On a recent afternoon in Maracaibo, Venezuela, Alexander Marinez, who has short-cropped black hair and three-to-four-day stubble, sat in front of his computer tracking herbiboars in the mushroom forests on Fossil Island. He pressed down on his glowing mouse, the newest addition to his otherwise timeworn gaming setup. The pixelated character on his computer screen followed the tracks of a hedgehoglike creature with triangular tusks and herbs growing out of its back. Outside Marinez's one-story house, the sun bore down on the dirt road. His home lies about six miles away from the strait that connects the Caribbean Sea with Lake Maracaibo, one of the world's richest sources of oil. The character inspected a tunnel. Suddenly, the herbiboar appeared, and the character attacked, stunning it.

In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, which is labor-intensive to collect and label. What's more, the model can be ruined due to the domain shift between training data and testing data. Text recognition is a broadly studied field in computer vision and suffers from the same problems noted above due to the diversity of fonts and complicated backgrounds. In this paper, we focus on the text recognition problem and mainly make three contributions toward these problems. First, we collect a multi-source domain adaptation dataset for text recognition, including five different domains with over five million images, which is the first multi-domain text recognition dataset to our best knowledge. Secondly, we propose a new method called Meta Self-Learning, which combines the self-learning method with the meta-learning paradigm and achieves a better recognition result under the scene of multi-domain adaptation. Thirdly, extensive experiments are conducted on the dataset to provide a benchmark and also show the effectiveness of our method. The code of our work and dataset are available soon at https://bupt-ai-cz.github.io/Meta-SelfLearning/.

Significant to economic stability across the world, the current scenario in the aqua-farming industry is far from what it was a decade ago. With fewer changes in people and processes, the growth and evolution of the aqua-farming sector have been steady in the past decade. Although the technological advancements have been limited, yet the onset of IoT has triggered the introduction of AI-based process adoptions and automation. The sector is fairly large and deals with the production, and supply of aquatic animals. Fish, shrimp, oysters, and algae farming are closely associated with the global food industry.

Manifold-valued functional data analysis (FDA) recently becomes an active area of research motivated by the raising availability of trajectories or longitudinal data observed on non-linear manifolds. The challenges of analyzing such data come from many aspects, including infinite dimensionality and nonlinearity, as well as time-domain or phase variability. In this paper, we study the amplitude part of manifold-valued functions on $\mathbb{S}^2$, which is invariant to random time warping or re-parameterization. Utilizing the nice geometry of $\mathbb{S}^2$, we develop a set of efficient and accurate tools for temporal alignment of functions, geodesic computing, and sample mean calculation. At the heart of these tools, they rely on gradient descent algorithms with carefully derived gradients. We show the advantages of these newly developed tools over its competitors with extensive simulations and real data and demonstrate the importance of considering the amplitude part of functions instead of mixing it with phase variability in manifold-valued FDA.

"We know that flooding has been getting worse," said study lead author Frances Davenport, a PhD student in Earth system science in Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth). "Our goal was to understand why extreme precipitation is increasing, which in turn could lead to better predictions about future flooding." Among other impacts, global warming is expected to drive heavier rain and snowfall by creating a warmer atmosphere that can hold more moisture. Scientists hypothesize that climate change may affect precipitation in other ways, too, such as changing when and where storms occur. Revealing these impacts has remained difficult, however, in part because global climate models do not necessarily have the spatial resolution to model these regional extreme events.

From lake-draining drought in California to bridge-breaking floods in China, extreme weather is wreaking havoc. Preparing for weather extremes in a changing climate remains a challenge, however, because their causes are complex and their response to global warming is often not well understood. Now, Stanford researchers have developed a machine learning tool to identify conditions for extreme precipitation events in the Midwest, which account for over half of all major U.S. flood disasters. Published in Geophysical Research Letters, their approach is one of the first examples using AI to analyze causes of long-term changes in extreme events and could help make projections of such events more accurate. "We know that flooding has been getting worse," said study lead author Frances Davenport, a Ph.D. student in Earth system science in Stanford's School of Earth, Energy & Environmental Sciences (Stanford Earth).

In estimations of ocean heat content – important when assessing and predicting the effects of climate change – calculations have often presented the rate of warming as a gradual rise from the mid 20th century to today. However, new research from UC Santa Barbara scientists Timothy DeVries and Aaron Bagnell could overturn that assumption, suggesting the ocean maintained a relatively steady temperature throughout most of the 20th century, before embarking on a steep rise. The newly discovered dynamics may have significant implications for what we might expect in the future. "There wasn't an onset of an imbalance until about 1990, which is later than most estimates," said DeVries, an associate professor in the Department of Geography, and a co-author on a paper that appears in the journal Nature Communications. According to the study, the period from 1950 to1990 saw temperature fluctuations in the water column but no net warming.

