Public transit can have significantly lower environmental impact than personal vehicles; however, it still uses a substantial amount of energy, causing air pollution and greenhouse gas emission. While electric vehicles (EVs) can reduce energy use, most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large public transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging while serving an existing fixed-route transit schedule. We present an integer program for optimal discrete-time scheduling, and we propose polynomial-time heuristic algorithms and a genetic algorithm for finding solutions for larger networks. We evaluate our algorithms on the transit service of a mid-size U.S. city using operational data collected from public transit vehicles. Our results show that the proposed algorithms are scalable and achieve near-minimum energy use.

Governor Andrew Cuomo has repeatedly scolded New Yorkers for not practicing social distancing amid the coronavirus pandemic and now an anonymous citizen has offered their services to enforce the policy. A drone was spotted flying over the East River Park in Manhattan that chastised pedestrians for walking in groups along a path. Dubbed'the Anti-COVID-19 Volunteer Drone Task Force', the unmanned aerial vehicle told groups to stay at least six-feet apart in order to'reduce the death toll and save lives.' However, it is illegal to fly drones in certain parts of the city and the Federal Aviation Administration told DailyMail.com it is'looking into the drone flight to determine if it was compliant with Federal Aviation Regulations (FAR).' WATCH: A drone was seen flying over a Manhattan park on Saturday, urging pedestrians to "maintain social distancing." A drone was spotted flying over the East River Park in Manhattan that chastised pedestrians for walking in groups along a path.

Two employees of controversial facial recognition startup Clearview AI have been found to have ties to white nationalism, according to an exhaustive report by HuffPost published on Tuesday. The report found that one investigator for the company belonged to a white nationalist group based in Washington, DC who continued to work for the company as recently as last month. Another employee had enthusiastically endorsed "Islamophobia, Eurocentrism and anti-Semitism" in online writings in 2015. Reached by The Verge, Clearview CEO Hoan Ton-That said he was unaware of the online writings and that neither employee remains with the company. "I am not a white supremacist or an anti-semite, nor am I sympathetic to any of those views," Ton-That said in a statement.

We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics. The method employs a minimum amount of data by enforcing the metriplectic structure of dissipative Hamiltonian systems in the form of the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling, GENERIC [M. Grmela and H.C Oettinger (1997). Dynamics and thermodynamics of complex fluids. I. Development of a general formalism. Phys. Rev. E. 56 (6): 6620-6632]. The method does not need to enforce any kind of balance equation, and thus no previous knowledge on the nature of the system is needed. Conservation of energy and dissipation of entropy in the prediction of previously unseen situations arise as a natural by-product of the structure of the method. Examples of the performance of the method are shown that include conservative as well as dissipative systems, discrete as well as continuous ones.

The relative spatial layout of a human and an object is an important cue for determining how they interact. However, until now, spatial layout has been used just as side-information for detecting human-object interactions (HOIs). In this paper, we present a method for exploiting this spatial layout information for detecting HOIs in images. The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object. The visual and layout modules share information through lateral connections at several stages. The model uses predictions from the layout module as a prior to the visual module and the prediction from the visual module is given as the final output. It also incorporates semantic information about the object using word2vec vectors. The proposed model reaches an mAP of 24.79% for HICO-Det dataset which is about 2.8% absolute points higher than the current state-of-the-art.

We undertake a precise study of the asymptotic and non-asymptotic properties of stochastic approximation procedures with Polyak-Ruppert averaging for solving a linear system $\bar{A} \theta = \bar{b}$. When the matrix $\bar{A}$ is Hurwitz, we prove a central limit theorem (CLT) for the averaged iterates with fixed step size and number of iterations going to infinity. The CLT characterizes the exact asymptotic covariance matrix, which is the sum of the classical Polyak-Ruppert covariance and a correction term that scales with the step size. Under assumptions on the tail of the noise distribution, we prove a non-asymptotic concentration inequality whose main term matches the covariance in CLT in any direction, up to universal constants. When the matrix $\bar{A}$ is not Hurwitz but only has non-negative real parts in its eigenvalues, we prove that the averaged LSA procedure actually achieves an $O(1/T)$ rate in mean-squared error. Our results provide a more refined understanding of linear stochastic approximation in both the asymptotic and non-asymptotic settings. We also show various applications of the main results, including the study of momentum-based stochastic gradient methods as well as temporal difference algorithms in reinforcement learning.

In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. In natural language, the meaning of phrases emerges from the combination of words. In general medicine, a diagnosis is established on the basis of the interaction of symptoms. Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.) and quantifies their importance within the aforementioned corpora. We find that interactions play an important role in those corpora. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150\% in the probability of an outcome. Furthermore, their role greatly improves the predictive power of the model. Our findings suggest that neglecting interactions when modeling real-world phenomena might lead to incorrect conclusions being drawn.

A lunar crater on the farside of the Moon could be turned into a new radio telescope resembling the Death Star from Star Wars, under new plans from NASA. Funding for the project has come from the NASA Innovative Advanced Concepts (NIAC) Programme, designed to support potentially game changing projects. If the telescope is ever built it would be the largest open radio telescope in the solar system, according to the NASA team behind the idea. The space agency says putting a radio telescope on the Moon presents'tremendous advantages' compared to Earth-based or Earth-orbiting telescopes. In this image you can see the concept of a mesh of wire covering the inside of the crater which would be installed by robots.

The fully programmable Nao robot has been used to experiment with machine ethics. In his 1942 short story'Runaround', science-fiction writer Isaac Asimov introduced the Three Laws of Robotics -- engineering safeguards and built-in ethical principles that he would go on to use in dozens of stories and novels. They were: 1) A robot may not injure a human being or, through inaction, allow a human being to come to harm; 2) A robot must obey the orders given it by human beings, except where such orders would conflict with the First Law; and 3) A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. Fittingly, 'Runaround' is set in 2015. Real-life roboticists are citing Asimov's laws a lot these days: their creations are becoming autonomous enough to need that kind of guidance.

There's no denying that artificial intelligence is having a huge impact on our lives. According to PwC, AI will add $16 trillion to the world's economy over the next 10 years as automated decision-making spreads widely. Despite this incredible impact, AI doesn't bring much value for some problems, like predicting a viral pandemic, forecasting the winner of the presidential election, or servicing clients with diverse needs, experts say. Data is, of course, the rootstock for all forms of AI, whether it takes the form of a basic search engine or a self-driving car. But it turns out that some data are quite hard to come by, even for some of the most high-impact events.