You totally prepared for this trip. You booked the flights months ago. You have all the best sights saved in Google Maps. But then you land in Munich or Kigali or Buenos Aires and realize you can't even identify the sign pointing toward baggage claim, much less tell your cabbie where you're headed. Luckily your phone can now do those things for you.

A San Francisco nonprofit is using a fascinating mix of machine learning and solar panel technology to help the fight against deforestation in Brazil. Rainforest Connection, led by founder and CEO Topher White, creates devices called Guardians that listen to the rainforest and send real-time alerts to combat illegal logging. White's startup places sensors high in the canopy of the Amazon Rainforest in Pará, northern Brazil. The devices are powered by solar panels and built using modified cellphones. Using machine learning and "bio-acoustic monitoring," White analyzes the noises recorded by the sensors, singles out these sounds, and pinpoints the location sent by the Guardian device.

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior on the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications: we demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard "fairness" assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special "structural" properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.

Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year's worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives.

If you're in the market to buy fresh papayas, it can be a challenge to figure out ripeness based on peel color without also squeezing the fruit to test for softness. A Brazilian research group could make life easier for both shoppers and producers in the near future with a computer vision algorithm that estimates ripeness based on images alone. Last year, the United States alone imported more than US $107 million worth of fresh papayas as the world's largest papaya import market. The computer vision software could enable papaya growers to maximize the value of their fruit by sending the ripest papayas to local markets and saving less ripe papayas for export, says Douglas Fernandes Barbin, a researcher in the department of food engineering at the University of Campinas in São Paulo, Brazil. But he and his colleagues also want to help individual shoppers get their money's worth in grocery aisles.

The anatomical structure of the mummies discovered near Nazca, in southern Peru, last year is not human, Russian scientists who were studying the case said. A team of researchers from St. Petersburg, who collected the tissue samples in Peru and brought them back to Russia to study, have determined that the mummies -- with elongated heads and three fingers on each hand -- possess 23 chromosomes that are human but lack the human anatomy. One of the mummies is named Maria, a 5th-century woman believed to predate the arrival of Europeans in America. Scientists believe that she belonged to a race that was wiped from existence due to a flood or a comet strike. Apart from Maria, the scientists are also studying a nine-year-old mummy called Vavita, and four other male mummies.

SAN FRANCISCO: There's a dirty little secret about artificial intelligence: It's powered by hundreds of thousands of real people. From makeup artists in Venezuela to women in conservative parts of India, people around the world are doing the digital equivalent of needlework _drawing boxes around cars in street photos, tagging images, and transcribing snatches of speech that computers can't quite make out. Such data feeds directly into machine learning'' algorithms that help self-driving cars wind through traffic and let Alexa figure out that you want the lights on. These repetitive tasks pay pennies apiece. But in bulk, this work can offer a decent wage in many parts of the world _ even in the U.S.

Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been reported to yield promising results. However, the majority of ensemble methods applied too imbalanced learning are static ones. Moreover, they only deal with binary imbalanced problems. Hence, this paper presents an empirical analysis of dynamic selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and four dynamic selection methods. Our experiments conducted on 26 multi-class imbalanced problems show that the dynamic ensemble improves the F-measure and the G-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases.

Does it ever seem like your dog is in tune with your emotions? You may be on to something. In new experiments, dogs showed signs of understanding whether a human or a dog was happy or mad based on facial expressions and vocalizations. The research, published in the journal Biology Letters, set out to explore the emotional connection between man and his best friend. "We still didn't know if the dogs ... can somehow understand that, say, a happy facial expression is positive and a negative facial expression is negative," says study leader Natalia de Souza Albuquerque, a Ph.D. student in experimental psychology at the University of São Paulo in Brazil.