Papalambrou, who works at the University's School of Naval Architecture and Marine Engineering, says the custom-made monitoring system -- with solar power, recognition software and luminosity-triggered lens wipers to unclog debris -- could be a template deployed to other underwater sites.

Leading conservationists have emphasized that conservation's priority is the protection of species and populations, not the welfare of individual nonhuman animals (hereafter “animals”) ([ 1 ][1]–[ 3 ][2]). Although individual conservationists often harbor concern for animal welfare, conservation organizations and scientists frequently downplay or ignore the ethical implications of actions they promote that harm individual animals, from culling and sport hunting to the discontinuation of wildlife rescue from oil spills ([ 3 ][2]–[ 5 ][3]). A growing body of scientific evidence should prompt conservation organizations to reconsider their inattention to animal welfare. A wide variety of vertebrate species (and perhaps some invertebrates) are capable of experiencing physical and emotional pain, engaging in substantive relationships, and executing cognitively complex tasks ([ 6 ][4]–[ 8 ][5]), bolstering claims that animal well-being is morally significant and policy-relevant. Addressing animal welfare in conservation would be politically challenging, and given the central role of predation and competition in ecosystems, conservation science cannot altogether avoid difficult decisions; harming animals can be a necessary step toward a worthwhile goal. Despite these trade-offs, conservation organizations face a singular opportunity to reshape conservation into a discipline that promotes both the quantity of species and the quality of animal life. Although humans are exceptional in many ways, the once-popular belief that it is unscientific to ascribe emotions or thoughts to animals is now regarded as inconsistent with evolutionary theory, experimental evidence, and any reasonable burden of proof ([ 9 ][6], [ 10 ][7]). Commonalities in basic neural functioning across vertebrate species, ranging from fish to mammals, suggest similarities in experiential capacities ([ 9 ][6], [ 11 ][8]). Evidence indicates that the thalamocingulate division of the limbic system and the anterior cingulate cortex evolved prior to the radiation of mammals, with all studied mammals sharing seven basic emotional systems including joy, fear, grief, parental nurturance, and playfulness. Deep neurological similarities underpin the extensive use of mammalian models in medical research, including for depression and anxiety. Further, research indicates that convergent evolution of the mammalian cortex and avian pallium has led to similar neural architecture between birds and mammals ([ 12 ][9]), with birds exhibiting similar forms of some affective states, consciousness, and attachment-oriented behaviors. Recent research has also demonstrated that various animal species are cognitively sophisticated, with findings including tool use in diverse taxa; spontaneous insight and innovative behavior; self-recognition and metacognition; collaboration to solve unfamiliar tasks; planning for future events; political strategy; empathetic concern; and the ability to recognize hundreds of human words (see supplementary materials). The accumulating scientific evidence that animals have vibrant inner lives was anticipated by modern philosophers, the field of animal welfare science, and numerous world cultures that have accorded moral relevance to the quality of animal life. Yet with limited exceptions, the most prominent international conservation organizations do not attempt to promote animal welfare in their mission or vision statements or to safeguard animal welfare in their readily available public policies. This contrasts with often robust ethics policies on a range of other social and environmental issues. From one perspective, the omission of animal welfare is befuddling. Conservationists must believe that animals deserve protection from human-induced harm; by combating habitat destruction and poaching, conservation often already promotes wild animal welfare. Officially recognizing the imperative of protecting animals as individuals could broaden conservation's constituency. Whereas the public often finds the value of biodiversity to be abstract and unrelatable, many people are concerned when human actions unnecessarily violate the freedom and well-being of wild animals. Conservation organizations have realized this, often using stories of human-induced suffering of individual animals to generate empathy and raise funds. Yet, owing to the pervasiveness of activities that