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LUXEMBOURG—( )—SES announced today that it has selected SpaceX as a launch partner to deliver its next-generation Medium Earth Orbit (MEO) satellite constellation into space on board Falcon 9 rockets from Cape Canaveral. The two companies have disrupted the industry in the past when SES became the first to launch a commercial GEO satellite with SpaceX, and later as the first ever payload on a SpaceX reusable rocket. Their next launch, in 2021, will be another one for the records as the revolutionary terabit-scale capabilities of SES’s O3b mPOWER communications system disrupt the industry again.

The global O3b mPOWER system comprises an initial constellation of seven high-throughput, low-latency MEO satellites, each capable of generating thousands of electronically-steered beams that can be dynamically adjusted to serve customers in various markets including telecom and cloud, communications-on-the-move and government. O3b mPOWER also will include a variety of intelligent, application-specific Customer Edge Terminals integrated with SES’s terrestrial network and dynamically optimised using the recently announced Adaptive Resource Control (ARC) software system, further boosting O3b mPOWER’s market-leading flexibility.

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FRANCE’S tourist beaches are being overrun with toxic slime which experts say can kill sunbathers and swimmers within seconds.

The green algae releases poisonous gases when trodden on causing those nearby to faint and suffer cardiac arrest, say reports.

At least three people and dozens of animals have already died, but some fear other deaths may have been mistakenly passed off as drownings.

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Dr. Kevin Strange is the CEO and co-founder of Novo Biosciences, a biotechnology company focused on regenerating the heart and other organs. We recently had the opportunity to interview him about MSI-1436 (trodusquemine), a compound that promotes regeneration in multiple animal models.

What, if anything, happens to existing scar tissue in the presence of MSI-1436?

More detailed studies need to be conducted to fully understand how MSI-1436 impacts existing scar tissue. However, our published work is very encouraging. We induced ischemic injury in the adult mouse heart by permanently ligating the left anterior descending coronary artery. This is a standard heart attack model. Twenty-four hours after ligation of the artery, we began treating with MSI-1436 or vehicle (placebo). Hearts were isolated from mice for histological analysis 3 days and 28 days after injury and collagen deposition (i.e., scarring) was quantified. In hearts isolated after 3 days, the scarring index measured as area was the same, ~40%, in both MSI-1436- and vehicle-treated mice. In other words, there was no difference in the extent of initial scarring in the two groups of animals.

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This is the first Russian x-risks newsletter, which will present news about Russia and global catastrophic risks from the last 3 months.

Given the combination of high technological capabilities, poor management, high risk tolerance and attempts to catch up with West and China in the military sphere, Russia is prone to technological catastrophes. It has a 10 times higher level of aviation catastrophes and car accidents than developed countries.

Thus it seems possible that a future global catastrophe may be somehow connected with Russia. However, most of the work in global catastrophic and existential risk (x-risks) prevention and policy efforts are happening in the West, especially in US, UK and Sweden. Even the best policies adopted by the governments of these countries may not help if a catastrophe occurs in another country or countries.

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Computer vision is one of the most popular applications of artificial intelligence. Image classification, object detection and object segmentation are some of the use cases of computer vision-based AI. These techniques are used in a variety of consumer and industrial scenarios. From face recognition-based user authentication to inventory tracking in warehouses to vehicle detection on roads, computer vision is becoming an integral part of next-generation applications.

Computer vision uses advanced neural networks and deep learning algorithms such as Convolutional Neural Networks (CNN), Single Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN). Applying these algorithms requires a thorough understanding of neural network architecture, advanced mathematics and image processing techniques. For an average ML developer, CNN remains to be a complex branch of AI.

Apart from the knowledge and understanding of algorithms, CNNs demand high end, expensive infrastructure for training the models, which is out of reach for most of the developers.

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