Things to Do in L.A. Canadian snowbirds are still unhappy with Trump. This is read by an automated voice. Please report any issues or inconsistencies here . Palm Springs relies heavily on Canadian tourists, who are declining to travel to the U.S. or shortening their stays because of Trump. The number of Canadian visitors to California plummeted more than 18% in 2025 compared with the year prior.

We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound.

Transformer architectures have shown impressive performance in multiple research domains and have become the backbone of many neural network models. However, there is limited understanding on how Transformer works. In particular, with a simple predictive loss, how the representation emerges from the gradient training dynamics remains a mystery. In this paper, we analyze the SGD training dynamics for 1-layer transformer with one self-attention plus one decoder layer, for the task of next token prediction in a mathematically rigorous manner. We open the black box of the dynamic process of how the self-attention layer combines input tokens, and reveal the nature of underlying inductive bias. More specifically, with the assumption (a) no positional encoding, (b) long input sequence, and (c) the decoder layer learns faster than the self-attention layer, we prove that self-attention acts as a discriminative scanning algorithm: starting from uniform attention, it gradually attends more to key tokens that are distinct for a specific next token to be predicted, and pays less attention to common key tokens that occur across different next tokens. Among distinct tokens, it progressively drops attention weights, following the order of low to high co-occurrence between the key and the query token in the training set. Interestingly, this procedure does not lead to winner-takes-all, but decelerates due to a phase transition that is controllable by the learning rates of the two layers, leaving (almost) fixed token combination. We verify this scan and snap dynamics on synthetic and real-world data (WikiText).

Novel categories names:['High_Jump', 'Front_Crawl', 'Pole_V ault', 'Hammer_Throw', All experiments are conducted under the 16-shot setting. An incremental bayesian approach tested on 101 object categories. Conditional prompt learning for vision-language models.

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.

To alleviate the above issues, we propose a simple yet effective framework for producing H igh Q uality black-box hard-label A dversarial Attack, named HQA-Attack . The overview of HQA-Attack is shown in Figure 1. By "high quality", it means that the HQA-Attack method can generate

Our development of ToolQA involved a scalable, automated process for dataset curation, along with 13 specialized tools designed for interaction with external knowledge in order to answer questions.

A notable example that motivates our work is the problem of minimizing costs (or maximizing rewards) in a single-trajectory Markov Decision Process (MDP).