compromise animal welfare, many conservation organizations could face a variety of political risks and programmatic complications if they were to officially endorse the legitimacy of animal welfare concerns. Conservation organizations often depend on a diverse coalition of political interests, including groups that habitually harm animals. For instance, the U.S. government is one of the largest bilateral sources of funding for international conservation largely because the U.S. Congress's International Conservation Caucus is among the largest bipartisan caucuses in the legislature, with many participants being vocal supporters of recreational hunting and fishing. For conservation organizations to acknowledge that killing animals for recreation might have moral implications ([ 4 ][10]) could complicate these politically important relationships in both halls of power and remote settings globally. There are well-evidenced concerns for how wild animals, especially wide-ranging species like elephants, some cetaceans, and carnivores, fare in captivity, but zoos can also inspire considerable support for wildlife conservation. Finally, conservation organizations and conservationists themselves (like other environmentalists) often regularly purchase factory farm products even though factory farms pose serious concerns about human-induced animal suffering. For conservation organizations, officially acknowledging the moral significance of animal welfare could complicate how many conservationists see themselves and generally cause discontent within their communities. Furthermore, conservation programming takes place in complex socioecological systems that pose practical trade-offs between animal welfare and biodiversity conservation or even human rights. At its extreme, efforts to curtail hunting and fishing in the world's poor rural areas could unjustly harm communities that rely on bushmeat or wild fish for their nutrition and livelihoods. Conservation groups can be seen as elitist, out-of-touch, or culturally oppressive where they oppose the killing of dangerous animals like elephants or traditional practices like subsistence whaling—such conflicts could become more common if conservation organizations consistently prioritize the interests of individual animals. In settings where wildlife tourism is not profitable, prohibiting sport hunting could deprive organizations of funding to protect wildlife from poaching, perversely leading to an increase in the killing of wildlife. Additionally, there are many examples of direct trade-offs between animal welfare and traditional conservation objectives like preventing extinction and maintaining ecosystem function. Invasive mammals—like goats on the Galapagos or feral cats on remote islands—suffer during eradication campaigns, but there may be no other way to secure the future for endangered native species. Programs to cull white-tailed deer similarly might be necessary to ensure the regeneration of forests in the eastern United States. Ecological research and reintroduction programs can also involve duress for the animals involved. Despite challenges posed by these trade-offs, conservation science should adjust its priorities in response to the overwhelming evidence that animals think and feel. Only explicit consideration of animal welfare in decision-making can ensure that conservation organizations do not unnecessarily compromise the well-being of individual animals. As a community, conservation organizations should set in motion three processes to (i) develop consensus principles, (ii) build the evidence base to identify best practices, and (iii) develop advisory institutions that can advance best practices. Each of these should engage diverse voices, including representatives from different cultures, countries with diverse political realities, and researchers and practitioners from both animal welfare science and conservation. The process of developing consensus principles to bring animal welfare concerns into conservation science has already begun, with ideas coming from national regulatory bodies, nongovernmental organizations concerned with wild animal welfare, the World Association of Zoos and Aquariums ([ 13 ][11]), animal welfare experts ([ 14 ][12], [ 15 ][13]), and the burgeoning compassionate conservation movement ([ 3 ][2], [ 4 ][10]). Conservation and animal welfare organizations should collaborate to systematically refine and select practically applicable ethical principles. Given the diverse cultural practices and economic systems that involve harm to animals, prohibitions on animal captivity, killing animals, and eating meat are unlikely to gain consensus