Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an understanding of the features influencing the prediction is necessary to ensure fairness, safety, and legal compliance. In this paper we consider one class of such tasks, tabular dataset classification, and propose a novel neuro-symbolic architecture, Neural Reasoning Networks (NRN), that is scalable and generates logically sound textual explanations for its predictions. NRNs are connected layers of logical neurons which implement a form of real valued logic. A training algorithm (R-NRN) learns the weights of the network as usual using gradient descent optimization with backprop, but also learns the network structure itself using a bandit-based optimization. Both are implemented in an extension to PyTorch (https://github.com/IBM/torchlogic) that takes full advantage of GPU scaling and batched training. Evaluation on a diverse set of 22 open-source datasets for tabular classification demonstrates performance (measured by ROC AUC) which improves over multi-layer perceptron (MLP) and is statistically similar to other state-of-the-art approaches such as Random Forest, XGBoost and Gradient Boosted Trees, while offering 43% faster training and a more than 2 orders of magnitude reduction in the number of parameters required, on average. Furthermore, R-NRN explanations are shorter than the compared approaches while producing more accurate feature importance scores.

There is a limitation in the literature of data-driven analyses for the ex-post evaluation of community risk and resilience, particularly using features related to the performance of coupled human-infrastructure systems. To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience and their interplay based on features related to the coupled human-infrastructure systems performance. Utilizing feature groups related to population protective actions, infrastructure/building performance features, and recovery features, we examined the risk and resilience performance of communities in the context of the 2017 Hurricane Harvey in Harris County, Texas. These features related to the coupled human-infrastructure systems performance were processed using the K-means clustering method to classify census block groups into four distinct clusters then, based on feature analysis, these clusters were labeled and designated into four quadrants of risk-resilience archetypes. Finally, we analyzed the disparities in risk-resilience status of spatial areas across different clusters as well as different income groups. The findings unveil the risk-resilience status of spatial areas shaped by their coupled human-infrastructure systems performance and their interactions. The results also inform about features that contribute to high resilience in high-risk areas. For example, the results indicate that in high-risk areas, evacuation rates contributed to a greater resilience, while in low-risk areas, preparedness contributed to greater resilience.

The recent outbreak of the COVID-19 shocked humanity leading to the death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US, employed different strategies including the mask mandate (MM) order issued by the states' governors. Although most of the previous studies pointed in the direction that MM can be effective in hindering the spread of viral infections, the effectiveness of MM in reducing the degree of exposure to the virus and, consequently, death rates remains indeterminate. Indeed, the extent to which the degree of exposure to COVID-19 takes part in the lethality of the virus remains unclear. In the current work, we defined a parameter called the average death ratio as the monthly average of the ratio of the number of daily deaths to the total number of daily cases. We utilized survey data provided by New York Times to quantify people's abidance to the MM order. Additionally, we implicitly addressed the extent to which people abide by the MM order that may depend on some parameters like population, income, and political inclination. Using different machine learning classification algorithms we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. Our results showed a promising score as high as 0.94 with algorithms like XGBoost, Random Forest, and Naive Bayes. To verify the model, the best performing algorithms were then utilized to analyze other states (Arizona, New Jersey, New York and Texas) as test cases. The findings show an acceptable trend, further confirming usability of the chosen features for prediction of similar cases.

Due to the limited natural water resources and the increase in population, managing water consumption is becoming an increasingly important subject worldwide. In this paper, we present and compare different machine learning models that are able to predict water demand for Central Indiana. The models are developed for two different time scales: daily and monthly. The input features for the proposed model include weather conditions (temperature, rainfall, snow), social features (holiday, median income), date (day of the year, month), and operational features (number of customers, previous water demand levels). The importance of these input features as accurate predictors is investigated. The results show that daily and monthly models based on recurrent neural networks produced the best results with an average error in prediction of 1.69% and 2.29%, respectively for 2016. These models achieve a high accuracy with a limited set of input features.

At DigitalGlobe we spend a great deal of time focused on urban areas, where the majority of the world's people now live, and thus where the majority of commerce, conflict, and change take place. Earlier this year an interesting thought crossed my mind: "What would a perfect city look like from space?" Would it have large open venues or lots of public transportation? Would there be an abundance of parks or the tallest skyscrapers in the world? How would this vision of idyllic urban design change depending on where in the world you were? Since I spend my days at DigitalGlobe working to bring cutting-edge analytics to our GBDX platform, I naturally wondered if we could apply machine learning to our satellite imagery to explore this idea.

Uber is pulling its self-driving cars from California roads after state regulators demanded special permits. SAN FRANCISCO -- Uber has agreed to pay $20 million for exaggerating how much its drivers could earn and encouraging them to lease cars through a'low-cost' program the government says was anything but. The agreement was made public Thursday in documents filed by the Federal Trade Commission in San Francisco. It alleges that Uber had engaged in unfair or deceptive practices. Uber didn't admit to wrongdoing but did agreed to settle with the FTC.

Airbnb boasts almost two million listings in 34,000 cities, and according to data from Inside Airbnb, a independent data analysis website, listed about 36000 apartments in New York as of July 5, 2016. This data exploration sets out to visualize how Airbnb operates in New York City. Airbnb's presence in NYC has been clouded in controversy from the beginning, with law makers arguing that Airbnb drive up rents for New York residents, as well as facilitating a lot of illegal hosting activities, all the while not paying any of the fees hotels are subjected to. Rent is drived up when landlords decide to rather rent apartments to short-term guests at higher rates, compared to signing up tenants for yearlong leases. In a study conducted in 2014, The New York State Attorney General concluded that 72%of all units used as private short-term rentals on Airbnb during 2010 through mid-2014 appeared to violate both state and local New York laws.

He takes the NYC Data Science Academy 12 week full time Data Science Bootcamp program from July 5th to September 22nd, 2016. This post is based on their first class project - the Exploratory Data Analysis Visualization Project, due on the 2nd week of the program. You can find the original article here. Airbnb boasts almost two million listings in 34,000 cities, and according to data from Inside Airbnb, a independent data analysis website, listed about 36000 apartments in New York as of July 5, 2016. This data exploration sets out to visualize how Airbnb operates in New York City.

IT and engineering salaries in the U.S. rose 3.9 percent in 2015, the second-highest annual increase since 2010, according to a survey from IEEE-USA. The median income for IT and engineering professionals rose to US 135,000 in 2015, up from 130,000 in 2014, IEEE-USA said. Salaries rose nearly 4.3 percent between 2013 and 2014, after rising just 0.6 percent in 2013. Engineers working in systems and control, including the subspecialties of robotics and automation, control systems, industrial electronics and cybernetics, saw the largest salary increases in 2015. Their salaries rose 8.7 percent to 130,000.

Airbnb boasts almost two million listings in 34,000 cities, and according to data from Inside Airbnb, a independent data analysis website, listed about 36000 apartments in New York as of July 5, 2016. This data exploration sets out to visualize how Airbnb operates in New York City. Airbnb's presence in NYC has been clouded in controversy from the beginning, with law makers arguing that Airbnb drive up rents for New York residents, as well as facilitating a lot of illegal hosting activities, all the while not paying any of the fees hotels are subjected to. Rent is drived up when landlords decide to rather rent apartments to short-term guests at higher rates, compared to signing up tenants for yearlong leases. In a study conducted in 2014, The New York State Attorney General concluded that 72%of all units used as private short-term rentals on Airbnb during 2010 through mid-2014 appeared to violate both state and local New York laws.