Many users consult digital archives daily, but the information they can access is unrepresentative of the diversity of documentary history.The sequence-to-sequence architecture typically used for optical character recognition (OCR) -which jointly learns a vision and language model -is poorly extensible to lowresource document collections, as learning a language-vision model requires extensive labeled sequences and compute.This study models OCR as a character level image retrieval problem, using a contrastively trained vision encoder.Because the model only learns characters' visual features, it is more sample efficient and extensible than existing architectures, enabling accurate OCR in settings where existing solutions fail.Crucially, it opens new avenues for community engagement in making digital history more representative of documentary history.

Existing full text datasets of U.S. public domain newspapers do not recognize the often complex layouts of newspaper scans, and as a result the digitized content scrambles texts from articles, headlines, captions, advertisements, and other layout regions. OCR quality can also be low. This study develops a novel, deep learning pipeline for extracting full article texts from newspaper images and applies it to the nearly 20 million scans in Library of Congress's public domain Chronicling America collection. The pipeline includes layout detection, legibility classification, custom OCR, and association of article texts spanning multiple bounding boxes. To achieve high scalability, it is built with efficient architectures designed for mobile phones. The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge. The dataset could also be added to the external database of a retrieval-augmented language model to make historical information - ranging from interpretations of political events to minutiae about the lives of people's ancestors - more widely accessible. Furthermore, structured article texts facilitate using transformer-based methods for popular social science applications like topic classification, detection of reproduced content, and news story clustering. Finally, American Stories provides a massive silver quality dataset for innovating multimodal layout analysis models and other multimodal applications.

The increasing demand to provide new network services in a timely and efficient manner is driving the need to design, test and deploy networks quickly and consistently. Testing and verifying at scale is a challenge: network equipment is expensive, requires space, power and cooling, and there is never enough test equipment for everyone who wants to use it! Network virtualization technologies enable a flexible environment for educators, researchers, and operators to create functional models of current, planned, or theoretical networks. This demonstration will show VIRL --- the Virtual Internet Routing Lab --- a platform that can be used for network change validation, training, education, research, or network-aware applications development. The platform combines network virtualization technologies with virtual machines (VMs) running open-source and commercial operating systems; VM orchestration capabilities; a context-aware configuration engine; and an extensible data-collection framework. The system simplifies the process to create both simple and complex environments, run simulations, and collect measurement data.

Robotic tasks in space require manipulation of massive objects capable of attaining large momentum. The momentum can pose hazardous conditions and introduce destabilizing effects on a space platform. Consequently, a technique for limiting the momentum applied to objects under manipulation subject to arbitrary velocity input commands is proposed. The algorithm does not require mass position or inertia information about the object, and it takes actuator limitations into account in forming the momentum limits. To evaluate the probability that a velocity trajectory will fall within the momentum bounds, reliability theory is employed. This enables autonomously generated trajectories to be validated for compliance with momentum limits.>