support—but that need not prevent constructive discussions on minimizing human-induced suffering of animals, general agreement to minimize suffering during killing, and principles guiding the circumstances when killing animals might be acceptable. Animal welfare principles can alert conservationists to when the harm an activity causes to individual animals outweighs the benefits to biodiversity. Second, international conservation and animal welfare organizations should fund the development of an evidence base for how best to engage with wildlife in a way that minimizes avoidable suffering. Again, scientists have begun this process ([ 4 ][10], [ 13 ][11], [ 14 ][12])—but the evidence compiled must come from more diverse settings and situations and reflect practical limitations and trade-offs faced by conservation organizations in places where even human rights are not adequately realized. In addition to improving conservation practice, such evidence would help animal welfare organizations recognize where the protection of biodiversity, ecological function, and local communities might necessitate harming individual animals. This evidence review process would also highlight areas of research that could help resolve ethical dilemmas posed by conservation programming. International conservation bodies should also work with animal welfare scientists to establish advisory committees that review (voluntarily submitted) conservation project proposals to assess whether they satisfy principles of animal welfare. The process could be modeled as a voluntary version of the Institutional Animal Care and Use Committee that reviews animal research in the United States, working to promote best practices, build precedent, and collect real-life cases that can improve the evidence base. The committees' recommendations should provide a basis for informed debate about the trade-offs between wildlife conservation and animal welfare, helping better define whether the suffering of individual animals might be commensurate with conservation benefits ([ 14 ][12]). Over time, the cumulative experience of these committees should allow conservation organizations to recommend evidence-based animal welfare safeguards that can fit into the broader category of social and environmental safeguards, much like policies striving to minimize carbon emissions or protect human rights in conservation and development. Inevitably, these processes will take time. In the meanwhile, conservation organizations can take two steps toward building a better world for all animals: publicly commit to considering animal welfare in their decisions, and adopt policies against the purchase of factory farm meat where less harmful alternatives are available. Given the implications of factory farming not just for animal welfare but also for climate change and biodiversity, such action would further demonstrate the sincerity of conservation organizations' pursuit of a more just and sustainable planet. [science.sciencemag.org/content/369/6504/629/suppl/DC1][14] 1. [↵][15]1. M. E. Soule , Bioscience 35, 727 (1985). [OpenUrl][16][CrossRef][17][Web of Science][18] 2. 1. P. Kareiva, 2. M. Marvier , Bioscience 62, 962 (2012). [OpenUrl][19][CrossRef][20][Web of Science][21] 3. [↵][22]1. D. Ramp, 2. M. Bekoff , Bioscience 65, 323 (2015). [OpenUrl][23][CrossRef][24] 4. [↵][25]1. A. D. Wallach et al ., Conserv. Biol. 32, 1255 (2018). [OpenUrl][26] 5. [↵][27]1. P. 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It's no surprise that AI has a carbon footprint, which refers to the amount of greenhouse gases (carbon dioxide and methane, primarily) that producing and consuming AI releases into the atmosphere. In fact, training AI models requires so much computing power, some researchers have argued that the environmental costs outweigh the benefits. However, I believe they've not only underestimated the benefits of AI, but also overlooked the many ways that model training is becoming more efficient. Greenhouse gases are what economists refer to as an "externality" -- a cost borne inadvertently by society at large, such as through the adverse impact of global warming, but inflicted on us all by private participants who have little incentive to refrain from the offending activity. Typically, public utilities emit these gases when they burn fossil fuels in order to generate electricity that powers the data centers, server farms, and other computing platforms upon which AI runs. During the past few years, AI has been unfairly stigmatized as a major contributor to global warming, owing to what some observers regard as its inordinate consumption of energy in the process of model training.

