A social media platform designed for America's military community is now equipped with a custom machine learning model that insiders say can rapidly review public posts and pinpoint those that show signs and risks of potential self-harm. With support from the Veterans Affairs Department and Harvard University's Nock Lab, Amazon Web Services linked up with the existing RallyPoint military social media platform to target the production of a technological solution that can speedily surface sensitive public posts and boost online suicide intervention. "Historically, the heavy lifting of mental health support on RallyPoint has been shouldered by RallyPoint members stepping up to help each other when they come across people sharing their challenges on our site," RallyPoint CEO Dave Gowel recently told Nextgov. "Now, through our work with the VA, AWS and mental health experts from Harvard, we are more proactive in reinforcing our members' good work by offering helpful resources when we are alerted about public posts showing signs of risk." Launched in 2012, RallyPoint enables nearly 2 million service members, veterans, and their families to connect, share stories and information, ask questions and ultimately chat on topics that accompany military and veteran life.

It turns out humans are not the only creatures that use food as foreplay. Researchers in southwestern Mexico have recorded a group of rough-toothed dolphins sharing a meal and getting frisky. A drone camera caught two dolphins passing a piece of fish back and forth in what may be the first video of the conduct. The repast seemed to inspire some amorous behavior, as well, with two males initiating sexual encounters with another member of the pod. Rough-toothed dolphins spend up to 80 percent of their time in the ocean depths, making them extremely difficult to study.

Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka et al. (2015, 2016a, 2016b, 2017) develop a procedure for causal feature learning (CFL) in an effort to automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments.

The ocean is filled with microscopic microalgae called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton is flow cytometry, which measures the optical properties of thousands of individual cells per second. Today, oceanographers are able to collect flow cytometry data in real-time onboard a moving ship, providing them with fine-scale resolution of the distribution of phytoplankton across thousands of kilometers. One of the current challenges is to understand how these small and large scale variations relate to environmental conditions, such as nutrient availability, temperature, light and ocean currents. In this paper, we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations. We demonstrate the usefulness and interpretability of the approach using both synthetic data and real observations collected on an oceanographic cruise conducted in the north-east Pacific in the spring of 2017.

"We head to the Pacific northwest to understand the obstacles that confront these endangered orcas and how researchers are using artificial intelligence to help orcas and humans to coexist. WHAT HAPPENED TO J thirty five or Tala wasn't an anomaly the southern resident cavs have been struggling to survive for some time they've been listed as endangered in both the US and Canada since the mid arts. But their numbers continue to fall in two, thousand five there were eight. Now there are just seventy two in the wild one lives in captivity. Their home waters in the sailor, see an elaborate network of channels that span the coasts of Seattle Vancouver from Olympia Washington in the south to the middle of Vancouver Island British Columbia in the north. The see encompasses puget sound the Strait of Georgia and the Strait of Juan De. Much of it is rich in natural beauty and teeming with wildlife with rural shorelines backlit by tall evergreens and craggy.

We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which is made available by smart city infrastructure such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility. To capture cyclists' route choices, we develop a bilevel program based on the Multinomial Logit model. We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route choice based planning model as a mixed integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists' route choices. The proposed framework drives the data-driven urban planning scheme in smart city operations management.

Fox Business Flash top headlines are here. Check out what's clicking on FoxBusiness.com. The U.S. Navy could possibly operate thousands of combat ships in the coming years as the service seeks to combine surface, air and undersea drones into its fleet. It is part of a formal Integrated Force Structure Assessment in which analysis teams led by the chief of naval operations and Marine Corps commandant explored questions of fleet size in relation to fast-emerging man-unmanned teaming integration. "[W]e came up with a discreet number of ships which was more than 355 and then command and control drone networking separately unmanned," Admiral Michael Gilday, chief of naval operations, said earlier this year at the Navy's 2020 West Conference in San Diego, California. The assessment, Gilday explained, was not so much "coordinated" as "integrated," taking up a blend between a specific number of planned manned ships and a still "conceptual" number of drones.

Nitin Alabur is an iOS developer from India who lived in the US and dreamed of creating a tech startup. "I had a zillion ideas," he tells me. But he'd been hired by a US firm under an H-1B visa, which ties you to your employer. A green card that would make self-employment possible was years away. "It felt like shackles," he says.

Bike sharing systems (BSS) have been a popular traveling service for years and are used worldwide. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and greenhouse gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. Some studies propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Other studies propose to extend the approach by including weather data. This study aims to extend upon these proposals and opportunities to explore how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm. K-means managed to identify three clusters that correspond to bike usage depending on weather conditions. The results show that the weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables.

For 23 years, Larry Collins worked in a booth on the Carquinez Bridge in the San Francisco Bay Area, collecting tolls. The fare changed over time, from a few bucks to $6, but the basics of the job stayed the same: Collins would make change, answer questions, give directions and greet commuters. "Sometimes, you're the first person that people see in the morning," says Collins, "and that human interaction can spark a lot of conversation." But one day in mid-March, as confirmed cases of the coronavirus were skyrocketing, Collins' supervisor called and told him not to come into work the next day. The tollbooths were closing to protect the health of drivers and of toll collectors. Going forward, drivers would pay bridge tolls automatically via FasTrak tags mounted on their windshields or would receive bills sent to the address linked to their license plate. Collins' job was disappearing, as were the jobs of around 185 other toll collectors at bridges in Northern California, all to be replaced by technology.