A piece of the UFC White House event's setup is sitting in Pennsylvania Amish country Viral Ottawa Senators fan blamed for team's 0-2 playoff start banished to Taiwan Edward Cabrera's strikeout prop is the play as struggling Phillies face surging Cubs today Nuggets vs Timberwolves Game 3 pick hinges on Jaden McDaniels calling out Denver's entire defense Charles Barkley was disgusted by Magic's highly questionable pregame handshake ChatGPT predicted the first round of the NFL Draft and here's what it said Curt Cignetti was so focused this offseason, he turned down all external requests: 'I'm 95% football' Former MLB owner claims'despicable' San Francisco Giants are the reason the A's left Oakland Trump weighs in on Iran's internal power struggle and Strait of Hormuz control Hasan Piker justifies'social murder' of CEO Fox News celebrates'Bring Your Kids to Work Day' Trump says there's'no time frame' to secure Iran deal Iranian activist praises Trump's intervention after female protesters saved from execution Silver says owner Robert Pera has no interest in relocating but wants the Grizz to be'Tennessee's team' Rumors of the Memphis Grizzlies potentially leaving the Bluff City are nothing new, but they've gotten louder in recent months on the heels of the franchise's worst season in nearly a decade. NBA commissioner Adam Silver, however, recently explained that Memphians have nothing to worry about, but did offer up a suggestion for the team that some fans may be hesitant to commit to. Silver recently joined the Pardon My Take podcast and, for the most part, delivered the Memphis-friendly message. NBA Commissioner Adam Silver held a press conference at Chase Center in San Francisco, Calif., on Feb. 15, 2025, during NBA All-Star weekend. There's no reason why the Memphis Grizzlies can't be successful.

Michael Dell and Susan Dell became the first donors to give more than $1 billion to the University of Texas at Austin, funding a massive AI-native hospital and research campus.

The initial rollout will be limited to a couple of neighborhoods in the two cities. Tesla is expanding its Robotaxi footprint across Texas by introducing availability in both Dallas and Houston. As announced in a post on X, the EV maker is rolling out its Robotaxis to small sections of the Texas cities, as detailed by two maps of its new service areas. The first Robotaxi rides started in Austin, Texas where Tesla is headquartered, but the service's launch was paired with a Tesla Safety Monitor, or a supervising human in the passenger seat. Earlier this year, Tesla began to transition away from including safety monitors, leaving its Robotaxis to operate unsupervised and fully autonomous.

We consider the problem of clustering nested or hierarchical data, where observations are grouped and there are both group-level and observation-level variables. In our motivating OneK1K dataset, observations consist of single-cell RNA-sequencing (scRNA-seq) data from 982 individuals (groups), totaling 1.27 million cells (observations), along with individual-specific genotype data. This type of data would enable the identification of cell types and the investigation of how genetic variations among individuals influence differences in cell-type profiles. Our goal, therefore, is to jointly cluster cells and individuals to capture the heterogeneity across both levels using cell-specific gene expressions as well as individual-specific genotypes. However, existing grouped clustering methods do not incorporate group-level variables, thereby limiting their ability to capture the heterogeneity of genotypes in our motivating application. To address this, we propose the Nested Atoms Model (NAM), a new Bayesian nonparametric approach that enables the desired two-layered clustering, accounting for both group-level and observation-level variables. To scale NAM for high-dimensional data, we develop a fast variational Bayesian inference algorithm. Simulations show that NAM outperforms existing methods that ignore group-level variables. Applied to the OneK1K dataset, NAM identifies clusters of genetically similar individuals with homogeneous cell-type profiles. The resulting cell clusters align with known immune cell types based on differential gene expression, underscoring the ability of NAM to capture nested heterogeneity and provide biologically meaningful insights.

A quantum computer capable of breaking the encryption that secures the internet now seems to be just around the corner. Stunning revelations from two research teams outline how it could happen, with one suggesting that the current largest quantum machine is already more than halfway towards the size needed. The two studies concern an encryption technique built around the elliptic curve discrete logarithm problem (ECDLP). The particulars of how this mathematical problem is solved made it a good candidate for encrypting data and led to its widespread adoption for securing lots of internet communication, including bank transactions, and nearly every major cryptocurrency, including bitcoin. It is extremely difficult for conventional computers to crack ECDLP-based encryption, but since the 1990s researchers have known that quantum computers wouldn't have the same trouble.

We present mlr3mbo, a comprehensive and modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous parallelization, input and output transformations, and robust error handling. While it can be used for many standard Bayesian optimization variants in applied settings, researchers can also construct custom BO algorithms from its flexible building blocks. In addition to an introduction to the software, its design principles, and its building blocks, the paper presents two extensive empirical evaluations of the software on the surrogate-based benchmark suite YAHPO Gym. To identify robust default configurations for both numeric and mixed-hierarchical optimization regimes, and to gain further insights into the respective impacts of individual settings, we run a coordinate descent search over the mlr3mbo configuration space and analyze its results. Furthermore, we demonstrate that mlr3mbo achieves state-of-the-art performance by benchmarking it against a wide range of optimizers, including HEBO, SMAC3, Ax, and Optuna.

While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use remains challenging. The hurdles are diverse, ranging from limited accessibility and a lack of specialized knowledge to a general mistrust of ML methods that are perceived as insufficiently physical. Here, we introduce a framework to overcome these barriers by integrating both climate science and machine learning perspectives. We find that designing easy-to-adopt emulators that address a clearly defined task and demonstrating their reliability offers a promising path for bridging the gap between our two fields.

We introduce a novel framework for uncertainty quantification in clustering. By combining the martingale posterior paradigm with density-based clustering, uncertainty in the estimated density is naturally propagated to the clustering structure. The approach scales effectively to high-dimensional and irregularly shaped data by leveraging modern neural density estimators and GPU-friendly parallel computation. We establish frequen-tist consistency guarantees and validate the methodology on synthetic and real data.

Participants are not allowed to modify the training procedure.