Technology Engineering World's largest steam locomotive heads out on tour Union Pacific's Big Boy No. 4014 will travel coast-to-coast in commemoration of the semiquincentennial. Breakthroughs, discoveries, and DIY tips sent six days a week. The world's largest operating steam locomotive is hitting the road--or tracks . Union Pacific's Big Boy No. 4014 is heading out on its first coast-to-coast steam tour to celebrate the United States' 250th anniversary . The first leg begins on March 29, when Big Boy and other historical passenger cars from Union Pacific's Heritage Fleet will travel from the locomotive's home base in Cheyenne, Wyoming, west towards California.

Exudative (wet) age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, typically treated with intravitreal injections. Emerging therapies, such as subretinal injections of stem cells, gene therapy, small molecules or RPE cells require precise delivery to avoid damaging delicate retinal structures. Autonomous robotic systems can potentially offer the necessary precision for these procedures. This paper presents a novel approach for motion compensation in robotic subretinal injections, utilizing real-time Optical Coherence Tomography (OCT). The proposed method leverages B$^{5}$-scans, a rapid acquisition of small-volume OCT data, for dynamic tracking of retinal motion along the Z-axis, compensating for physiological movements such as breathing and heartbeat. Validation experiments on \textit{ex vivo} porcine eyes revealed challenges in maintaining a consistent tool-to-retina distance, with deviations of up to 200 $\mu m$ for 100 $\mu m$ amplitude motions and over 80 $\mu m$ for 25 $\mu m$ amplitude motions over one minute. Subretinal injections faced additional difficulties, with horizontal shifts causing the needle to move off-target and inject into the vitreous. These results highlight the need for improved motion prediction and horizontal stability to enhance the accuracy and safety of robotic subretinal procedures.

Open-source large language models are becoming increasingly available and popular among researchers and practitioners. While significant progress has been made on open-weight models, open training data is a practice yet to be adopted by the leading open-weight models creators. At the same time, there researchers are working to make language models safer. We propose a data curation pipeline to reduce harmful outputs by models trained on public domain data. There are unique challenges to working with public domain data, as these sources differ from web text in both form and content. Many sources are historical documents and are the result of Optical Character Recognition (OCR). Consequently, current state-of-the-art approaches to toxicity filtering are often infeasible or inappropriate for open data models. In this paper, we introduce a new fully open-source pipeline for open-data toxicity filtering. Our contributions are threefold. We create a custom training dataset, ToxicCommons, which is composed of texts which have been classified across five different dimensions (racial/origin-based, gender/sex-based, religious, ability-based discrimination, and violence). We use this dataset to train a custom classifier, Celadon, that can be used to detect toxic content in open data more efficiently at a larger scale. Finally, we describe the balanced approach to content filtration that optimizes safety filtering with respect to the filtered data available for training.

Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers' weblinks, manufacturing services, certifications, and location entity types.

Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper, we propose a unified model to perform language-based colorization with any-level descriptions. We leverage the pretrained cross-modality generative model for its robust language understanding and rich color priors to handle the inherent ambiguity of any-level descriptions. We further design modules to align with input conditions to preserve local spatial structures and prevent the ghosting effect. With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios. Extensive experimental results demonstrate our advantages of effectively handling any-level descriptions and outperforming both language-based and automatic colorization methods. The code and pretrained models are available at: https://github.com/changzheng123/L-CAD.

We present a robust markerless image based visual servoing method that enables precision robot control without hand-eye and camera calibrations in 1, 3, and 5 degrees-of-freedom. The system uses two cameras for observing the workspace and a combination of classical image processing algorithms and deep learning based methods to detect features on camera images. The only restriction on the placement of the two cameras is that relevant image features must be visible in both views. The system enables precise robot-tool to workspace interactions even when the physical setup is disturbed, for example if cameras are moved or the workspace shifts during manipulation. The usefulness of the visual servoing method is demonstrated and evaluated in two applications: in the calibration of a micro-robotic system that dissects mosquitoes for the automated production of a malaria vaccine, and a macro-scale manipulation system for fastening screws using a UR10 robot. Evaluation results indicate that our image based visual servoing method achieves human-like manipulation accuracy in challenging setups even without camera calibration.

Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.

A veteran California Highway Patrol officer is on administrative leave after being arrested this week on suspicion of transmitting child pornography. Placer County sheriff's detectives arrested Patrick Francis Cooney Jr., around 4 p.m. Monday. An ongoing investigation revealed that Cooney, 54, allegedly sent child pornography that depicts sexual acts involving persons under 18, officials said. Authorities searched his Auburn residence and seized his electronic devices. "CHP is aware of the arrest of one of its officers by the Placer County Sheriff Department for alleged behavior that occurred while the employee was off duty," Chief Janice Mulanix of the CHP Valley Division in Sacramento said in a statement.

The last time state lawmakers tried to place limits on drones in the skies above California, they were met with the veto of Gov. Jerry Brown, who said he did not want to create new crimes to enforce bans on the use of such devices. This year, the pushback to new rules is coming not from the governor but through the lobbying efforts of a budding industry that hopes to influence policy at the state Capitol and nationwide. As drones multiply in number and category, cities and states want to set boundaries. But drone manufacturers and associations this legislative session boosted their politicking, successfully beating back several bills they said would create a patchwork of laws that vary by state and hinder innovation. "We want to solve problems and address concerns, but to do it in a way that is constantly clear across the country," said Brendan Schulman, vice president of policy and legal affairs for DJI Technology Co., the world's largest drone maker. "Otherwise, it will be too confusing for commercial users and consumers to understand what the rules are when they travel from place to place."

A Placer County man was taken into custody Friday on suspicion of flying a drone into a firefighting effort northeast of Sacramento. Information posted on social media helped lead to the arrest of Eric Wasser, 57, of Foresthill. Cal Fire officers took him into custody, booked him into the Placer County Jail in Auburn and charged him with interfering with firefighting operations, a misdemeanor. He also could face prosecution from federal authorities for violating restricted air space, said Daniel Berlant, a spokesman for the California Department of Forestry and Fire Protection. Berlant said the arrest over a drone's interference with firefighting was a first in California. The presence of the drone prompted authorities to ground aircraft for 30 minutes the evening of June 28, the day that the Trailhead fire broke out.