Airlines need to construct crew pairings to cover their flights. A pairing is a sequence of flights starting and finishing at a base and satisfying complex collective agreement constraints. For major airlines which handle more than 10k flights on a weekly basis, this becomes an important and difficult problem to solve. Efficient solutions are required since savings as low as 1% represent many dozens of millions saved every year. The complexity of the problem lies in the large number of possible pairings, and the selection of the set of pairings of minimal cost, which is a large integer programming problem impossible to solve with standard solvers (Elhallaoui et al., 2005; Kasirzadeh et al., 2017). In our review of related work, we address some advanced optimization techniques that reduce the number of variables and the number of constraints to solve it. The main drawback of these techniques, however, is that they require days to compute, while airlines are often given all the scheduling data only a few days before having to build the schedule. The objective of this paper is to use machine learning (ML) techniques to improve the algorithmic efficiency and solve this problem in a more feasible time horizon. Unfortunately, solving the problem with ML alone seems out of reach.

Artificial Intelligence is transforming the world at a rapid and accelerating pace, offering huge potential, but also posing social and economic challenges. Human beings are naturally fearful of machines – this is a constant. Technological advancements tend to outpace cultural shifts. It has taken the shock of a global pandemic to accelerate the uptake of many technologies that have been around for at least a decade. Unsurprisingly, much of the public discussion on AI has focused on recent controversies around facial recognition, automated decision-making and exam algorithms.

Mexico's increasingly militarized crackdown of powerful drug cartels has left nearly 39,000 unidentified bodies languishing in the country's morgues – a grotesque symbol of the ever-burgeoning war on drugs and rampant violence. Investigative NGO Quinto Elemento Labs, in a recent report, found that an alarming number of people have been simply buried in common graves without proper postmortems, while others were left in funeral homes. The so-called war of drugs has claimed the lives of nearly 300,000 people over the last 14 years, while another 73,000 have gone missing. All the while, these cartels have yet to be designated formal terrorist organizations despite boasting well-documented arsenals of sophisticated weaponry to rival most fear-inducing militias on battlefields abroad. Just last month, reports surfaced that Mexico's Jalisco New Generation Cartel (CJNG) now possess bomb-toting drones – which The Drive's Warzone depicts as "small quadcopter-type drones carrying small explosive devices to attack its enemies."

For more than 100 years Nobel Prizes have been given out annually to recognize breakthrough achievements in chemistry, literature, medicine, peace, and physics. As these disciplines undoubtedly continue to impact society, newer fields like artificial intelligence (AI) and robotics have also begun to profoundly reshape the world. In recognition of this, the world's largest AI society -- the Association for the Advancement of Artificial Intelligence (AAAI) -- announced yesterday the winner of their new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, a $1 million award given to honor individuals whose work in the field has had a transformative impact on society. The recipient, Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT and a member of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), is being recognized for her work developing machine learning models to develop antibiotics and other drugs, and to detect and diagnose breast cancer at early stages. In February, AAAI will officially present Barzilay with the award, which comes with an associated prize of $1 million provided by the online education company Squirrel AI. "Only world-renowned recognitions, such as the Association of Computing Machinery's A.M. Turing Award and the Nobel Prize, carry monetary rewards at the million-dollar level," says AAAI awards committee chair Yolanda Gil. "This award aims to be unique in recognizing the positive impact of artificial intelligence for humanity."

For more than 100 years Nobel Prizes have been given out annually to recognize breakthrough achievements in chemistry, literature, medicine, peace, and physics. As these disciplines undoubtedly continue to impact society, newer fields like artificial intelligence (AI) and robotics have also begun to profoundly reshape the world. In recognition of this, the world's largest AI society -- the Association for the Advancement of Artificial Intelligence (AAAI) -- announced today the winner of their new Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, a $1 million award given to honor individuals whose work in the field has had a transformative impact on society. The recipient, Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT and a member of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), is being recognized for her work developing machine learning models to develop antibiotics and other drugs, and to detect and diagnose breast cancer at early stages. In February, AAAI will officially present Barzilay with the award, which comes with an associated prize of $1 million provided by the online education company Squirrel AI. "Only world-renowned recognitions, such as the Association of Computing Machinery's A.M. Turing Award and the Nobel Prize, carry monetary rewards at the million-dollar level," says AAAI awards committee chair Yolanda Gil.

