In September of 1982, a young engineer named Thomas Zimmerman filed a patent for an optical-flex sensor mounted inside a glove. The mitt would measure the yaw, pitch, and roll of its wearer's forearm and the bending of their fingers, a useful way to transpose a person's movement onto a screen. Seven years later, a commercial version known as the Power Glove launched for the Nintendo Entertainment System. The technology was simplified and styled to look like a knight's gauntlet, to which a video-game controller appeared to have been inelegantly glued. While wearing the glove, a player could throw a punch from the sofa, and watch it land in an explosion of pixels behind the television's glass.

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration. However, there is still a divide between the application development pipelines of existing supercomputing environments, and these new dynamic environments that disaggregate fluid resource pools through accessible, portable and re-programmable interfaces. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven scientific practice for the purpose of more efficient computing and usable tools for specific scientific domains. In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster. We also summarize a case study in wildfire modeling, that demonstrates the application of this new infrastructure in scientific workflows: a composed system that bridges the insights from edge sensing, AI and computing capabilities with a physics-driven simulation.

Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge. However, the current DTL techniques suffer from either catastrophic forgetting dilemma (losing the previously obtained knowledge) or overly biased pre-trained models (harder to adapt to target data) in finetuning pre-trained models or freezing a part of the pre-trained model, respectively. Progressive learning, a sub-category of DTL, reduces the effect of the overly biased model in the case of freezing earlier layers by adding a new layer to the end of a frozen pre-trained model. Even though it has been successful in many cases, it cannot yet handle distant source and target data. We propose a new continual/progressive learning approach for deep transfer learning to tackle these limitations. To avoid both catastrophic forgetting and overly biased-model problems, we expand the pre-trained model by expanding pre-trained layers (adding new nodes to each layer) in the model instead of only adding new layers. Hence the method is named EXPANSE. Our experimental results confirm that we can tackle distant source and target data using this technique. At the same time, the final model is still valid on the source data, achieving a promising deep continual learning approach. Moreover, we offer a new way of training deep learning models inspired by the human education system. We termed this two-step training: learning basics first, then adding complexities and uncertainties. The evaluation implies that the two-step training extracts more meaningful features and a finer basin on the error surface since it can achieve better accuracy in comparison to regular training. EXPANSE (model expansion and two-step training) is a systematic continual learning approach applicable to different problems and DL models.

During the past few months, Microsoft Exchange servers have been like chum in a shark-feeding frenzy. Threat actors have attacked critical zero-day flaws in the email software: an unrelenting cyber campaign that the US government has described as "widespread domestic and international exploitation" that could affect hundreds of thousands of people worldwide. Gaining visibility into an issue like this requires a full understanding of all assets connected to a company's network. This type of continuous tracking of inventory doesn't scale with how humans work, but machines can handle it easily. For business executives with multiple, post-pandemic priorities, the time is now to start prioritizing security. "It's pretty much impossible these days to run almost any size company where if your IT goes down, your company is still able to run," observes Matt Kraning, chief technology officer and co-founder of Cortex Xpanse, an attack surface management software vendor recently acquired by Palo Alto Networks. You might ask why companies don't simply patch their systems and make these problems disappear. If only it were that simple. Unless businesses have implemented a way to find and keep track of their assets, that supposedly simple question is a head-scratcher. But businesses have a tough time answering what seems like a straightforward question: namely, how many routers, servers, or assets do they have? If cybersecurity executives don't know the answer, it's impossible to then convey an accurate level of vulnerability to the board of directors. And if the board doesn't understand the risk--and is blindsided by something even worse than the Exchange Server and 2020 SolarWinds attacks--well, the story almost writes itself. That's why Kraning thinks it's so important to create a minimum set of standards.

This past week, we witnessed wrenching debates over speech--involving protesters on the street, our Twitterer-in-chief, and aspiring New York Times op-ed writers. Some of the best tools we have to inspire and contextualize social movements are books and film, and in the next week, we will host conversations with some of the most interesting leaders in the book industry and Hollywood. We hope you'll join us: After a man is injured in a forklift accident, he takes on a lucrative offer to "raise" a robot. After a jarring first impression (imagine a toddler in the body of a massive robot), the relationship makes the protagonist rethink much of his life. In the response essay, John Frank Weaver, author of Robots Are People Too warns about the manipulative capabilities of all-too-human robots: "A company that records all your interactions raising a child--the stress, the exhaustion, the jubilation, the love--has a treasure trove of information about what makes you tick as a person, even when the child is a robot."

