The field for NASA's next New Frontiers mission is narrowing. Officials announced the two finalists for a new robotic explorer mission -- one that would send a spacecraft to bring samples of the comet 67P/Churyumov-Gerasimenko to Earth, and another to explore Saturn's moon Titan. The two mission concepts, CAESAR and Dragonfly, detailed in a NASA briefing Wednesday, beat out 10 other proposals to explore solar system targets including a basin on the moon; the surface of Venus; and Enceladus, the icy ocean world that also circles Saturn. "The New Frontiers program is really the premier program for our principal investigators and indeed it's one of the most difficult programs to be selected for," said Jim Green, director of the Planetary Science Division at NASA Headquarters in Washington. NASA only selects about two of these medium-class missions every decade, he added.

We study the computational complexity of candidate control in elections with few voters, that is, we consider the parameterized complexity of candidate control in elections with respect to the number of voters as a parameter. We consider both the standard scenario of adding and deleting candidates, where one asks whether a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding or deleting few candidates, as well as a combinatorial scenario where adding/deleting a candidate automatically means adding or deleting a whole group of candidates. Considering several fundamental voting rules, our results show that the parameterized complexity of candidate control, with the number of voters as the parameter, is much more varied than in the setting with many voters.

Designing new and efficient therapies is a long and ever more costly process, with less than ten percent of new treatments entering Phase I finally being approved by the FDA and commercialized [1, 2]. One of the major challenges for the improvement of drug development is to better understand how drugs interact with patients, particularly for treatments displaying heterogeneous responses. Therefore, conducting a detailed analysis of clinical trial data is critical to find subgroups of patients with higher benefit-risk ratio or to understand why a drug does not work on some subpopulation to improve existing therapeutic strategies. Moreover, understanding the relationships of patient descriptors which compose the most responsive cross-section of the population is of great importance when planning a Phase III trial, for salvaging failed trials, or accelerating advances in personalized medicine. This process of biomarker identification is critical to detect subgroups within a given indication, but, as shown recently for immunotherapies, can also provide the basis for pan-indication drug approval [3].

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate "worldviews" on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of "we're all equal" (WAE) and "what you see is what you get" (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (college admissions; credit application; insurance contracts) and map out the policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence.

Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.

To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.

Ever since Tesla CEO and founder Elon Musk announced his plans to develop a brain-computer interface (BCI) through his Neuralink startup, BCI technologies have received more attention. Musk, however, wasn't the first to propose the possibility of enhancing human capabilities through brain-computer interfacing. A number of other startups are working on a similar goal, including Braintree founder Bryan Johnson with Kernel. Even the U.S. Defense Department's Defense Advanced Research Projects Agency (DARPA) is working on one. Now, according to a collaboration of 27 experts--neuroscientists, neurotechnologists, clinicians, ethicists and machine-intelligence engineers--calling themselves the Morningside Group, BCIs present a unique and rather disturbing conundrum in the realm of artificial intelligence (AI). Essentially designed to hack the brain, BCIs themselves run the risk of being hacked by AI. "Such advances could revolutionize the treatment of many conditions, from brain injury and paralysis to epilepsy and schizophrenia, and transform human experience for the better," the experts wrote in a comment piece in the journal Nature.

From cars that drive themselves to diagnosing cancer with the snap of a smartphone camera, the world as we know it is set to undergo drastic change in the decade to come thanks to rapid advancement in the field of artificial intelligence, or AI. AI refers to the field of computer science that is focused on enabling technology to perform tasks that are normally done by humans. Faster and more powerful computers and the increased availability of immense sets of data have significantly increased AI's capabilities to identify patterns and use that information to reach human-like predictions and insights. Worldwide spending on AI solutions is predicted to grow to over $46 billion through 2020 and Canada is preparing to enjoy a bigger piece of that pie thanks to a number of factors that are setting the country apart as a global hub in the field. Here are three of the key reasons behind Canada's growing reputation as an AI hotspot.

There is a war a-brewin', but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin's declaration that "the nation that leads in AI (Artificial Intelligence) will be the ruler of the world," the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet. Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings / environment. See the blog "Artificial Intelligence is not Fake Intelligence" for a more detailed explanation …read more

The European Union's top court has ruled that ride-hailing service Uber should be regulated like a taxi company, a decision that could change the way it functions across the continent. Barney Harford, former CEO of Orbitz, joins Uber as its first COO. SAN FRANCISCO -- Uber named its first-ever chief operating officer Wednesday, a move designed to remake the upper ranks of the ride-hailing company as it grapples with fallout from its previous leadership. Barney Harford, the former CEO of travel company Orbitz, takes up his position January 2 as second-in-command to recently installed CEO Dara Khosrowshahi, a onetime rival and colleague from both men's tenures in the online travel business. Harford has been working at Uber as an advisor to Khosrowshahi since October, and will oversee global operations, marketing and customer support.