A major breakthrough with fluorescents has allowed scientists to create the "brightest known materials in existence". The advance paves the way for a new class of materials that transform fluorescent dyes into solid objects, according to researchers from Indiana University in the US and the University of Copenhagen. There are more than 100,000 fluorescent dyes in existence but until now it was not possible to mix them in a predictable way in order to produce solid optical materials. The researchers formulated the new class of materials, called small-molecule ionic isolation lattices (SMILES), using positively charged fluorescent dyes to create a solid material. "These materials have potential applications in any technology that needs bright fluorescence or calls for designing optical properties, including solar energy harvesting, bioimaging, and lasers," said Amar Flood, a chemist at Indiana University and co-author of the study.

ALBUQUERQUE, N.M. -- A proposed project to help scientists use the laws of physics to view multiscale physical events with a clarity never before achieved has won an Early Career Research Program award from the Department of Energy for Sandia National Laboratories researcher Nathaniel Trask. Such work may require observations over a millionfold change in scale, with features ranging from the meter- to microscale. Sandia National Laboratories researcher Nat Trask, winner of the Department of Energy's Early Career award, is researching how to clearly present huge changes in scale. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Consider the following problem, which we term Offline Meta Reinforcement Learning (OMRL): given the complete training histories of $N$ conventional RL agents, trained on $N$ different tasks, design a learning agent that can quickly maximize reward in a new, unseen task from the same task distribution. In particular, while each conventional RL agent explored and exploited its own different task, the OMRL agent must identify regularities in the data that lead to effective exploration/exploitation in the unseen task. To solve OMRL, we take a Bayesian RL (BRL) view, and seek to learn a Bayes-optimal policy from the offline data. We extend the recently proposed VariBAD BRL algorithm to the off-policy setting, and demonstrate learning of Bayes-optimal exploration strategies from offline data using deep neural networks. Furthermore, when applied to the online meta-RL setting (agent simultaneously collects data and improves its meta-RL policy), our method is significantly more sample efficient than the conventional VariBAD.

Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many time series databases have only a limited number of time series. In this study, we propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of the GFM models in less data-abundant settings. We use three time series augmentation techniques: GRATIS, moving block bootstrap (MBB), and dynamic time warping barycentric averaging (DBA) to synthetically generate a collection of time series. The knowledge acquired from these augmented time series is then transferred to the original dataset using two different approaches: the pooled approach and the transfer learning approach. When building GFMs, in the pooled approach, we train a model on the augmented time series alongside the original time series dataset, whereas in the transfer learning approach, we adapt a pre-trained model to the new dataset. In our evaluation on competition and real-world time series datasets, our proposed variants can significantly improve the baseline accuracy of GFM models and outperform state-of-the-art univariate forecasting methods.

StartX startup Buzz Solutions out of Stanford, California just introduced its AI solution to help utilities quickly spot powerline and grid faults so repairs can be made before wildfires start. AI and machine vision technology, like that from Buzz Solutions, can take data from sources like ... [ ] this drone, helicopters, and aircraft to determine characteristics like powerline and grid faults so repairs can be made before wildfires start. Their unique platform uses AI and machine vision technology to analyze millions of images of powerlines and towers from drones, helicopters, and aircraft to find dangerous faults and flaws as well as overgrown vegetation, in and around the grid infrastructure to help utilities identify problem areas and repair them before a fire starts. This system can do the analysis at half the cost and in a fraction of the time compared to humans, hours to days not months to years. The California Department of Forestry and Fire Protection determined that PG&E's transmission lines were at fault for the huge Kincade Fire in Sonoma, California last year.

The field development optimization (FDO) problem represents a challenging mixed-integer nonlinear programming (MINLP) problem in which we seek to obtain the number of wells, their type, location, and drilling sequence that maximizes an economic metric. Evolutionary optimization algorithms have been effectively applied to solve the FDO problem, however, these methods provide only a deterministic (single) solution which are generally not robust towards small changes in the problem setup. In this work, the goal is to apply convolutional neural network-based (CNN) deep reinforcement learning (DRL) algorithms to the field development optimization problem in order to obtain a policy that maps from different states or representation of the underlying geological model to optimal decisions. The proximal policy optimization (PPO) algorithm is considered with two CNN architectures of varying number of layers and composition. Both networks obtained policies that provide satisfactory results when compared to a hybrid particle swarm optimization - mesh adaptive direct search (PSO-MADS) algorithm that has been shown to be effective at solving the FDO problem.

The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization. Unlike existing tutorials that mainly focus on {\it algorithmic procedures} for a small set of problems, e.g., nonnegativity or sparsity-constrained factorization, we take a {\it top-down} approach: we start with general optimization theory (e.g., inexact and accelerated block coordinate descent, stochastic optimization, and Gauss-Newton methods) that covers a wide range of factorization problems with diverse constraints and regularization terms of engineering interest. Then, we go `under the hood' to showcase specific algorithm design under these introduced principles. We pay a particular attention to recent algorithmic developments in structured tensor and matrix factorization (e.g., random sketching and adaptive step size based stochastic optimization and structure-exploiting second-order algorithms), which are the state of the art---yet much less touched upon in the literature compared to {\it block coordinate descent} (BCD)-based methods. We expect that the article to have an educational values in the field of structured factorization and hope to stimulate more research in this important and exciting direction.