Leonel Garcia-Marques CICPSI Faculdade de Psicologia Universidade de Lisboa Portugal garcia_marques@sapo.pt Abstract --An agent, artificial or human, must be continuously adjusting its behaviour in order to thrive in a more or less demanding environment. An artificial agent with the ability to predict human emotional valence in a geospatial and temporal context requires proper adaptation to its mobile device environment with resource consumption strict restrictions (e.g., power from battery). The developed distributed system includes a mobile device embodied agent ( SensAI) plus Cloud-expanded ( Expanse) cognition and memory resources. The system is designed with several adaptive mechanisms in a best effort for the agent to cope with its interacting humans and to be resilient on collecting data for machine learning towards prediction. These mechanisms encompass homeostatic-like adjustments such as auto recovering from an unexpected failure in the mobile device, forgetting repeated data to save local memory, adjusting actions to a proper moment (e.g., notify only when human is interacting), and the Expanse complementary learning algorithms' parameters with auto adjustments. Regarding emotional valence prediction performance, results from a comparison study between state-of-the-art algorithms revealed Extreme Gradient Boosting on average the best model for prediction with efficient energy use, and explainable using feature importance inspection. Therefore, this work contributes with a smartphone sensing-based system, distributed in the Cloud, robust to unexpected behaviours from humans and the environment, able to predict emotional valence states with very good performance. I NTRODUCTION The scientific evidence of epigenetics reveal on/off mechanisms inside chromosomes of human agents and reinforces the importance of any entity continuous adaptation to its environment.

What the Amazon founder and CEO wants for his empire and himself, and what that means for the rest of us. Where in the pantheon of American commercial titans does Jeffrey Bezos belong? Andrew Carnegie's hearths forged the steel that became the skeleton of the railroad and the city. John D. Rockefeller refined 90 percent of American oil, which supplied the pre-electric nation with light. Bill Gates created a program that was considered a prerequisite for turning on a computer. At 55, Bezos has never dominated a major market as thoroughly as any of these forebears, and while he is presently the richest man on the planet, he has less wealth than Gates did at his zenith. Yet Rockefeller largely contented himself with oil wells, pump stations, and railcars; Gates's fortune depended on an operating system. The scope of the empire the founder and CEO of Amazon has built is wider. Indeed, it is without precedent in the long history of American capitalism. More product searches are conducted ...

It'll be a cloud-native world soon, but we will still need pencils.Amido It's that time of year again and the IT industry is laying down predictions for the year ahead. A good many of these stories write themselves, that is - they all feature cloud computing, Artificial Intelligence (AI) and automation, mobile device centricity, IoT and perhaps a peppering of neural networks and Machine Learning (ML). If you're lucky, you get a forward taster of quantum computing in most enterprise IT predictions stories too, but don't count on that every time. But leaving the monolithic IT vendors themselves aside for a moment, what do the implementers think? London, UK-based cloud technology consultancy and developer shop Amido says its core business comes from rebuilding old-fashioned corporate technology infrastructures in the cloud.

The Expanse should just change their post credits for each episode to include a list of homework questions. Seriously--there are so many great things to explore in this hard science fiction show. In a recent episode, one of the large spaceships (the Navoo) rotates in order to create artificial gravity (that's not really a spoiler). Let me get right to it. You are probably somewhere near the surface of the Earth and there is a gravitational force between you and the Earth pulling you down.

Speckling the surface of one of Mars' oldest impact basins, NASA's Mars Reconnaissance Orbiter has spotted a sprawling expanse of'honeycomb' landforms, with individual cells of up to 6 miles wide. The origin of these textured features has long remained a mystery, as scientists debate which type of natural process could be responsible, from glacial events to wind erosion. It's possible that multiple processes are at play, according to NASA, with evidence suggesting the honeycombs and the surrounding landscape in Mars northwestern Hellas Planitia may still be undergoing activity today. Speckling the surface of one of Mars' oldest impact basins, NASA's Mars Reconnaissance Orbiter has spotted a sprawling expanse of'honeycomb' landforms, with individual cells of up to 6 miles wide. According to NASA, the area has features of different natural processes, suggesting activity may still be reshaping the land today.