Constitutive modeling based on continuum mechanics theory has been a classical approach for modeling the mechanical responses of materials. However, when constitutive laws are unknown or when defects and/or high degrees of heterogeneity are present, these classical models may become inaccurate. In this work, we propose to use data-driven modeling, which directly utilizes high-fidelity simulation and/or experimental measurements to predict a material's response without using conventional constitutive models. Specifically, the material response is modeled by learning the implicit mappings between loading conditions and the resultant displacement and/or damage fields, with the neural network serving as a surrogate for a solution operator. To model the complex responses due to material heterogeneity and defects, we develop a novel deep neural operator architecture, which we coin as the Implicit Fourier Neural Operator (IFNO). In the IFNO, the increment between layers is modeled as an integral operator to capture the long-range dependencies in the feature space. As the network gets deeper, the limit of IFNO becomes a fixed point equation that yields an implicit neural operator and naturally mimics the displacement/damage fields solving procedure in material modeling problems. We demonstrate the performance of our proposed method for a number of examples, including hyperelastic, anisotropic and brittle materials. As an application, we further employ the proposed approach to learn the material models directly from digital image correlation (DIC) tracking measurements, and show that the learned solution operators substantially outperform the conventional constitutive models in predicting displacement fields.

Control Barrier Functions (CBFs) are becoming popular tools in guaranteeing safety for nonlinear systems and constraints, and they can reduce a constrained optimal control problem into a sequence of Quadratic Programs (QPs) for affine control systems. The recently proposed High Order Control Barrier Functions (HOCBFs) work for arbitrary relative degree constraints. One of the challenges in a HOCBF is to address the relative degree problem when a system has multiple control inputs, i.e., the relative degree could be defined with respect to different components of the control vector. This paper proposes two methods for HOCBFs to deal with systems with multiple control inputs: a general integral control method and a method which is simpler but limited to specific classes of physical systems. When control bounds are involved, the feasibility of the above mentioned QPs can also be significantly improved with the proposed methods. We illustrate our approaches on a unicyle model with two control inputs, and compare the two proposed methods to demonstrate their effectiveness and performance.

The interplay between models and observations is a cornerstone of the scientific method, aiming to inform which theoretical models are reflected in the observed data. Within cosmology, as both models and observations have substantially increased in complexity over time, the tools needed to enable a rigorous comparison have required updating as well. With an eye towards the next decade in cosmology, the vast data volumes to be delivered by ongoing and upcoming surveys, as well as the ever-expanding theoretical search-space, motivate a re-thinking of the statistical machinery used. In particular, we are now at a crucial juncture where we may be limited by the statistical and data-driven tools themselves rather than the quality or volume of the available data. Methods based on artificial intelligence (AI) and machine learning (ML) have recently emerged as promising tools for cosmological applications, demonstrating the ability to overcome some of the computational bottlenecks associated with traditional statistical techniques. Machine learning is starting to see increased adoption across different subfields of and for various applications within cosmology. At the same time, the nascent and emergent nature of practical artificial intelligence motivates careful continued development and significant care when it comes to their application in the sciences, as well as cognizance of their potential for broader societal impact. In this white paper, we provide an overview of some of the ways machine learning methods are becoming increasingly central to the way cosmological data is collected, analyzed, and interpreted. Along the way, we highlight our vision for necessary developments, framing these as recommendations--both technological as well as sociological--for the widespread safe and equitable adoption of machine learning methods within cosmology in the coming decade.

While automotive technology has not traditionally been a core feature at South by Southwest, the annual festival in Austin has, in many respects, been an innovator in finding ways to get people around town. From SXSW shuttle busses, rickshaw-inspired pedicabs and the proliferation of scooters and motorized bicycles, the City of Austin, and South-By in particular, have always made festival navigation relatively simple. For starters, the conference features a dedicated mobility track with U.S. Secretary of Transportation Pete Buttigieg speaking on Wednesday. A full lineup of panels covering topics like last-mile mobility, the hyperloop, autonomous vehicles, EVs, electric bicycles, personal air vehicles, delivery, drones, and commercial space travel are also featured. For the first time this year, a major part of the conference will feature the involvement and participation of multiple automotive manufacturers.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A woman stabbed her date whom she had met online in retaliation for the 2020 death of an Iranian military leader killed in an American drone strike, police said.Nika Nika Nikoubin is scheduled to appear in court for a preliminary hearing March 24. Nika Nikoubin, 21, has been charged with attempted murder, battery with a deadly weapon and burglary, KLAS-TV reported. Nikoubin and the man met online on a dating website, Henderson police wrote in an arrest report.

Ukraine is using Clearview AI's facial recognition software to uncover Russian assailants and identify Ukrainians who've sadly lost their lives in the conflict. The company's chief executive, Hoan Ton-That, told Reuters that Ukraine's defence ministry began using the software on Saturday. Clearview AI's facial recognition system is controversial but indisputably powerful--using billions of images scraped from the web to identify just about anyone. Ton-That says that Clearview has more than two billion images from Russian social media service VKontakte alone. Reuters says that Ton-That sent a letter to Ukrainian authorities offering Clearview AI's assistance.

Anti-corruption measures – Canada's placing among the top 10 countries on the Transparency International Corruption Perception Index in prior years provided the impression that the country was generally free of corruption. Canada, on the other hand, has dropped in recent years, ranking 11th in 2020 and 13th in 2021. This slow drop has been linked to, among other things, Canada's lax implementation of the OECD Anti-Bribery Convention, an anti-corruption treaty that mandates countries to outlaw the bribery of foreign public officials. Bribery of foreign public officials is a particular source of concern for Canada, given Canadian businesses' proclivity for bribery and corruption in developing countries. Given that this is a persistent issue, how can businesses conduct themselves responsibly?

Ukraine is now using Clearview AI's facial recognition technology for purposes such as identifying Russian soldiers, its CEO claimed. Hoan Ton-That told Reuters the company offered Ukraine's defense ministry free access to its system following the invasion by Russia. According to the report, Clearview suggested Ukraine could use the tech to reunite refugees with family members, fight misinformation, assess at checkpoints whether someone is a person of interest and to identify dead bodies. The company hasn't offered its technology to Russia. Engadget has contacted the defense ministry for comment.

Who needs the metaverse when your life can be as weird as Palmer Luckey's? In 2016, the founder of the virtual reality startup Oculus was unceremoniously pushed out of the company that acquired it--Facebook. Zuckerberg and his minions had soured on Luckey's Trump-embracing politics. At the time, few would have guessed that the fanciful technologist, gamer, and cosplayer who once posed on a virtual beach on the cover of Time Magazine would become a major figure in defense technology. But Luckey quickly cofounded Anduril, a Founders Fund-backed startup devoted to cutting-edge military tech. Lucky is now winning billion-dollar Pentagon contracts.

A Vietnamese woman who lives in Miyagi Prefecture sent a message to the "letters from readers" section of the Kahoku Shimpo expressing a grievance. The letter explained that when she informed the Japanese-language school where she had been studying that she had to cancel her enrollment because of financial hardship stemming from the pandemic, she was about to pay a cancellation fee of ¥3 million, after being pressured by the school to do so. The school said that it asks for such a fee to discourage students from quitting the school and switching to a work visa, but experts say the approach takes advantage of students' weak position and is a violation of their human rights. In November 2020, the woman, who is in her 30s, obtained a student visa, came to Japan and entered the Japanese-language school in Sendai's Aoba Ward. She planned to study Japanese for two years with a goal of becoming a nursing care worker in Japan.