This paper considers the estimation problem of predicting unlabeled edge flows from nodal supply and demand.

We investigated some difficulties that students often face when studying linear algebra at the undergraduate level, and identified some common mistakes and difficulties they often encountered when dealing with topics that require algorithmic thinking skills such as matrix factorization. In particular, we focused on (orthogonal) diagonalization and singular value decomposition (SVD). We also offered the possibility of exploring these topics using SageMath, a Python-based free open software computer algebra system (CAS) that has been identified to be useful for assisting many students in the computational process even though its output is static by nature. We then explored dynamic ChatGPT by inquiring the chatbot about the topic, either by asking to provide an example or to solve a problem, that is by constructing an (orthogonal) diagonalization or SVD from a particular matrix. By consolidating essential concepts in linear algebra and improving computational skills through effective practice, mastering these topics would become easier and mistakes could be minimized. Static SageMath, in particular, is a great aid for calculation confirmation and handling tedious computations. Although dynamic ChatGPT is relatively unreliable for solving problems in linear algebra, the mistakes it produces could become a valuable tool for improving critical thinking skills.

The vehicles still drive with a safety driver and a software operator. Optimus Ride, an MIT spinoff, has started operating its autonomous vehicles at Paradise Valley Estates in Fairfield, California. The shuttles, which have been carrying passengers for a couple of months now, follow deployments at the Seaport District in Boston, the Halley Rise mixed-use district in Reston, Virginia, and the Brooklyn Navy Yard in New York, a 300-acre industrial park. At the moment, the vehicles still drive with two people from the company on board, a safety driver and a software operator, but the goal of the company is to be fully driverless later this year. We caught up with the company recently -- check out the video below.

Optimus Ride has already deployed its autonomous transportation systems in the Seaport area of Boston, in a mixed-use development in South Weymouth, Massachusetts, and in the Brooklyn Navy Yard, a 300-acre industrial park. Some of the biggest companies in the world are spending billions in the race to develop self-driving vehicles that can go anywhere. Meanwhile, Optimus Ride, a startup out of MIT, is already helping people get around by taking a different approach. The company's autonomous vehicles only drive in areas it comprehensively maps, or geofences. Self-driving vehicles can safely move through these areas at about 25 miles per hour with today's technology.

Self-driving vehicle company, Optimus Ride, has launched a fleet of autonomous shuttles in New York City's Brooklyn Navy Yard for what will be the city's biggest test of self-driving tech to date. According to the company, the six self-driving cars will serve passengers only on the Navy Yards' private roads as well through a loop shuttle service connecting NYC Ferry passengers from dock 72 to a gate next to Flushing Avenue. Vehicles will operated from 7 pm until 10 pm and be chaperoned by two safety attendants -- one in the drivers seat to intervene if necessary and another in the passenger seat logging the vehicles' performance. For now, the rides will be free according to The Verge, as Optimus has received $18 million in its first round of funding and is in contract with the Navy Yard for an undisclosed sum. Optimus says its expecting to service 500 passengers per day and cater to the roughly 10,000 workers that are based there.

Select commuters in New York City and Fairfield, California will have a chance to pioneer a fleet of autonomous vehicles slated to begin serving the cities later this year. Boston-based, Optimus Ride, announced that in the second quarter of 2019 it will deploy a fleet of autonomous cars at New York City's Brooklyn Navy Yard, an up-and-coming modern industrial and business park, as well as Paradise Valley Estates, a senior community in Fairfield, California. For New York, the introduction of Optimus' fully autonomous vehicles will mark the first-ever commercial self-driving car to tread in New York State where it will have a chance to offer rides to thousands of commuters. According to the company, the New York self-driving cars will help serve passengers on the Navy Yards private roads as well as'providing a loop shuttle service to connect NYC Ferry passengers to Flushing Avenue outside the Yard's perimeter.' In Paradise Valley, the cars will serve to provide potential residents of the community their own tours of the neighborhood and in the later phases of its deployment, be able to serve current residents looking to travel to destinations within the gated community.

While using fireworks to mark July Fourth dates back to 1777, one US Air Force Base is set to give their display a modern twist. Travis Air Force Base in Fairfield, California, has been working with Intel to replace explosives with 500 drones. In celebration of July Fourth and Travis Air Force Base's 75th anniversary, tonight they will take to the skies in a choreographed aerial routine that honors active military and veterans. Intel Shooting Star drones create a U.S. flag during 2018 Independence Day celebration rehearsals on June 28 at Travis Air Force Base in Fairfield, California. The display will use the same Intel Shooting Star drones seen at the Superbowl and the Winter Olympics in PyeongChang.

When experiencing or describing a new place people will often compare it against other places that they already know. However, this human attention to the simultaneous similarities and differences between places is not reflected in the design of user interfaces of current place search technologies. In this demo, we present Frankenplace, an application for doing similarity-based place search that allows users to interactively find new places based on mixtures of features drawn from different places. The features of places are derived from a combination of authoritative data sources and unstructured observation data from social media, and organized into an extensible set of layers. We demonstrate the Frankenplace interface, which lets a user build a profile of a target place by selecting the most relevant of the properties shared by known places.

The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.