Gear Fitness Gear Callaway's new golf driver face combines titanium, carbon fiber, and a military-grade polymer found in an unlikely way Callaway's new Quantum drivers use the Tri-Force Face, a three-layer design bonding titanium and carbon fiber with a polymer the R&D team found in military research. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The three-layered construction allows both carbon and titanium to shine. We may earn revenue from the products available on this page and participate in affiliate programs. Golf driver faces have been almost exclusively titanium for more than three decades, with some detours into carbon fiber .

Much of the recent progress has been achieved by taking advantage of continuous relaxations of the system, which are not always available or even possible.

Plus: Meta plans to add face recognition to its smart glasses, Jared Kushner named as part of whistleblower's mysterious national security complaint, and more. The widespread protests in Iran have exposed both Tehran's brutal tactics in the streets, where state authorities have killed thousands of demonstrators since early January, and extreme measures to block access to the global internet. As it has done repeatedly in the past, the Iranian regime cut off the country's residents from the global internet during the latest anti-government uprising. But it also shut down access to the country's intranet, known as the National Information Network, which new research found is becoming a mechanism of constant and pervasive surveillance that may ultimately be the only way Iranians can get online. The last remaining major nuclear weapons treaty between the United States and Russia just expired.

While existing approaches only model finite, discrete fidelities, in practice, the feasible fidelity choice is often infinite, which can correspond to a continuous mesh spacing orfinite element length.

Table evaluated hyperparameters Dataset Nd GPR |M| - - q() - - free-form Boston 506 13 3.049 Concrete 1030 8 4.864 Ener 768 8 0.441 WineRed1599 11 0.640 Yacht308 6 0.353

We refer to this divergence as thelog-variance loss.

June Squibb sparkles opposite Cynthia Nixon in a futuristic drama, and Michelle Williams loses her way in Eugene O'Neill's Pulitzer Prize winner. Appropriately enough, Jordan Harrison's déjà-vu-inducing "Marjorie Prime" has been here before. The Off Broadway theatre Playwrights Horizons produced the poignant sci-fi play about hyperrealistic re-creations of the dead--so-called Primes, which are used as a supportive technology for the bereaved--in Anne Kauffman's spirited, delicately comic production, back in 2015. Lois Smith, then eighty-five years old, played Marjorie, a woman struggling with dementia. It's the early twenty-sixties, and so Marjorie is attended by a holographic Prime of her husband, Walter, who tells her stories from her own life.

Safe navigation is essential for autonomous systems operating in hazardous environments, especially when multiple agents must coordinate using only high-dimensional visual observations. While recent approaches successfully combine Goal-Conditioned RL (GCRL) for graph construction with Conflict-Based Search (CBS) for planning, they typically rely on static edge pruning to enforce safety. This binary strategy is overly conservative, precluding feasible missions that require traversing high-risk regions, even when the aggregate risk is acceptable. To address this, we introduce a framework for Risk-Bounded Multi-Agent Path Finding (\problem{}), where agents share a user-specified global risk budget ($Δ$). Rather than permanently discarding edges, our framework dynamically distributes per-agent risk budgets ($δ_i$) during search via an Iterative Risk Allocation (IRA) layer that integrates with a standard CBS planner. We investigate two distribution strategies: a greedy surplus-deficit scheme for rapid feasibility repair, and a market-inspired mechanism that treats risk as a priced resource to guide improved allocation. This yields a tunable trade-off wherein agents exploit available risk to secure shorter, more efficient paths, but revert to longer, safer detours under tighter budgets. Experiments in complex visual environments show that, our dynamic allocation framework achieves higher success rates than baselines and effectively leverages the available safety budget to reduce travel time.