Virginia's seventeenth- and eighteenth-century land patents survive primarily as narrative metes-and-bounds descriptions, limiting spatial analysis. This study systematically evaluates current-generation large language models (LLMs) in converting these prose abstracts into geographically accurate latitude/longitude coordinates within a focused evaluation context. A digitized corpus of 5,471 Virginia patent abstracts (1695-1732) is released, with 43 rigorously verified test cases serving as an initial, geographically focused benchmark. Six OpenAI models across three architectures (o-series, GPT-4-class, and GPT-3.5) were tested under two paradigms: direct-to-coordinate and tool-augmented chain-of-thought invoking external geocoding APIs. Results were compared with a GIS-analyst baseline, the Stanford NER geoparser, Mordecai-3, and a county-centroid heuristic. The top single-call model, o3-2025-04-16, achieved a mean error of 23 km (median 14 km), outperforming the median LLM (37.4 km) by 37.5%, the weakest LLM (50.3 km) by 53.5%, and external baselines by 67% (GIS analyst) and 70% (Stanford NER). A five-call ensemble further reduced errors to 19 km (median 12 km) at minimal additional cost (approx. USD 0.20 per grant), outperforming the median LLM by 48.6%. A patentee-name-redaction ablation increased error by about 9%, indicating reliance on textual landmark and adjacency descriptions rather than memorization. The cost-efficient gpt-4o-2024-08-06 model maintained a 28 km mean error at USD 1.09 per 1,000 grants, establishing a strong cost-accuracy benchmark; external geocoding tools offered no measurable benefit in this evaluation. These findings demonstrate the potential of LLMs for scalable, accurate, and cost-effective historical georeferencing.

Given the ever-increasing size of modern neural networks, the significance of sparse architectures has surged due to their accelerated inference speeds and minimal memory demands. When it comes to global pruning techniques, Iterative Magnitude Pruning (IMP) still stands as a state-of-the-art algorithm despite its simple nature, particularly in extremely sparse regimes. In light of the recent finding that the two successive matching IMP solutions are linearly connected without a loss barrier, we propose Sparse Weight Averaging with Multiple Particles (SWAMP), a straightforward modification of IMP that achieves performance comparable to an ensemble of two IMP solutions. For every iteration, we concurrently train multiple sparse models, referred to as particles, using different batch orders yet the same matching ticket, and then weight average such models to produce a single mask. We demonstrate that our method consistently outperforms existing baselines across different sparsities through extensive experiments on various data and neural network structures.

Create unique AI artwork using text, phrases, images & presets, share, download, print & mint as NFTs

On Saturday, Mojang held its annual Minecraft Live fan convention. As in years past, the event saw the studio detail the future of its immensely popular sandbox game. And if you're a fan of Minecraft, the livestream did not disappoint. The studio kicked off the event with the announcement of The Wild Update. Set to come out sometime in 2022, Mojang promises this latest DLC will change how players explore and interact with the game's overworld.

It's hard to love a swamp. Mountains have majesty, deserts have mystery, but what do wetlands have? I imagine some Joe on the street would boil it down to something like "muck, malaria, and mosquitoes." Even so, Famia Mercius, an antiquarian who's a great admirer of the Elder Scrolls series' lizard-like Argonians, is trying to get me to love the surrounding marsh as she does. She slaps a gnat off her neck while in the middle of a giddy introduction, and her stone house suggests she retains some reservations about living like the locals.

On The Tonight Show, in March 1978, the late astronomer Carl Sagan had lots to talk about. He had just published Dragons of Eden: Speculations on the Evolution of Human Intelligence--which would win the Pulitzer Prize--and Star Wars, released the year before, still captivated the public's imagination. When Johnny Carson, the show's then-host, asked Sagan to expand on some comments he'd made prior to the evening, about the film's indifference to scientific accuracy, Sagan said the "11-year-old in me loved" it, but it "could have made a better effort to do things right." His critique would resonate today: After making the biological point that the Star Wars scenario--humans evolving long ago, in a faraway galaxy--is vastly improbable, Sagan said there's another problem: "They're all white." Carson, pushing back a bit, said, "They did have a scene in Star Wars with a lot of strange characters."

We explore a symmetry-based reformulation technique which can speed up optimal pathfinding on undirected uniform-cost grid maps by over 30 times. Our offline approach decomposes grid maps into a set of empty rectangles, removing from each all interior nodes and possibly some from along the perimeter. We then add macro-edges between selected pairs of remaining perimeter nodes to facilitate provably optimal traversal through each rectangle. To further speed up search, we also develop a novel online pruning technique. Our algorithm is fast, memory efficient and retains both optimality and completeness during search.

Search algorithms often suffer from exploring areas which eventually are not part of the shortest path from the start to a goal. Usually it is the purpose of the heuristic function to guide the search algorithm such that it will ignore as much as possible of these areas. We consider other, non-heuristic methods that can be used to prune the search space to make search even faster. We present two algorithms: one for search in graphs that fit in memory, and in which we will need to perform many searches, and another, which improves the search time of planning problems that contain symmetries.

In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include "swamps" - areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an algorithm for detecting hierarchies of swamps, and exploiting them. Experiments support our claims of improved efficiency, showing significant reduction in search time.

However, there are many domains, work that is perhaps closest to ours is the "dead-end heuristic" such as map-based searches (common in GPS navigation, introduced by Björnsson and Halldórsson (2006). They computer games, and robotics) where the entire use a preprocessing phase to identify areas that are deadends, state-space is given explicitly. Optimal paths for such domains and create an abstract graph whose nodes are these can be found relatively quickly with simple heuristics, areas. Initially, the search is performed on the abstracted especially when compared to the time it takes to explore graph. The areas that were not visited during the search exponentially large combinatorial problems. Relative on the abstracted graph are then ignored when the search is quickness, however, might still not be fast enough in certain performed in the original search space. In addition to identifying real-time applications, where further improvement towards dead-ends, our approach also identifies (and prunes, high-speed performance is especially valued.