On the morning of November 9, 2016, the world woke up to the shocking outcome of the U.S. Presidential election: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots--automated social media accounts mimicking humans--played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory.16,19 In the aftermath of the 2016 U.S. elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects these malicious actors seem to have on our societies.27,29 This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times--during the run-up to the 2020 U.S. elections--the question appears as more crucial than ever. Particularly so, also in light of the recent reported tampering of the electoral debate by thousands of AI-powered accounts.a What struck social, political, and economic analysts after 2016--deception and automation--has been a matter of study for computer scientists since at least 2010. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavors in social bot detection can also inform strategies for detecting and mitigating the effects of other--more recent--forms of online deception, such as strategic information operations and political trolls.

Frances E. Allen, an American computer scientist, ACM Fellow, and the first female recipient of the ACM A.M. Turing Award (2006), passed away on Aug. 4, 2020--her 88th birthday--from complications of Alzheimer's disease. Allen was raised on a dairy farm in Peru, NY, without running water or electricity. She received a BS degree in mathematics from the New York State College for Teachers (now the State University of New York at Albany). Inspired by a beloved math teacher, and by the example of her mother, who had also been a grade-school teacher, Allen started teaching high school math. She needed a master's degree to be certified, so she enrolled in a mathematics master's program at the University of Michigan.

As per the records of theWorld Health Organization, the first formally reported incidence of Zika virus occurred in Brazil in May 2015. The disease then rapidly spread to other countries in Americas and East Asia, affecting more than 1,000,000 people. Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes (Aedes aegypti and Aedes albopictus). The abundance of mosquitoes and, as a result, the prevalence of Zika virus infections are common in areas which have high precipitation, high temperature, and high population density.Nonlinear spatio-temporal dependency of such data and lack of historical public health records make prediction of the virus spread particularly challenging. In this article, we enhance Zika forecasting by introducing the concepts of topological data analysis and, specifically, persistent homology of atmospheric variables, into the virus spread modeling. The topological summaries allow for capturing higher order dependencies among atmospheric variables that otherwise might be unassessable via conventional spatio-temporal modeling approaches based on geographical proximity assessed via Euclidean distance. We introduce a new concept of cumulative Betti numbers and then integrate the cumulative Betti numbers as topological descriptors into three predictive machine learning models: random forest, generalized boosted regression, and deep neural network. Furthermore, to better quantify for various sources of uncertainties, we combine the resulting individual model forecasts into an ensemble of the Zika spread predictions using Bayesian model averaging. The proposed methodology is illustrated in application to forecasting of the Zika space-time spread in Brazil in the year 2018.

An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.

Obtaining data with meaningful labels is often costly and error-prone. In this situation, semi-supervised learning (SSL) approaches are interesting, as they leverage assumptions about the unlabeled data to make up for the limited amount of labels. However, in real-world situations, we cannot assume that the labeling process is infallible, and the accuracy of many SSL classifiers decreases significantly in the presence of label noise. In this work, we introduce the LGC_LVOF, a leave-one-out filtering approach based on the Local and Global Consistency (LGC) algorithm. Our method aims to detect and remove wrong labels, and thus can be used as a preprocessing step to any SSL classifier. Given the propagation matrix, detecting noisy labels takes O(cl) per step, with c the number of classes and l the number of labels. Moreover, one does not need to compute the whole propagation matrix, but only an $l$ by $l$ submatrix corresponding to interactions between labeled instances. As a result, our approach is best suited to datasets with a large amount of unlabeled data but not many labels. Results are provided for a number of datasets, including MNIST and ISOLET. LGCLVOF appears to be equally or more precise than the adapted gradient-based filter. We show that the best-case accuracy of the embedding of LGCLVOF into LGC yields performance comparable to the best-case of $\ell_1$-based classifiers designed to be robust to label noise. We provide a heuristic to choose the number of removed instances.