaches typically focus on predictive accuracy and computational cost, largely ignoring certain other practical issues such as robustness to contaminated samples. In this paper, by re-interpreting the self-attention mechanism as a non-parametric kernel density estimator, we adapt classical robust kernel density estimation methods to develop novel classes of transformers that are resistant to adversarial attacks and data contamination. We first propose methods that down-weight outliers in RKHS when computing the self-attention operations. We empirically show that these methods produce improved performance over existing state-of-the-art methods, particularly on image data under adversarial attacks. Then we leverage the median-of-means principle to obtain another efficient approach that results in noticeably enhanced performance and robustness on language modeling and time series classification tasks. Our methods can be combined with existing transformers to augment their robust properties, thus promising to impact a wide variety of applications.

For example, HE uses secrets with small entries, and the HE community has considered standardizing small sparse secrets to improve efficiency and functionality.