Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to non-linear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL) based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season. Among the three algorithms, namely SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data postprocessing, in particular, ERA5 data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation.

In this paper, we propose two nonparametric methods used in the forecasting of functional time-dependent data, namely functional singular spectrum analysis recurrent forecasting and vector forecasting. Both algorithms utilize the results of functional singular spectrum analysis and past observations in order to predict future data points where recurrent forecasting predicts one function at a time and the vector forecasting makes predictions using functional vectors. We compare our forecasting methods to a gold standard algorithm used in the prediction of functional, time-dependent data by way of simulation and real data and we find our techniques do better for periodic stochastic processes.

This excerpt from my upcoming book is an extension of the work I have published online on Artificial General Intelligence, Artificial General Cognition, Cognitive Artificial General Intelligence and AI Development. All of the previous content is included in The Artificial Superintelligence Handbook Series (vol. 1 & 2) available on Amazon and vol 3, including this article in full, scheduled for release early next year. Thank you to all who follow and are inspired to reach for the singularity. There is one truth in Artificial Intelligence design and development. There are lots of good coders and researchers but a huge gap between the development talent pool and financially viable commercial products.

New footage of Arecibo Observatory collapsing in the jungle of Puerto Rico shows the moment its main cables snapped and sent a massive platform hurling to the ground - triggering a reaction that led to its destruction. Drones were investigating cables around the telescope when the restraints failed and dropped the 900-ton platform onto to the reflector dish 400 feet below. In one of the videos, the platform begins swaying in the air before letting out a loud roar as it dislodged from the supporting cable and snapping into pieces as it dropped. The second part of the clip is a view of the cables at the top of a support tower, which shows a group of frayed wires and an empty spot where cables were that had previously failed this year. One of the cables begins to strain and then violently flies out of its support, creating a cloud of smoke and debris.

TEHRAN, Iran (AP) -- Iran's supreme leader on Saturday demanded the "definitive punishment" of those behind the killing of a scientist who led Tehran's disbanded military nuclear program, as the Islamic Republic blamed Israel for a slaying that has raised fears of reignited tensions across the Middle East. After years of being in the shadows, the image of Mohsen Fakhrizadeh suddenly was to be seen everywhere in Iranian media, as his widow spoke on state television and officials publicly demanded revenge on Israel for the scientist's slaying. Israel, long suspected of killing Iranian scientists a decade ago amid earlier tensions over Tehran's nuclear program, has yet to comment on Fakhrizadeh's killing Friday. However, the attack bore the hallmarks of a carefully planned, military-style ambush, the likes of which Israel has been accused of conducting before. The attack has renewed fears of Iran striking back against the U.S., Israel's closest ally in the region, as it did earlier this year when a U.S. drone strike killed a top Iranian general.

As even the most inattentive observer of contemporary international politics will attest, technological competition – mostly, but not always, between the U.S. and its allies on one hand, and China and Russia on the other – has once again risen to the fore. Analysts, so far, have approached this issue from various angles: what it means in terms of military balances, the possibility of international cooperation, what a technological edge implies for domestic policies, and so on. The outgoing Trump administration has made technological contestation with China a cornerstone of its strategic policy, emphasizing the need for the United States to maintain its edge when it comes to artificial intelligence (AI), quantum information science, and aerospace and other critical technologies, among others. Other Indo-Pacific powers, such as Australia, India, and Japan, have also joined the fray in pushing both new and emerging tech at home as well as promoting collaboration around it between "like-minded countries." In June this year, a Global Partnership on Artificial Intelligence of 14 states along with the European Union was launched, to facilitate collective AI research as well as implementation.

We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identification (Subtask 1) and province-level sub-dialect identification (Subtask 2). The data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected from the Twitter domain. As such, NADI is the first shared task to target naturally-occurring fine-grained dialectal text at the sub-country level. A total of 61 teams from 25 countries registered to participate in the tasks, thus reflecting the interest of the community in this area. We received 47 submissions for Subtask 1 from 18 teams and 9 submissions for Subtask 2 from 9 teams.

Perched in his fire tower high above the pine trees, Nick Dutton leans back and nods to the cascading hills and mountains behind him. "I love being out here, just away from stuff," he says. "I mean, you can't really complain." Dutton, a fire tower operator, is sitting in his office, a tiny cabin propped high above the treetops by metal supports that sway with the wind. His walls are littered with compass points and references, each a guide to the bush stretching in every direction along the eastern ACT-NSW border.

In 1997, The Simpsons prophesized that for future wars, "most of the actual fighting will be done by small robots" with soldiers only responsible to "build and maintain those robots." Though the cartoon's track record with predictions is debatable, few will argue that robots have played a critical role in combat over the past decade. Whether it is a Predator drone patrolling a No-Fly zone or a Packbot diffusing a bomb, robots have made their presence known on the battlefield. The U.S. military and coalition forces use the base, located in an undisclosed location, to launch airstrikes against ISIL in Iraq and Syria, as well as to distribute cargo and transport troops supporting Operation Inherent Resolve. The Predators at the base are operated and maintained by the 46th Expeditionary Reconnaissance Squadron, currently attached to the 386th Air Expeditionary Wing.

Applying a machine learning technique to a group of possible storm paths could help meteorologists provide more accurate medium-term hurricane forecasts. This approach could also help them issue timely warnings to communities in the path of these potentially deadly storms, report researchers. In a new study, the researchers used machine learning to remove certain groups of hurricane predictions from ensembles--sets of predictions from weather models that are based on a range of weather possibilities--to lower errors and improve forecasts four to six days ahead. "…WHEN YOU ARE FACING A HURRICANE, IT'S IMPORTANT TO KNOW WHAT TYPE OF STORM YOU'RE GOING TO GET--AND WHEN YOU'RE GOING TO GET IT." Scientists use these ensemble models because weather is highly complex and trying to forecast even a single event creates huge amounts of data, says Jenni Evans, professor of meteorology and atmospheric science and director of the Institute for Computational and Data Sciences at Penn State. "The models are run slightly differently many, many times to create an ensemble of possible future states of the atmosphere. It's this ensemble that is given to the forecasters," says Evans. "We're looking at 120 different forecasts at every time around the globe, then focusing on an individual typhoon or hurricane and asking, 'What will this storm do in the future?' Now, if you give those predictions to a forecaster only a few hours before their forecast goes live, that's a huge amount of information to process," she says.