The ministry's new missive to lawmakers came in response to a request made late last year by Andrej Hunko and fellow Linke members of parliament about the status of the Eurodrone. The questioners appear especially curious about IABG's role in helping to define key performance parameters for the aircraft, particularly related to airworthiness in civilian airspace. According to the ministry's letter, the contractor recommended outfitting the drone with a twin-turboprop propulsion system, a design feature that the developer nations ultimately adopted.

The Advocate reported Saturday that the resolution adopted last month is just another link a long chain of USPS issues the city wants fixed. Plaquemine Mayor Ed Reeves says residents sometimes go days without mail. Residents say packages are sometimes thrown from delivery vans or delivered to the wrong address and expected mail sometimes never appears.

London is a truly remarkable city – an economic powerhouse and a cultural destination without equal. But it's a terrible irony that this fast-moving, fast-living, fast-earning metropolis is stuck in traffic so much of the time. According to traffic data company INRIX, London is – unsurprisingly – the UK's most congested city. Each motorist is spending approximately 74 hours a year in gridlock during peak times. And the effect on the environment is devastating – the mayor of London's office concluded in 2017 that road transport in London was responsible for half of the main air pollutants in the capital.

Data influences every aspect of your life. If not completely already, the technological landscape will be completely data-centric in the near future. The device you are reading this article on probably collects your data to optimize your user experience. Or, you may have been recommended this article based on your reading habits. Even, the self-driving car you use may take data collected from other cars on the road to keep you safe.

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a pre-specified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine, because they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2x2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.

Security-Constrained Unit Commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via Mixed-Integer Linear Programming, sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning (ML) techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions and affine subspaces where the optimal solution is likely to lie, leading to significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that, using the proposed techniques, SCUC can be solved on average 12 times faster than conventional methods, with no negative impact on solution quality.

Recently, many adversarial examples have emerged for Deep Neural Networks (DNNs) causing misclassifications. However, in-depth work still needs to be performed to demonstrate such attacks and security vulnerabilities for spiking neural networks (SNNs), i.e. the 3rd generation NNs. This paper aims at addressing the fundamental questions:"Are SNNs vulnerable to the adversarial attacks as well?" and "if yes, to what extent?" Using a Spiking Deep Belief Network (SDBN) for the MNIST database classification, we show that the SNN accuracy decreases accordingly to the noise magnitude in data poisoning random attacks applied to the test images. Moreover, SDBNs generalization capabilities increase by applying noise to the training images. We develop a novel black box attack methodology to automatically generate imperceptible and robust adversarial examples through a greedy algorithm, which is first of its kind for SNNs.

Despite recent advances in rank aggregation and mixture learning, there has been a limited amount of success for learning a mixture model for ranking data. Motivated by the problem of learning a mixture of ranking models from pair-wise comparisons, we consider mixture learning from partial observations. The generic approaches for mixture learning do not generalize to this setting. Matrix estimation, however, provides a way to recover a structured underlying matrix from its partial, noisy observations. We utilize matrix estimation as a pre-processing step to extend the mixture learning problem to allow for partial observations. Instantiating our matrix estimation subroutine with singular value thresholding, we provide a bound on the estimation error with respect to $\|\cdot\|_{2,\infty}$-norm. In particular, we show that if $p$ (the fraction of observed entries) scales as $\tilde{\Omega}((\frac{r}{d})^{\frac{1}{3}})$, then the normalized $\|\cdot\|_{2,\infty}$ error vanishes to $0$ as long as the underlying $N \times d$ ($N\geq d$) matrix is rank $r$; this holds true even if the noise is correlated across columns. As an application, we argue if $\Gamma p=\tilde{\Omega}(\sqrt{r})$, then the mixture components can be correctly identified with $N=poly(d)$ samples; $\Gamma$ is the minimum gap between the mixture means. Further, we argue a large class of popular ranking models (e.g., Mallow, Multinomial Logit (MNL) Model) satisfy the sub-gaussian property when viewed through a pairwise embedding lens. Hence, our method provides a sufficient condition for efficiently recovering the mixture components for an important class of models. For example, mixtures of $r$ components can be clustered correctly using $\tilde{O}(rn^4)$ pair-wise comparisons when the components are well-separated and distributed as per either a Mallows, MNL, or any Random Utility Model over $n$ items.

Photovoltaic (PV) power generation has emerged as one of the lead renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather conditions like solar irradiance and temperature. Predicting PV production, even in the 24 hour forecast, remains a challenge and leads energy providers to keep idle - often carbon emitting - plants. In this paper we introduce a Long-Term Recurrent Convolutional Network using Numerical Weather Predictions (NWP) to predict, in turn, PV production in the 24 hour and 48 hour forecast horizons. This network architecture fully leverages both temporal and spatial weather data, sampled over the whole geographical area of interest. We train our model on a NWP dataset from the National Oceanic and Atmospheric Administration (NOAA) to predict spatially aggregated PV production in Germany. We compare its performance to the persistence model and to state-of-the-art methods.

While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.