Existing Spatial Interaction Models (SIMs) are limited in capturing the complex and context-aware interactions between business clusters and trade areas. To address the limitation, we propose a SIM-GAT model to predict spatiotemporal visitation flows between community business clusters and their trade areas. The model innovatively represents the integrated system of business clusters, trade areas, and transportation infrastructure within an urban region using a connected graph. Then, a graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the complexity and interdependencies of business clusters. We developed this model with data collected from the Miami metropolitan area in Florida. We then demonstrated its effectiveness in capturing varying attractiveness of business clusters to different residential neighborhoods and across scenarios with an eXplainable AI approach. We contribute a novel method supplementing conventional SIMs to predict and analyze the dynamics of inter-connected community business clusters. The analysis results can inform data-evidenced and place-specific planning strategies helping community business clusters better accommodate their customers across scenarios, and hence improve the resilience of community businesses.

COVID-19 has impacted nations differently based on their policy implementations. The effective policy requires taking into account public information and adaptability to new knowledge. Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (APC). Among the core challenges to be overcome include (a) inability to handle a high degree of non-homogeneity in different contributing features across the pandemic timeline, (b) lack of an approach that enables adaptive incorporation of public health expert knowledge, and (c) transparent models that enable understanding of the decision-making process in suggesting policy. In this work, we take the early steps to address these challenges using Knowledge Infused Policy Gradient (KIPG) methods. Prior work on knowledge infusion does not handle soft and hard imposition of varying forms of knowledge in disease information and guidelines to necessarily comply with. Furthermore, the models do not attend to non-homogeneity in feature counts, manifesting as partial observability in informing the policy. Additionally, interpretable structures are extracted post-learning instead of learning an interpretable model required for APC. To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner. The study establishes a theory for imposing hard and soft constraints and simulates it through experiments. In comparison with knowledge-intensive baselines, we show quick sample efficient adaptation to new knowledge and interpretability in the learned policy, especially in a pandemic context.

The novel coronavirus disease (COVID-19) has crushed daily routines and is still rampaging through the world. Existing solution for nonpharmaceutical interventions usually needs to timely and precisely select a subset of residential urban areas for containment or even quarantine, where the spatial distribution of confirmed cases has been considered as a key criterion for the subset selection. While such containment measure has successfully stopped or slowed down the spread of COVID-19 in some countries, it is criticized for being inefficient or ineffective, as the statistics of confirmed cases are usually time-delayed and coarse-grained. To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks, where the results demonstrate the efficiency and effectiveness of C-Watcher for early detection of high-risk neighborhoods from a large number of cities.

I recently had the opportunity to take a ride in a Waymo self-driving car in Chandler, AZ. I had been looking forward to this experience, not only to see how well the technology worked but also what the experience might be like as a passenger. Upon my arrival at the Waymo facility, I had apparently approached the side of the building where the Waymo cars go at the end of their duty cycles to be refueled and inspected. As I drove in, I was more or less surrounded by incoming Waymo vehicles. I relaxed as they navigated their way around me.

Your dog remembers more than you might think. A new study that tested the memory of man's best friend found that dogs exhibit something akin to episodic memory -- a process that's been well documented in humans, but difficult to prove in other animals. In experiments, the dogs were able to recall human actions even when they weren't expecting to be tested on what they observed, according to a report published Wednesday in the journal Current Biology. The findings show that episodic memory, thought to be linked to self-awareness, may extend well beyond humans to species outside of the primate lineage. Scientists have long wondered whether other animals have something like episodic memory, which allows us to recall specific past events even though they may not have been particularly important when they happened.

In research conducted in China, a rhesus monkey whose spinal cord was partially severed quickly regained lost control over his paralyzed leg after researchers implanted a signal-emitting electronic array below the site of the spinal injury. That pulse generator sent out electrical signals to the monkey's leg to move, and the monkey's affected leg responded as early as six days after his spinal cord was deliberately injured. The signals to move were commands collected from the motor cortex of unharmed rhesus monkeys as they freely walked and used their legs. Together, the two devices leaped over the broken connection between brain and limb, allowing the partially paralyzed monkey to mimic key walking motions. The brain-spine interface offers new hope that patients who have lost function due to spinal cord injury might be able to restore movement and prevent the degeneration of the neural wiring that is needed for an eventual return to movement.

The underwater lake, discovered 3,300 feet below the surface of the Gulf of Mexico, is a pit of super-salty water and dissolved methane that kills any critter unlucky enough to fall inside. The discovery was made last year by a San Pedro-based research vessel, the E/V Nautilus. In the video, scientists excitedly navigate a remotely operated vehicle, the Hercules, above the circular pool. They point out the "pickled crabs" that succumbed to the elements. "These larger organisms really don't like to be in this fluid -- or maybe they just come here to die," Scott Wankel, a marine chemist, says on the video.