Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Iranian attacks in the waterways of the Middle East and elsewhere in the region "have the attention of everyone" as tensions rise over Tehran's advancing nuclear program, the head of the U.S. Navy's 5th Fleet said Tuesday. Vice Adm. Brad Cooper also told The Associated Press that he's seen a rise in what he described as Iran's "malign activities" in the region over his two years leading the Bahrain-based 5th Fleet. While Cooper pointed to recent seizures of weapons by American and allied forces in the region as a success, he acknowledged that Iran has been able to carry out drone attacks targeting shipping in the Mideast and other assaults in the region.

The government decided Tuesday on draft legislation to screen and certify Japanese-language schools to ensure their quality by setting standards including the number of teachers and educational content. In the legislation, eyed for enforcement in April 2024 after its enactment in the current parliament session, the government also requires instructors at certified schools to obtain a new national qualification for teaching Japanese. The government's strengthened surveillance over the Japanese-language schools follows cases of questionable management, such as with one operator which was found to be allegedly making illegal job arrangements for foreign students in 2017. 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.

Dr. Axel Spies is a German attorney (Rechtsanwalt) in Washington, DC, and co-publisher of the German journals Multi-Media-Recht (MMR) and Zeitschrift für Datenschutz (ZD). The impact of the Artificial Intelligence Act (AIA) proposed by the European Commission, and currently debated at the European Parliament (EP), has been underestimated in the United States. With approximately 3,000 amendments that must be reconciled, the AIA represents the first attempt to regulate artificial intelligence (AI) by a uniform law from cradle to grave. The AIA focuses on the providers of AI services that put them on the market or use them for their own purposes. Germany is actively contributing to the debate: AI is mentioned in the federal government's Coalition Treaty as a "digital key technology" and a European AIA is generally supported.

'Gutfeld!' panelists weigh in on a new survey claiming almost half of recent college graduates aren't'emotionally' prepared for a 9-5 job. Student senator Mike Abel at the University of Houston successfully challenged the Student Government Association's (SGA) "Respect for Pronouns" bill that would have mandated the use of others members' preferred pronouns. The bill also calls for "Name tags containing the proper pronouns will be given to every member of the Student Government Association" and it "strongly recommend[ed]" to list pronouns during Zoom meetings. The SGA's Supreme Court is poised to rule in favor of Abel, determining that such legislation constituted a violation of the students' first amendment rights, Campus Reform reported Friday. Specifically, the SGA Supreme Court is said to have found that the last sentence of the legislation, which would have compelled speech from the organization's members, was a violation.

Supported by Rethink Priorities • This is part of a weekly series summarizing the top posts on the EA and LW forums - you can see the full collection here. The first post includes some details on pur…

Robust quantification of forest carbon stocks and their dynamics is important for climate change mitigation and adaptation strategies [FAO and UNEP, 2020]. The Paris Agreement [United Nations / Framework Convention on Climate Change, 2015] and the IPCC [Shukla et al., 2019] acknowledge that climate change mitigation goals cannot be achieved without a substantial contribution from forests. Spatial details in the carbon budget of forests are necessary to encourage transformational actions towards a sustainable forest sector [Harris et al., 2021, 2012]. Currently, many countries do not have nationally specific forest carbon accumulation rates but rather rely on default rates from the IPCC 2018 [Masson-Delmotte et al., 2019, Requena Suarez et al., 2019]), without accounting for finer-scale variations of carbon stocks [Cook-Patton et al., 2020]. Precise spatio-temporal monitoring of forest carbon dynamics at large scales has proven to be challenging [Erb et al., 2018, Griscom et al., 2017]. This is due to the complex structure of forests, topographic features, and land management practices [Tubiello et al., 2021, Lewis et al., 2019]. Technological developments in remote sensing and the concurrent increased availability of field-based measurements have led to an improvement in estimating carbon stocks using remote sensing observations of forest attributes that serve as proxy for above-ground biomass (AGB) [Knapp et al., 2018, Bouvier et al., 2015, Pan et al., 2013]. Currently, three remote sensing techniques are applied to collect data for AGB estimates: i) passive optical imagery, ii) synthetic aperture radar (SAR), and iii) light detection and ranging (LiDAR).

