We asked attendees of Super Bowl LX's pregame festivities for their takes on the competing halftime shows, the potential for ICE actions, and the influence of Silicon Valley on the event. To say this year's Super Bowl came at a charged time in American culture and politics is, perhaps, an understatement. While the pair of teams who took the field Sunday--the Seattle Seahawks and the New England Patriots--comprised a pretty classic matchup (no underdogs here!), the rest of the event was set to be anything but. Santa Clara's Levi's Stadium is in the heart of Silicon Valley, just a few miles from the corporate headquarters of Nvidia and AMD, whose chips are powering the AI arms race that had competitors OpenAI and Anthropic sparring via rival Super Bowl ads . There was an explosion in sports "trading" activity on sites like Kalshi and Polymarket in the lead-up to the game, even in states like California where traditional sports betting is illegal. Sunday could prove to be an extraordinary success for prediction markets, as the industry becomes more mainstream . Fresh off a historic Grammy Album of the Year win (a first for a Spanish-language album), the unapologetically political Puerto Rican rapper and singer Bad Bunny headlined --a choice that sparked a perhaps inevitable MAGA backlash. Meanwhile, Turning Point USA organized an alternative program called The All-American Halftime Show, featuring the likes of Kid Rock and Brantley Gilbert. Never mind that Bad Bunny is Puerto Rican, and therefore an American citizen. Rumors were even buzzing about possible actions by US Immigration and Customs Enforcement agents at the Super Bowl. Even though the NFL and California governor Gavin Newsom said on Thursday that there would be " no immigration enforcement tied to the game," anti-ICE protesters were on the streets. We caught up with football fans at a tailgate five minutes away from Levi's Stadium to hear their thoughts on all the drama. Here's what they had to say.

A central question in computational neuroscience is how structure determines functioninneuralnetworks.

This application is often needed in safety-critical domains suchashealthcare, whereestimating andcommunicating uncertainty to decision-makers iscrucial. Weintroduce apractical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in highdimensional data, where standard applications of causal effect approaches fail.

In this paper, we investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose two novel data augmentation techniques specifically designed for the constraints of differentially private learning.

Organization In Section A, we first introduce the baselines and our model and then describe the experimental details of graph classification and link prediction tasks but also our in-depth analyses. Then, in Section B, we provide the additional experimental results about analyses on datasets, ablation study for our proposed objectives, effects of our hyperparameters (λ1, α, λ2, and the perturbation magnitude), ablation study of attribute masking, and the comparison with augmentation-freeapproaches. In particular,thepre-training dataset consists of306K unlabeled protein ego-networksof50species,andthe fine-tuning dataset consists of 88K protein ego-networks of 8 species with the label given by the functionalityoftheegoprotein. For pre-training, the number of epochs is 100, the batch size is128, the learning rate is0.001, and the margin is10. For fine-tuning, we also follow the conventional setting from Hu et al.[3]. ForJOAOandGraphLoG, we use the publicsource codes4,toobtain the pre-trained models.

Figure 1: (a) Conventional predictive learningthat aims to predict local attributes by masking them.(b) Conventional contrastive learningthat could maximize the similarity of dissimilar graphs perturbed from original graphs.

The use of machine learning models in high-stake applications (e.g., healthcare, lending, college admission) has raised growing concerns due to potential biases