130K likes, 2,245 comments - jerry_truck_seller on April 21, 2026: "Shacman 7.2 meters truck,Your best Workhorse.#usedtrucks #truck #shacman #truckforsale".
5000 Feet is the Best
5000 FEET IS THE BEST is based on two meetings with a former drone operator which were recorded in a hotel in Las Vegas in september 2010. On camera, the drone operator agreed to discuss the
Week 9: Euro Truck Simulator - Alex's Blog
Westland RV | Lightweight & High-Quality Truck Campers
Westland RV crafts lightweight, economical truck campers for comfort & adventure. 30+ years of quality—now proudly operating in Lethbridge, AB!
Matt Jones on Instagram: "A trio of CSX Geeps lead M780 North through the massive Big Hormady Cut at Tantallon, TN. Even with the Cowan pusher shoving on the bottom the 39 car local struggled up the steep mountain grade. #railphotos_usa #railsupremacy #trb_express #pocket_rail #daily_crossing #railroadphotography #railroad #RailfanDepot #railfan #railfans_of_instagram #railfannation #trains_magazine #trains_worldwide #tennessee #trainsofinstagram #travel #trains #rock #fall #mountains #canonusa #canonphoto #canon"
1,274 likes, 16 comments - rusty_rail_junkie on October 26, 2021: "A trio of CSX Geeps lead M780 North through the massive Big Hormady Cut at Tantallon, TN. Even with the Cowan pusher shoving on the bottom the 39 car local struggled up the steep mountain grade. #railphotos_usa #railsupremacy #trb_express #pocket_rail #daily_crossing #railroadphotography #railroad #RailfanDepot #railfan #railfans_of_instagram #railfannation #trains_magazine #trains_worldwide #tennessee #trainsofinstagram #travel #trains #rock #fall #mountains #canonusa #canonphoto #canon".
Kinomap
Kinomap is the world's largest geolocated video sharing platform, with thousands of videos from the best tracks around the world. We pair to your equipment ...
12-mile Middle Earth Hexmap
I've been re-reading the Hobbit & Lord of The Rings this year, so naturally my latest cartographic hyperfixation was to make a map of Tolkien's paracosm. ...
Through a tight rocky area in Tantallon, Tennessee
633K subscribers in the trains community. The Home for all things "Iron Horse". Steam, Diesel, Electric, Pneumatic, Hydraulic. It doesn't matter, let…
Joe Truzman on Twitter / X
I have seen parts of ballistic missiles sold on local online market places but this is the first time I have observed a kid attempt to sell a drone on TikTok. I assume this is somewhere in Iraq. pic.twitter.com/XyJfVTsI55— Joe Truzman (@JoeTruzman) April 4, 2026
Humpty Dumpty in Oakland — Oakland Public Library
Humpty Dumpty in Oakland — Dick, Philip K. — In 1950s San Francisco, elderly garage owner Jim Ferguson prepares to retire and sell his business, but when he is offered the deal of a lifetime by record-company owner Chris Harman, Al Miller, one of Jim's mechanics who thinks that Harman is a crook, sets out to protect his mentor.
The Deadly Rise of Giant Trucks and S.U.V.s
The vehicles on American roads have grown larger — and they are killing thousands more pedestrians, a Times investigation found.
Army acquires 43 drones, wings 46 Turkey-trained personnel
The Federal Government has bolstered the Nigerian Army’s operational capacity with the acquisition of 43 Bayraktar TB2 drones, primarily for deployment in
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Welcome to Bigfoot RV! Explore our Truck Campers & Travel Trailers here on our site. Choose the right model for your adventuring plans, whether your dream vacation is lounging around an RV Resort or exploring a road less traveled.
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value
Data valuation is a powerful framework for providing statistical insights into which data are beneficial or detrimental to model training. Many Shapley-based data valuation methods have shown promising results in various downstream tasks, however, they are well known to be computationally challenging as it requires training a large number of models. As a result, it has been recognized as infeasible to apply to large datasets. To address this issue, we propose Data-OOB, a new data valuation method for a bagging model that utilizes the out-of-bag estimate. The proposed method is computationally efficient and can scale to millions of data by reusing trained weak learners. Specifically, Data-OOB takes less than $2.25$ hours on a single CPU processor when there are $10^6$ samples to evaluate and the input dimension is $100$. Furthermore, Data-OOB has solid theoretical interpretations in that it identifies the same important data point as the infinitesimal jackknife influence function when two different points are compared. We conduct comprehensive experiments using 12 classification datasets, each with thousands of sample sizes. We demonstrate that the proposed method significantly outperforms existing state-of-the-art data valuation methods in identifying mislabeled data and finding a set of helpful (or harmful) data points, highlighting the potential for applying data values in real-world applications.
Data Shapley in One Training Run
Data Shapley offers a principled framework for attributing the contribution of data within machine learning contexts. However, the traditional notion of Data Shapley requires re-training models on various data subsets, which becomes computationally infeasible for large-scale models. Additionally, this retraining-based definition cannot evaluate the contribution of data for a specific model training run, which may often be of interest in practice. This paper introduces a novel concept, In-Run Data Shapley, which eliminates the need for model retraining and is specifically designed for assessing data contribution for a particular model of interest. In-Run Data Shapley calculates the Shapley value for each gradient update iteration and accumulates these values throughout the training process. We present several techniques that allow the efficient scaling of In-Run Data Shapley to the size of foundation models. In its most optimized implementation, our method adds negligible runtime overhead compared to standard model training. This dramatic efficiency improvement makes it possible to perform data attribution for the foundation model pretraining stage. We present several case studies that offer fresh insights into pretraining data's contribution and discuss their implications for copyright in generative AI and pretraining data curation.