Abstract: An Artificially Intelligent system (an AI) has debatable personhood if it's epistemically possible either that the AI is a person or that it falls far short of personhood. Debatable personhood is a likely outcome of AI development and might arise soon. Debatable AI personhood throws us into a catastrophic moral dilemma: Either treat the systems as moral persons and risk sacrificing real human interests for the sake of entities without interests worth the sacrifice, or don't treat the systems as moral persons and risk perpetrating grievous moral wrongs against them. The moral issues become even more perplexing if we consider cases of possibly conscious AI that are subhuman, superhuman, or highly divergent from us in their morally relevant properties. We might soon build artificially intelligent entities - AIs - of debatable personhood. Our systems and habits of ethical thinking are currently as unprepared for this decision as medieval physics was for space flight.

Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snapshot mosaic sensors offer a promising approach due to their fast acquisition speed and compact size. However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images. Most state-of-the-art demosaicking algorithms require ground-truth training data with paired snapshot and high-resolution hyperspectral images, but such imagery pairs with the exact same scene are physically impossible to acquire in intraoperative settings. In this work, we present a fully unsupervised hyperspectral image demosaicking algorithm which only requires exemplar snapshot images for training purposes. We regard hyperspectral demosaicking as an ill-posed linear inverse problem which we solve using a deep neural network. We take advantage of the spectral correlation occurring in natural scenes to design a novel inter spectral band regularisation term based on spatial gradient consistency. By combining our proposed term with standard regularisation techniques and exploiting a standard data fidelity term, we obtain an unsupervised loss function for training deep neural networks, which allows us to achieve real-time hyperspectral image demosaicking. Quantitative results on hyperspetral image datasets show that our unsupervised demosaicking approach can achieve similar performance to its supervised counter-part, and significantly outperform linear demosaicking. A qualitative user study on real snapshot hyperspectral surgical images confirms the results from the quantitative analysis. Our results suggest that the proposed unsupervised algorithm can achieve promising hyperspectral demosaicking in real-time thus advancing the suitability of the modality for intraoperative use.

Crash sequence analysis has been shown in prior studies to be useful for characterizing crashes and identifying safety countermeasures. Sequence analysis is highly domain-specific, but its various techniques have not been evaluated for adaptation to crash sequences. This paper evaluates the impact of encoding and dissimilarity measures on crash sequence analysis and clustering. Sequence data of interstate highway, single-vehicle crashes in the United States, from 2016-2018, were studied. Two encoding schemes and five optimal matching based dissimilarity measures were compared by evaluating the sequence clustering results. The five dissimilarity measures were categorized into two groups based on correlations between dissimilarity matrices. The optimal dissimilarity measure and encoding scheme were identified based on the agreements with a benchmark crash categorization. The transition-rate-based, localized optimal matching dissimilarity and consolidated encoding scheme had the highest agreement with the benchmark. Evaluation results indicate that the selection of dissimilarity measure and encoding scheme determines the results of sequence clustering and crash characterization. A dissimilarity measure that considers the relationships between events and domain context tends to perform well in crash sequence clustering. An encoding scheme that consolidates similar events naturally takes domain context into consideration.

A common problem when forecasting rare events, such as recessions, is limited data availability. Recent advancements in deep learning and generative adversarial networks (GANs) make it possible to produce high-fidelity synthetic data in large quantities. This paper uses a model called DoppelGANger, a GAN tailored to producing synthetic time series data, to generate synthetic Treasury yield time series and associated recession indicators. It is then shown that short-range forecasting performance for Treasury yields is improved for models trained on synthetic data relative to models trained only on real data. Finally, synthetic recession conditions are produced and used to train classification models to predict the probability of a future recession. It is shown that training models on synthetic recessions can improve a model's ability to predict future recessions over a model trained only on real data.