Lua is a scripting language developed at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) that has come to be the leading scripting language for video games worldwide.3,7 It is also used extensively in embedded devices like set-top boxes and TVs and in other applications like Adobe Photoshop Lightroom and Wikipedia.14 Its first version was released in 1993. The current version, Lua 5.3, was released in 2015. Though mainly a procedural language, Lua lends itself to several other paradigms, including object-oriented programming, functional programming, and data-driven programming.5 It also offers good support for data description, in the style of JavaScript and JSON.

Docked in Lewes, Delaware, is a 166-foot ship called the DELRIVER that is rarely called out of port. Nonetheless, it's staffed 24/7 by a four-person crew and stands ready for action at a moment's notice. The DELRIVER is an oil-spill response vessel, funded by the local oil industry to clean up spills in the Delaware Bay as soon as they happen. The last major spill in the area was in 2004, when the tanker Athos spewed 265,000 gallons of heavy crude from Venezuela into the Delaware River. The last spill of any kind that it responded to was a small diesel spill in 2014.

The Next Economy will be driven by 10 emerging technologies; artificial intelligence underpins them all. And the race is on for the crown of having the Holy Grail of computing, the countries that are leading in the adoption of AI are China and the United States. These countries are way ahead of the rest of the planet; including Latin America. Endeavor, a non-profit organization that helps entrepreneurs, published a report on The State of Artificial Intelligence Adoption in LATAM (spanish). The report highlights the obstacles and future of the 240 companies that participated in the report; insights were captured using surveys and interviews from 70 projects in Argentina, Brazil, Chile, Colombia, Mexico and Peru.

At this moment, databanks worldwide contain brain images of previously unimaginable numbers. Combined with developments in data science, these massive data provide the potential to better understand the genetic underpinnings of brain diseases. However, different datasets, which are stored at different institutions, cannot always be shared directly due to privacy and legal concerns, thus limiting the full exploitation of big data in the study of brain disorders. Here we propose a federated learning framework for securely accessing and meta-analyzing any biomedical data without sharing individual information. We illustrate our framework by investigating brain structural relationships across diseases and clinical cohorts. The framework is first tested on synthetic data and then applied to multi-centric, multi-database studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of the approach for further applications in distributed analysis of multi-centric cohorts

Abstract--Violence is an epidemic in Brazil and a problem on the rise worldwide. Mobile devices provide communication technologies which can be used to monitor and alert about violent situations. However, current solutions, like panic buttons or safe words, might increase the loss of life in violent situations. We propose an embedded artificial intelligence solution, using natural language and speech processing technology, to silently alert someone who can help in this situation. The corpus used contains 400 positive phrases and 800 negative phrases, totaling 1,200 sentences which are classified using two well-known extraction methods for natural language processing tasks: bag-of-words and word embeddings and classified with a support vector machine. We describe the proof-of-concept product in development with promising results, indicating a path towards a commercial product. More importantly we show that model improvements via word embeddings and data augmentation techniques provide an intrinsically robust model. The final embedded solution also has a small footprint of less than 10 MB.

Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework with specially designed CNNs, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next day's direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features. The evaluations show a significant improvement in prediction's performance compared to the state of the art baseline algorithms.

Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components. Inputs were presented to a Scattering Transform (ST) representation layer which fed a recurrent neural network for sequence learning using three layers of Long Short-Term Memory (LSTM). The STs were calculated for each signal with downsampling parameters chosen to give approximately 1 s time resolution, resulting in an eighteen-fold data reduction. The LSTM layers then operated at this downsampled rate. Results: The proposed approach detected arousal regions on the 10% random sample of the hidden test set with an AUROC of 88.0% and an AUPRC of 42.1%.

Human perception of surrounding events is strongly dependent on audio cues. Thus, acoustic insulation can seriously impact situational awareness. We present an exploratory study in the domain of assistive computing, eliciting requirements and presenting solutions to problems found in the development of an environmental sound recognition system, which aims to assist deaf and hard of hearing people in the perception of sounds. To take advantage of smartphones computational ubiquity, we propose a system that executes all processing on the device itself, from audio features extraction to recognition and visual presentation of results. Our application also presents the confidence level of the classification to the user. A test of the system conducted with deaf users provided important and inspiring feedback from participants.

RIn-Close_CVC is an efficient (take polynomial time per bicluster), complete (find all maximal biclusters), correct (all biclusters attend the user-defined level of consistency) and non-redundant (all the obtained biclusters are maximal and the same bicluster is not enumerated more than once) enumerative algorithm for mining maximal biclusters with constant values on columns in numerical datasets. Despite RIn-Close_CVC has all these outstanding properties, it has a high computational cost in terms of memory usage because it must keep a symbol table in memory to prevent a maximal bicluster to be found more than once. In this paper, we propose a new version of RIn-Close_CVC, named RIn-Close_CVC2, that does not use a symbol table to prevent redundant biclusters, and keeps all these four properties. We also prove that these algorithms actually possess these properties. Experiments are carried out with synthetic and real-world datasets to compare RIn-Close_CVC and RIn-Close_CVC2 in terms of memory usage and runtime. The experimental results show that RIn-Close_CVC2 brings a large reduction in memory usage and, in average, significant runtime gain when compared to its predecessor.

The Healthcare Artificial Intelligence Market research report tries to understand the pioneering tactics taken by vendors in the global market to offer product differentiation through Porter's five forces analysis. It also points out the ways in which these companies can reinforce their stand in the market and increase their revenues in the coming years. Ongoing industrial advancements and the persistent penetration of Internet in the remote corners of the world are also responsible for the noteworthy growth of the Global Healthcare Artificial Intelligence Market. This Healthcare Artificial Intelligence market intelligent report highlights on the key retailers in this market everywhere throughout the world. This domain of the report contains the business formats, insurance, and product illustrations, volume, generation, contact statistics, price, and revenue.