Prediction markets like Kalshi and Polymarket are booming, and so is a fight among regulators, lawmakers, and advocates over their legality. Former New Jersey governor Chris Christie, who currently serves as an advisor to the American Gaming Association, has criticized prediction markets. The political fight in the US over the future of prediction markets like Polymarket and Kalshi has escalated into a full-blown war, and battle lines aren't being neatly drawn along party lines. Instead, conservative Mormons have aligned themselves with Las Vegas bigwigs and MAGA royalty is siding with liberal Democrat lobbyists. One side argues that the platforms are breaking the law by operating as shadow casinos.

Contrastive Clustering loss and update the prototypical vectors.Algorithm 1: Prototypical Constrative Clustering Loss Compute Prototypical Constrative Clustering Loss based on Eqn. Compute Prototypical Constrative Clustering Loss based on Eqn. Two segmentation network architectures have been used in our experiments, i.e., (1) DeepLab-V3 The learning rate is set individually for each step and dataset. Similarly, to illustrate the effectiveness and robustness of our method in the non-incremental setting. We also perform an additional ablation study on the ADE20K (100-50) benchmark to investigate the impact of the delta.

Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes.

'Sharkansas' contains entire fossilized skeletons dating back 320 million years. Breakthroughs, discoveries, and DIY tips sent six days a week. Arkansas is hundreds of miles from the Gulf of Mexico, but it's home to countless sharks . A trove of the fossilized predator's remains are embedded within the Fayetteville Shale --a roughly 350-million-year-old geological formation in the state's northwestern corner. Because a shark's cartilage skeleton decomposes so quickly, they usually only leave teeth behind when they die.

Small jaws mean big problems for modern humans. Modern diets gave us smaller jaws--and a lifetime of orthodontic problems. Breakthroughs, discoveries, and DIY tips sent six days a week. Every year, millions of children and teens undergo a common ritual of growing up: getting braces. And it's not just young folks who turn to metal brackets to handle some common dental issues--the Cleveland Clinic estimates that some 20% of new orthodontic patients are over the age of 18 .

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Federal immigration enforcement faces challenges as Minnesota ice agents deal with violent protests and uncooperative local officials nationwide.

Shipping experts say the big winter storm across a wide swath of the country should be business as usual--if their safeguards hold. Up to two-thirds of the US is facing down the threat of serious snow, cold, and ice this weekend, with the potential to snarl roads (and the businesses that depend on them) from Texas up to New York City . At this point, grocery stores, logistics experts, warehouse operators, and trucking companies have been prepping for days. Still, the effects on the supply chain--and the retail store shelves that depend on them--are yet to be determined. On one hand, this is winter business as usual.

Despite their promise, fair machine learning methods often yield Pareto-inefficient models, in which the performance of certain groups can be improved without degrading that of others. This issue arises frequently in traditional in-processing approaches such as fairness-through-regularization. In contrast, existing Pareto-efficient approaches are biased towards a certain perspective on fairness and fail to adapt to the broad range of fairness metrics studied in the literature. In this paper, we present BADR, a simple framework to recover the optimal Pareto-efficient model for any fairness metric. Our framework recovers its models through a Bilevel Adaptive Rescalarisation procedure. The lower level is a weighted empirical risk minimization task where the weights are a convex combination of the groups, while the upper level optimizes the chosen fairness objective. We equip our framework with two novel large-scale, single-loop algorithms, BADR-GD and BADR-SGD, and establish their convergence guarantees. We release badr, an open-source Python toolbox implementing our framework for a variety of learning tasks and fairness metrics. Finally, we conduct extensive numerical experiments demonstrating the advantages of BADR over existing Pareto-efficient approaches to fairness.

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