A new study found that all of these kinds of motion are well represented by a single mathematical model. "This didn't come out of nowhere -- this is from our real robot data," said Dan Zhao, first author of the study in the Proceedings of the National Academy of Sciences and a recent Ph.D. graduate in mechanical engineering at the University of Michigan. "Even when the robot looks like it's sliding, like its feet are slipping, its velocity is still proportional to how quickly it's moving its body." Unlike the dynamic motion of gliding birds and sharks and galloping horses -- where speed is driven, at least in part, by momentum -- every bit of speed for ants, centipedes, snakes and swimming microbes is driven by changing the shape of the body. This is known as kinematic motion.

As part of the $1 billion Build Back Better Regional Challenge, the Georgia Artificial Intelligence Manufacturing Technology Corridor (GA-AIM) is one of 21 winning projects announced recently by the Biden administration. The GA-AIM project will receive $65 million to support a statewide program that combines artificial intelligence with manufacturing innovations, in addition to transformational workforce and outreach programs. The project is led by the Schools of Mechanical Engineering and Materials Science and Engineering at Georgia Tech, joined by a long list of partners including the Georgia Department of Community Affairs, the Georgia Cyber Center, the Southwest Georgia Regional Commission, the Technical College System of Georgia and the University of Georgia among others.

The immigration agency stripped a Japanese language school of its certification Wednesday after it found a staff member physically restrained a male Vietnamese student last October. The staff member at the Nishinihon International Education Institute in the city of Fukuoka was found to have restrained the student for several hours by connecting his belt to the employee's belt with a chain and padlock, according to the Immigration Services Agency. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this support page.

Teledyne Technologies Inc. (NYSE:TDY) is one of the holdings in the AdvisorShares Drone Technology ETF [NYSE ARCA:UAV], the only ETF dedicated to the drone economy. The AdvisorShares Drone Technology ETF is a thematic investment strategy seeking to capture the growth opportunities in drones and autonomous vehicles (AV). AdvisorShares is a DRONELIFE sponsor." Teledyne FLIR is a global name in thermal and visible imaging. With SIRAS, Teledyne FLIR has equipped a reliable, user-friendly drone with their world-class imaging solutions, including visible and thermal cameras; and features designed to meet the security needs of public safety and government agencies – all with U.S.-based service and support. Product Manager Kelly Brodbeck explains that SIRAS was developed around the end user. "We maintain focus groups with end users all the time," he says. "We benefit from all of our contacts – we're constantly talking to people and looking for consistencies across their areas of expertise: public safety, oil and gas, industry." The decision not to use geofencing was the direct result of user feedback. "This is about giving the pilot control: for public safety in particular, users really don't want geofencing – that's just unacceptable.

In the early twenty-first century, the train of progress is again pulling out of the station – and this will probably be the last train ever to leave the station called Homo Sapiens. Those who miss this train will never get a second chance. In order to get a seat on it you need to understand twenty-first-century technology, and in particular the power of bio-technology and computer algorithms. AI has been evolving continuously and is an emerging concept whose practical implications in every walk of life have been a glaring reality and is expected to continue to have a diverse impact on most fields in both private and public sectors. Defence and security will be on the leading AI experimental and user fields.

One possible way of making thermal processing controllable is to gather real-time information on the product's current state. Often, sensory equipment cannot capture all relevant information easily or at all. Digital Twins close this gap with virtual probes in real-time simulations, synchronized with the process. This paper proposes a physics-based, data-driven Digital Twin framework for autonomous food processing. We suggest a lean Digital Twin concept that is executable at the device level, entailing minimal computational load, data storage, and sensor data requirements. This study focuses on a parsimonious experimental design for training non-intrusive reduced-order models (ROMs) of a thermal process. A correlation ($R=-0.76$) between a high standard deviation of the surface temperatures in the training data and a low root mean square error in ROM testing enables efficient selection of training data. The mean test root mean square error of the best ROM is less than 1 Kelvin (0.2 % mean average percentage error) on representative test sets. Simulation speed-ups of Sp $\approx$ 1.8E4 allow on-device model predictive control. The proposed Digital Twin framework is designed to be applicable within the industry. Typically, non-intrusive reduced-order modeling is required as soon as the modeling of the process is performed in software, where root-level access to the solver is not provided, such as commercial simulation software. The data-driven training of the reduced-order model is achieved with only one data set, as correlations are utilized to predict the training success a priori.

In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This work presents a novel approach, physics-guided adversarial machine learning (ML), that improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously-uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both models and in improving their propensity to be consistent with physics domain knowledge.

Academic and scientific research activities have a growing economic importance, requiring larger outlays and investments both in advanced and emerging nations. In both cases it is a matter of national prestige but more than that, of strategic relevance, since the presence of large numbers of highly educated citizens contributing to the advance of human knowledge has well-established impacts on technological and industrial capabilities. These, in turn, are highly relevant to ensure the competitiveness and the economic security of nations as well as yielding other benefits to the economy (Dasgupta and David (1994) and Salter and Martin (2001)). The care and promotion of research activities should thus be a focus of public policies aimed at ensuring social and economic development (Stephan (2012) and Etzkowitz (2013)). A particularly pressing issue in this matter is to assess the quality of the production generated by researchers in all fields of knowledge. On one hand, it is of interest to detect areas in which nationals have international impact, as to concentrate resources on them.

Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation. This is likely tied to implicit gender biases against women in authority. In this work, we present a comprehensive study of gender biases that appear in online political discussion. To this end, we collect 10 million comments on Reddit in conversations about male and female politicians, which enables an exhaustive study of automatic gender bias detection. We address not only misogynistic language, but also other manifestations of bias, like benevolent sexism in the form of seemingly positive sentiment and dominance attributed to female politicians, or differences in descriptor attribution. Finally, we conduct a multi-faceted study of gender bias towards politicians investigating both linguistic and extra-linguistic cues. We assess 5 different types of gender bias, evaluating coverage, combinatorial, nominal, sentimental, and lexical biases extant in social media language and discourse. Overall, we find that, contrary to previous research, coverage and sentiment biases suggest equal public interest in female politicians. Rather than overt hostile or benevolent sexism, the results of the nominal and lexical analyses suggest this interest is not as professional or respectful as that expressed about male politicians. Female politicians are often named by their first names and are described in relation to their body, clothing, or family; this is a treatment that is not similarly extended to men. On the now banned far-right subreddits, this disparity is greatest, though differences in gender biases still appear in the right and left-leaning subreddits. We release the curated dataset to the public for future studies.

How to integrate an emerging and all-pervasive technology such as AI into the structures and operations of our society is a question of contemporary politics, science and public debate. It has produced a considerable amount of international academic literature from different disciplines. This article analyzes the academic debate around the regulation of artificial intelligence (AI). The systematic review comprises a sample of 73 peer-reviewed journal articles published between January 1st, 2016, and December 31st, 2020. The analysis concentrates on societal risks and harms, questions of regulatory responsibility, and possible adequate policy frameworks, including risk-based and principle-based approaches. The main interests are proposed regulatory approaches and instruments. Various forms of interventions such as bans, approvals, standard-setting, and disclosure are presented. The assessments of the included papers indicate the complexity of the field, which shows its prematurity and the remaining lack of clarity. By presenting a structured analysis of the academic debate, we contribute both empirically and conceptually to a better understanding of the nexus of AI and regulation and the underlying normative decisions. A comparison of the scientific proposals with the proposed European AI regulation illustrates the specific approach of the regulation, its strengths and weaknesses.