Facial expression recognition (FER) must remain robust under both cultural variation and perceptually degraded visual conditions, yet most existing evaluations assume homogeneous data and high-quality imagery. We introduce an agent-based, streaming benchmark that reveals how cross-cultural composition and progressive blurring interact to shape face recognition robustness. Each agent operates in a frozen CLIP feature space with a lightweight residual adapter trained online at sigma=0 and fixed during testing. Agents move and interact on a 5x5 lattice, while the environment provides inputs with sigma-scheduled Gaussian blur. We examine monocultural populations (Western-only, Asian-only) and mixed environments with balanced (5/5) and imbalanced (8/2, 2/8) compositions, as well as different spatial contact structures. Results show clear asymmetric degradation curves between cultural groups: JAFFE (Asian) populations maintain higher performance at low blur but exhibit sharper drops at intermediate stages, whereas KDEF (Western) populations degrade more uniformly. Mixed populations exhibit intermediate patterns, with balanced mixtures mitigating early degradation, but imbalanced settings amplify majority-group weaknesses under high blur. These findings quantify how cultural composition and interaction structure influence the robustness of FER as perceptual conditions deteriorate.

Building usable models to run AI algorithms requires not just adequate data to train systems, but also the right hardware subsequently to run them. But because the theoretical and practical are often not the same thing, there is often a gap between what data scientists may hope to do and what they practically do. Today, a startup called Deci that has built a deep learning platform to help bridge that gap -- by building models that can work with the data and hardware that are available to use -- is announcing some funding after finding strong traction for its products with Fortune 500 tech companies running mass-market, AI-based products based on video and other computer vision-based services. The Tel Aviv-based startup has picked up a Series A of $21 million, money that it will be using to continue expanding its product and customer base. Insight Partners is leading the round, with previous backers Square Peg, Emerge and Jibe Ventures, alongside some new backers: Samsung Next, Vintage Investment Partners, and Fort Ross Ventures.

Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay "Excavating AI: The Politics of Images in Machine Learning Training Sets." The present article recounts the creation of the JAFFE dataset and unravels each of Crawford and Paglen's fallacious statements. I also discuss JAFFE more broadly in connection with research on facial expression, affective computing, and human-computer interaction.

A pizza box sized solar panel in orbit is producing enough electricity to power an iPad, according to a succesful test of the technology by the US Navy. The Photovoltaic Radiofrequency Antenna Module (PRAM) was launched in May 2020 attached to a drone that loops around the Earth every 90 minutes and is designed to harness light from the sun to convert to electricity. The 12x12 inch panel is an early experiment for a technology that could one day harness solar radiation from the sun and beam it to anywhere on the Earth. It is designed to make the best use of light in space, which doesn't have to pass through the atmosphere where it loses energy before reaching the ground. The Pentagon one day envisages an array of panels in space that could send power to even the most remote parts of the planet and create a new global power grid.

The "Training Humans" exhibition was held at the Milan Osservatorio from September 2019 to February 2020.

On Saturday, the US Air Force is expected to launch its secret space plane, X-37B, for a long-duration mission in low Earth orbit. The robotic orbiter looks like a smaller version of the space shuttle and has spent nearly eight of the past 10 years in space conducting classified experiments for the military. Almost nothing is known about what X-37B does up there, but ahead of its sixth launch the Air Force gave some rare details about its cargo. In addition to its usual suite of secret military tech, the X-37B will also host a few unclassified experiments during its upcoming sojourn in space. NASA is sending up two experiments to study the effects of radiation on seeds, and the US Air Force Academy is using the space plane to deploy a small research satellite.

In just about every submarine movie, there's a scene where the heroes, aboard one sub, engage the villains, in another, in some sort of deep-sea shootout. Neither side knows exactly where the other is, and the savvier captain usually the good guy -- turns that ambiguity to deadly advantage. A new program seeks to apply artificial intelligence to ocean data and thereby help submarine operators understand where their adversaries are, what they're doing, and what they can see. Even today's best sonar technology doesn't give a sub captain a very good sense of the battlespace, says Jules Jaffe, a research oceanographer at Scripps Oceanography at the University of California San Diego who is embarking on the U.S. Navy program. "What the submariners get is a low-dimensional picture. So if you are towing an array, you get information like bearing and sometimes frequency information," Jaffe said at the Defense One / Nextgov Genius Machines Summit here on Tuesday.

HOUSTON – The style of attack used against oil plants in Saudi Arabia that knocked out half of the country's production on Saturday is unlikely to be a risk in the United States, energy and security experts say. "The U.S. oil industry has a lot of redundancy," said Amy Myers Jaffe, senior fellow for energy at the Council on Foreign Relations. U.S. refineries go offline often, after accidents or storms, with little impact to the market, Jaffe said. Even production in the country's biggest oil field, the Permian Basin in Texas and New Mexico, is spread across thousands of wells in a 75,000- square-mile (194,250-square-kilometer) region. The kind of gas-oil separation facility hit in the attacks in Saudi Arabia is done in smaller plants located across U.S. oil fields.

So does that guy selling sunglasses on the beach. It's why the funny old French bakery around the corner's been running for 15 years. Everyone's talking about it, but what is it? Forging a genuine connection and using that connection to inform your marketing decisions. At its most complex, conversational marketing has become synonymous with cutting-edge technologies for computer-based dialog processing.

Patents are widely regarded as a proxy for inventive output which is valuable and can be commercialized by various means. Individual patent information such as technology field, classification, claims, application jurisdictions are increasingly available as released by different venues. This work has relied on a long-standing hypothesis that the citation received by a patent is a proxy for knowledge flows or impacts of the patent thus is directly related to patent value. This paper does not fall into the line of intensive existing work that test or apply this hypothesis, rather we aim to address the limitation of using so-far received citations for patent valuation. By devising a point process based patent citation type aware (self-citation and non-self-citation) prediction model which incorporates the various information of a patent, we open up the possibility for performing predictive patent valuation which can be especially useful for newly granted patents with emerging technology. Study on real-world data corroborates the efficacy of our approach. Our initiative may also have policy implications for technology markets, patent systems and all other stakeholders. The code and curated data will be available to the research community.