When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what to optimize and how to evaluate it -- which can evolve as evidence accumulates. We introduce Generate-Select-Refine (GSR), a open-ended BO framework that alternates between task generation and task optimization. Starting from a user-provided seed task, GSR generates new tasks in a coarse-to-fine manner while a task-acquisition function schedules optimization. Asymptotically, it concentrates evaluations on the best task, incurring only logarithmic regret overhead relative to single-task BO. We apply GSR to new product development, chemical synthesis scaling, algorithm analysis, and patent repurposing, where it outperforms existing LLM-based optimizers.

As chaotic negotiations over the end of the Iran war continue, US negotiators think they have the framework for a deal in place. Now they just have to sell the president on it. President Donald Trump's negotiators face the arduous task of trying to convince the president that a deal he previously rejected is their best option in Iran . Last month, Trump initially gave his blessing for a so-called "cash for uranium" deal, under which the US would release around $20 billion in frozen funds in exchange for Iran handing over its stockpile of highly enriched uranium, sources familiar with the matter tell WIRED. Trump's negotiators, vice president JD Vance, special envoy Steve Witkoff, and Jared Kushner, Trump's son-in-law, received repeated approvals from the president while they were in Islamabad, giving them confidence a deal was close.

Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and often affected by anomalous observations caused by traffic congestion, industrial emissions, and seasonal meteorological variability. This study proposes a Graph Convolutional Support Vector Regression (GCSVR) framework for robust spatiotemporal forecasting of urban air pollution. The model combines graph convolutional learning to capture inter-station spatial dependence with support vector regression to model nonlinear temporal dynamics while reducing sensitivity to outlier observations. The proposed framework is evaluated using air quality records from 37 monitoring stations in Delhi and 18 stations in Mumbai, representing inland and coastal metropolitan environments in India. Forecasting performance is assessed across multiple horizons and compared with established temporal and spatiotemporal benchmarks. The results show that GCSVR consistently improves predictive accuracy and maintains stable performance across seasons and outlier-prone pollution episodes. Statistical test further confirms the reliability of the proposed approach across the two cities. Finally, conformal prediction is integrated with GCSVR to generate calibrated prediction intervals, enhancing its practical value for uncertainty-aware air quality monitoring and public health decision-making.

Robotically assembled building blocks could be a more environmentally friendly method for erecting large-scale structures than some existing construction techniques, according to a new study by MIT researchers. The team conducted a feasibility study to evaluate the efficiency of constructing a simple building using "voxels," which are modular 3D subunits that assemble into complex, durable structures. After studying the performance of multiple voxels, the researchers developed three new designs intended to streamline building construction. They also produced a robotic assembler and a user-friendly interface for generating voxel-based building layouts and feeding instructions to the robots. Their results indicate this voxel-based robotic assembly system could reduce embodied carbon -- all of the carbon emitted during the lifecycle of building materials -- by as much as 82 percent, compared with popular techniques like 3D concrete printing, precast modular concrete, and steel framing.

Nine people have died in an explosion at a coal mine in Colombia in the latest fatal accident to hit the country's mining sector. Emergency workers said they had rescued six miners from the shafts in Sutatausa, north of the capital, Bogotá. Colombia's national mining agency said a build-up of gases was thought to have caused the explosion at 16:00 (21:00 GMT) on Monday. It also published a list of recommendations it said it had made to the mine's operators after an inspection less than a month ago, in which it had warned of a potentially dangerous gas build-up. Many mines in Colombia are operated informally and without proper safety standards.

We study the deterministic global optimization of trained Gaussian process posterior mean functions over hyperrectangular domains. Although the posterior mean function has a compact closed-form representation, its global optimization is challenging because it remains nonlinear and nonconvex. Existing exact deterministic approaches become increasingly difficult to scale as the number of training data points grows, leading to approximation-based methods that improve tractability by optimizing a modified (inexact) objective. In this work, we propose PALM-Mean, a piecewise-analytic lower-bounding framework embedded in reduced-space spatial branch-and-bound. At each node, kernel terms that are locally important are replaced by a sign-aware piecewise-linear relaxation in an appropriate scalar distance variable, while the remaining terms are bounded analytically in closed form. We show this hybrid approach yields a valid lower bound for the posterior mean, while limiting the size of the branch-and-bound subproblems. We establish validity of the node lower bounds and $\varepsilon$-global convergence of the resulting algorithm. Computational results on synthetic benchmarks and real-world application problems show that PALM-Mean improves scalability relative to representative general-purpose deterministic global solvers, particularly as the number of training data points increases.

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 .

Do not open until July 4, 2276: U.S. buries a'zombie-proof' time capsule The durable stainless steel container will be buried in Philadelphia for the country's 250th birthday. 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 time capsule will include items from all 50 states and six territories. Breakthroughs, discoveries, and DIY tips sent six days a week. It's been 250 years since the United States decided it was no longer interested in being part of Great Britain.

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space. Although this method is studied extensively and applied on classification and regression problems, its adoption on ranking problems is sporadic, and the few existing implementations lack theoretical justifications. This paper revisits invariant representation learning for ranking. Upon reviewing prior work, we found that they implement what we call item-level alignment, which aligns the distributions of the items being ranked from all lists in aggregate but ignores their list structure. However, the list structure should be leveraged, because it is intrinsic to ranking problems where the data and the metrics are defined and computed on lists, not the items by themselves. To close this discrepancy, we propose list-level alignment--learning domain-invariant representations at the higher level of lists. The benefits are twofold: it leads to the first domain adaptation generalization bound for ranking, in turn providing theoretical support for the proposed method, and it achieves better empirical transfer performance for unsupervised domain adaptation on ranking tasks, including passage reranking.