The unprecedented growth of mobile devices, applications and services pose have placed utmost demands on mobile and wireless networking infrastructure. Rapid research and development of 5G systems, have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experience challenges due to computational and battery power limitations. As a result models employed in the edge-based scenario are constrained to light-weight to achieve a trade-off between model complexity and accuracy. Also model compression, pruning, and quantization are largely in place.

What's now called Turkey was once the center of the Ottoman Empire, a global hub of culture and science during its heyday, which lasted over 600 years. It was the birthplace of the first surgical atlas and the first watch that measured time in minutes, and it's where astronomers first calculated the eccentricity of the Sun's orbit. Today, Turkey is better known for its rich cultural heritage, with large numbers of Russian and German tourists haggling over evil eyes, sipping Turkish tea in bazaars, and enjoying the hot water baths of Istanbul. With a population nearing 79 million people, Turkey also has high-quality and relatively cheap resources for developed markets to exploit capitalize on, along with a budding startup ecosystem. Deal sizes might be on the low side, but Turkish tech startups have stepped up to participate in the global AI race.

In this post, I cover some of my favorite methods for detecting outliers in time series data. There are many different approaches for detecting anomalous data points; for the sake of brevity, I only focus on unsupervised machine learning methods in this post. I deal with time series data a lot, and it's not uncommon for data sets to experience unexpected drops or spikes, flat lines, or phase shifts. Each of these situations qualifies as an'anomaly' -- something out of the ordinary when compared to the behavior of the sequence as a whole. Detecting anomalies in a time series is important for a variety of reasons.

Various technological innovations are injecting productivity, efficiency, and work safety into construction practices. The industry heads towards the smarter era! FREMONT, CA: Technology has advanced at a rapid pace. Just like every other industry, the construction industry has seen numerous developments to equipment, practices, and operational processes. All of the developments can easily be attributed to the growing technological prowess of construction companies. Several innovative application of modern technologies has disrupted age-old models of construction, enhanced productivity, improved safety, and ensured reduced wastage and costs.

Standard supervised learning procedures are validated against a test set that is assumed to have come from the same distribution as the training data. However, in many problems, the test data may have come from a different distribution. We consider the case of having many labeled observations from one distribution, $P_1$, and making predictions at unlabeled points that come from $P_2$. We combine the high predictive accuracy of random forests (Breiman, 2001) with an importance sampling scheme, where the splits and predictions of the base-trees are done in a weighted manner, which we call Locally Optimized Random Forests. These weights correspond to a non-parametric estimate of the likelihood ratio between the training and test distributions. To estimate these ratios with an unlabeled test set, we make the covariate shift assumption, where the differences in distribution are only a function of the training distributions (Shimodaira, 2000.) This methodology is motivated by the problem of forecasting power outages during hurricanes. The extreme nature of the most devastating hurricanes means that typical validation set ups will overly favor less extreme storms. Our method provides a data-driven means of adapting a machine learning method to deal with extreme events.

For a long, long time, renewable energy proponents have considered advancements in battery technology to be the Holy Grail of the industry. Advancements in energy storage has been among the hardest to achieve economically, thanks to the incredibly tricky chemistry that's involved in storing power. Now, one company that's launching from Y Combinator believes it has found the key to making batteries better. The company is called Holy Grail and it's launching in the accelerator's latest cohort. With an executive team that initially included Nuno Pereira, David Pervan and Martin Hansen, Holy Grail is trying to bring the techniques of the fabless semiconductor industry to the world of batteries.

The Image Recognition Technology Is, Usually, Associated with an Array of Security and Surveillance-Related Uses and the Rapidly Developing Autonomous Vehicle Niche. Can Image Recognition Apps Help Businesses in Other Verticals? With Reuters' predictions for the not-so-far-off year of 2022 being in the region of a hefty $43-57 billion, Image Recognition is one big lure for AI outfits, and, simultaneously, a lot of hope for businesses and organizations that depend upon it for their survival and success. These include entities as diverse, as manufacturers of autonomous cars and security systems, national nature parks, border security forces, and companies that produce drones. Be it monitoring the state of a much cherished rainforest or sending drones to remote oil rigs to check if all one's assets are in one piece, almost all of the widely known uses of Image Recognition seem to be related to security and surveillance.

By Laure-Anne Pessina and Nicola Nosengo Scientists at EPFL have developed a tiny pump that could play a big role in the development of autonomous soft robots, lightweight exoskeletons and smart clothing. Flexible, silent and weighing only one gram, it is poised to replace the rigid, noisy and bulky pumps currently used. The scientists' work has just been published in Nature. Soft robots have a distinct advantage over their rigid forebears: they can adapt to complex environments, handle fragile objects and interact safely with humans. Made from silicone, rubber or other stretchable polymers, they are ideal for use in rehabilitation exoskeletons – such as the ones being developed in the NCCR Robotics "Wearable Robotics" research line – and robotic clothing.

TEHRAN – A team of researchers at Tehran's Amir Kabir University of Technology have designed and manufactured a robotic arm for removing pollutants from the surfaces of the double umbrella type porcelain insulators, Mehr reported on Saturday. Cleaning the polluted insulators of the overhead high voltage power lines has been one of the problems for the power transmission network during recent years, Amir Kabir University faculty member Amir Abolfazl Souratgar told Mehr. He pointed to power failure in southwestern Khuzestan province and a number of western provinces due to sand and dust storm in January 2015 due to polluted insulators. The robotic arm is able to clean insulators at higher speed and precision in comparison with other methods, he said, adding that it can work at high temperatures of up to about 60 centigrade and at low temperatures of around minus 40 centigrade as well as during high humidity and other bad climate conditions. Live-line cleaning of insulators using robot technology is an alternative and novel approach to removing the contaminants on the surfaces of the insulator.

"DEWA supports the directives of His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai, who instructed Dubai Government to deliver services that are 10 years ahead of other cities, and to work under the leadership of HH Sheikh Hamdan bin Mohammed bin Rashid Al Maktoum, Crown Prince of Dubai and Chairman of the Board of Trustees of the Dubai Future Foundation (DFF), and HH Sheikh Maktoum bin Mohammed bin Rashid Al Maktoum, Deputy Ruler of Dubai. These directives have guided DEWA's strategy to shape the future and enhance its role in serving the community to become a globally leading sustainable innovative corporation. DEWA has achieved considerable progress in this and one of its most prominent achievements is the launch of Digital DEWA, the digital arm of DEWA, to redefine the concept of utilities and create a new digital future for Dubai, to become the premier digital organisation in the world; with autonomous systems for renewable energy and storage and the expansion of artificial intelligence and digital services," said Al Tayer. Al Tayer noted that Digital DEWA has a four-point strategy. The first is launching advanced solar power technologies in Dubai.