Baccini et al. (Reports, 13 October 2017, p. 230) report MODIS-derived pantropical forest carbon change, with spatial patterns of carbon loss that do not correspond to higher-resolution Landsat-derived tree cover loss. The assumption that map results are unbiased and free of commission and omission errors is not supported. The application of passive moderate-resolution optical data to monitor forest carbon change overstates our current capabilities. Baccini et al. (1) report net tropical forest aboveground carbon stock change from Moderate Resolution Imaging Spectroradiometer (MODIS) data and purport to capture all forest carbon dynamics resulting from both natural and anthropogenic processes. We believe their method and results overstate current monitoring capabilities and may confuse the global community of practitioners working to establish robust and defensible forest carbon monitoring systems.

Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences. In spite of their relevance, the computational properties of these alternatives have not yet been fully explored. We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016). We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data. In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete. Our study also reveals some minimal sets of elements needed to obtain these completeness results.

Its constant technological evolution characterizes the contemporary world, and every day the processes, once manual, become computerized. Data are stored in the cyberspace, and as a consequence, one must increase the concern with the security of this environment. Cyber-attacks are represented by a growing worldwide scale and are characterized as one of the significant challenges of the century. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in cybernetic data attacks, focusing on the SQL Injection attack. The tests were performed with real bases of SQL Injection attacks on government computers, using fuzzy neural networks. According to the results obtained, the feasibility of constructing a system based on fuzzy rules, with the classification accuracy of cybernetic invasions within the margin of the standard deviation (compared to the state-of-the-art model in solving this type of problem) is real. The model helps countries prepare to protect their data networks and information systems, as well as create opportunities for expert systems to automate the identification of attacks in cyberspace.

One noninvasive way to study frog communities is by analyzing long-term samples of acoustic material containing calls. This immense task has been optimized by the development of Machine Learning tools to extract ecological information. We explored a likelihood-ratio audio detector based on Gaussian mixture model classification of 10 frog species, and applied it to estimate presence-absence in audio recordings from an actual amphibian monitoring performed at Yasun ı National Park in the Ecuadorian Amazonia. A modified filter-bank was used to extract 20 cepstral features that model the spectral content of frog calls. Experiments were carried out to investigate the hyperparameters and the minimum frog-call time needed to train an accurate GMM classifier. With 64 Gaussians and 12 seconds of training time, the classifier achieved an average weighted error rate of 0.9% on the 10-fold cross-validation for nine species classification, as compared to 3% with MFCC and 1.8% with PLP features. For testing, 10 GMMs were trained using all the available training-validation dataset to study 23.5 hours in 141, 10-minute long samples of unidentified real-world audio recorded at two frog communities in 2001 with analog equipment. To evaluate automatic presence-absence estimation, we characterized the audio samples with 10 binary variables each corresponding to a frog species, and manually labeled a subset of 18 samples using headphones. The one-vs-all Receiver Operating Characteristics curves were used to tune the likelihood-ratio detector per class in order to set operating points that minimize false positives while still allowing moderately noisy calls to be detected. A recall of 87.5% and precision of 100% with average accuracy of 96.66% suggests good generalization ability of the algorithm, and provides evidence of the validity of this approach Finally, we applied the algorithm to the available corpus, and show its potentiality to gain insights into the temporal reproductive behavior of frogs. Introduction In long term ecological studies, it is important to quantify changes that occur on biodiversity and the ecosystem as a whole. Large scale temporal and spatial studies to understand the natural and anthropogenic induced population dynamics are demanded by the scientific community. In addition, recent anuran population declines around the world have motivated studies to gain an understanding of the phenomenon [1].

No matter the language, the principles are still the same--customers should be the focus of every company. South American companies face the volatile world of instability and inflation, but they still show that focusing on customers can lead to success. Here are 10 of the most customer-focused companies in South America. Arcos Dorados is based in Brazil and is the world's largest independent McDonald's franchisee with stores in more than 20 Latin American countries. The brand focuses on "cooltura de servicio", or "service culture," and encourages each employee to provide personalized service to customers.

We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals. To this end, we replace conventional convolution kernels with linear combinations of differential operators that are weighted by learnable parameters. Differential operators can be efficiently estimated on unstructured grids using one-ring neighbors, and learnable parameters can be optimized through standard back-propagation. As a result, we obtain extremely efficient neural networks that match or outperform state-of-the-art network architectures in terms of performance but with a significantly lower number of network parameters. We evaluate our algorithm in an extensive series of experiments on a variety of computer vision and climate science tasks, including shape classification, climate pattern segmentation, and omnidirectional image semantic segmentation. Overall, we present (1) a novel CNN approach on unstructured grids using parameterized differential operators for spherical signals, and (2) we show that our unique kernel parameterization allows our model to achieve the same or higher accuracy with significantly fewer network parameters.

Optical imaging techniques are important tools extensively used for probing a number of materials properties [1]. These imaging techniques are non-destructive and particularly convenient for dealing with biological and other complex materials [2]. Liquid crystals are among these materials widely studied via optical and image processing methods [3]. This occurs because liquid crystals are birefringent materials, and as such, simple polarized optical microscope imaging already access some of their important properties, including birefringence and sample thickness [4]. Moreover, this technique estimates the local ordering properties (for instance, the director distribution) across a sample when coupled with variable retarders and different algorithms for fast and sensitive measurements [5]. This approach is known as LC-PolScope [6] and has been used for fine imaging of defect cores in lyotropic liquid crystals [7] and can describe the orientational order of active nematics [8]. Despite the extensive use of optical imaging approaches in the study of liquid crystals [9-12], much less attention has been paid to the problem of extracting physical parameters directly from images of these materials. This is an important issue since several physical parameters of liquid crystals are only obtained by adjusting theoretical models to cumbersome and time demanding experimental results. Examples include the microscopic order parameter, from which several other parameters characterizing the nematic phase are dependent [3], and the pitch length of cholesteric liquid crystals.

Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based machine learning. More specifically, we show that so-called analogical proportions are closely connected to kernel functions on pairs of objects. Based on this result, we introduce the analogy kernel, which can be seen as a measure of how strongly four objects are in analogical relationship. As an application, we consider the problem of object ranking in the realm of preference learning, for which we develop a new method based on support vector machines trained with the analogy kernel. Our first experimental results for data sets from different domains (sports, education, tourism, etc.) are promising and suggest that our approach is competitive to state-of-the-art algorithms in terms of predictive accuracy.

When the Apollo 11 spacecraft was orbiting the Moon prior to the first lunar landing, Nasa officials told the astronauts on board to look out for the'lovely girl with a big rabbit'. They were jokingly referring to a story from Chinese mythology in which the goddess Chang'e escapes Earth to live on the Moon with her pet, Jade Rabbit. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon'. The robotic probe was named Chang'e 4, a product of China's £3.9 billion a year space exploration project. This week, almost 50 years on from that'giant leap for mankind', the legend of Chang'e resurfaced -- and this time the joke is on the Americans as China announced it had became the first nation to land a spacecraft on the'dark side of the moon' If ever there was a metaphor for the Communist super-power's obsessive secrecy and soaring global ambition, then this audacious secret mission provides it.

A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems. The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II). We evaluated the contextual information in four context-aware recommender systems. Different weights were assigned to each type of information. The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.