To do that, the nonprofit is implementing new technology like better video review platforms, better cameras on boats, and increased artificial intelligence, which CEO Mark Hager said is the most exciting. New England Marine Monitoring, in partnership with the Gulf of Maine Research Institute and Vesper, is developing artificial intelligence for fishermen. The goal is to make commercial fishing both economically and ecologically better. Typically, there are human observers on a boat to be sure the fishermen are following federal guidelines, but this technology could change that. "The idea is to ultimately shift from having at-sea human observers," Blaine Grimes of the Gulf of Maine Research Institute said.

Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

Human-wildlife conflict—defined here as direct interactions between humans and wildlife with adverse outcomes—costs the global economy billions of dollars annually, threatens human lives and livelihoods, and is a leading cause of biodiversity loss ([ 1 ][1]). These clashes largely stem from the co-occurrence of humans and wildlife seeking limited resources in shared landscapes and often has unforeseen consequences. For example, large carnivore species like leopards may prey upon livestock and disrupt human livelihoods, leading to retaliatory killings that can drive wildlife decline, zoonotic disease outbreaks, and child labor practices ([ 2 ][2]). As dire as these conflicts have been, climate change is intensifying human-wildlife conflict by exacerbating resource scarcity and forcing people and wildlife to share increasingly crowded spaces. Consequently, human-wildlife conflict is rising in frequency and severity, but the complex connections among climate dynamics, ecological dynamics, and social dynamics contributing to the heightened conflict have yet to be fully appreciated. ![Figure][3] Warming temperatures have driven animals to human-dominated areas in search of food. Increased attacks on livestock can spur retaliatory killing of predators. A sheep corral in the Himalayas is covered with wire to protect against attacks from snow leopards. PHOTO: NICK GARBUTT/MINDEN PICTURES Both extreme climate events and directional climate change have the potential to alter the dynamics of human-wildlife conflict. Acute climate events can cause rapid changes in resource availability that drive strong behavioral and spatial responses in animals and people, leading to increased co-occurrence and competition. In terrestrial systems, droughts in particular have intensified some of the most visible conflicts. For example, from 1986 to 1988, a severe drought in India brought about by an extreme El Niño led to a sharp decline in vegetation productivity; loss of food drove elephants to new human-dominated areas, which led to rapid increases in crop damage and fatal attacks on people ([ 3 ][4]). The same drought event in India saw a marked increase in livestock losses to lions, and human fatalities from lion attacks rose by more than 600% in one region to 6.7 deaths per year following the drought ([ 3 ][4]). More recently in 2018, a prolonged drought in Botswana saw some of the highest incidences of livestock depredations by large carnivores on record, compounding drought-induced food and economic insecurity in agricultural and pastoral communities ([ 4 ][5]). Similar connections between climate events and conflicts are occurring in marine systems. For instance, anomalously warm water temperatures off the South African coast drove changes in prey availability that displaced great white sharks into areas of high human use; the increase in spatial overlap between people and sharks led to a nearly fourfold increase in shark attacks within a single year ([ 5 ][6]). A similar increase in spatial overlap that resulted in heightened conflict occurred in 2014 to 2016 off the US West Coast, when an intense marine heat wave drove changes in both large-whale distributions and fisheries management, leading to an unprecedented number of whale entanglements in fishing gear ([ 6 ][7]). Not only did these entanglements cause high rates of whale mortality, but subsequent management restrictions have threatened millions of dollars in lost fishery revenue. Although extreme climate events often create dramatic conflicts, long-term warming is also producing conflicts with interconnected consequences for people and wildlife. In a notable example, over a 30-year period in Canada's Hudson Bay, human–polar bear conflicts involving property damage, life-threatening encounters, or bear killings have more than tripled as sea ice has declined and polar bears have spent more time on land ([ 7 ][8]). In the Himalayas, warming-induced vegetation changes at high elevations have driven the bharal or blue sheep to lower elevations, where they forage on crops, which affects the livelihoods of local subsistence agricultural producers. Simultaneously, the redistribution of bharal has also drawn their primary predator, snow leopards, to lower elevations, leading to increased livestock depredation and retaliatory killing of leopards ([ 8 ][9]). In other examples, crop foraging ([ 9 ][10]), livestock depredation ([ 10 ][11]) or competition ([ 11 ][12]), and human-wildlife encounters ([ 12 ][13]) are inversely correlated with interannual rainfall as a result of reduced food and water availability, and declining rainfall trends in parts of the globe continue to create more frequent and intense conflicts ([ 13 ][14]). Even as climate change restricts resource availability in many contexts, climate-driven expansion of the human footprint further forces people and animals to share spaces and can create new conflicts—for example, agricultural expansion into previously unproductive or inaccessible areas is significantly associated with rises in human-wildlife conflict ([ 9 ][10]). By investigating the interrelated consequences of climate change on wildlife and human populations, we can better anticipate undesired outcomes and identify how human interventions can mitigate cascading ecological and social dynamics. Climate impacts on human-wildlife conflict do not act in isolation—among other factors, socioeconomic drivers such as land-use change and demographic processes such as rising human populations or changes in predator and prey populations play major roles in determining the frequency, scale, and distribution of conflicts ([ 1 ][1]). Thus, illuminating and ultimately addressing the interconnections between climate change and human-wildlife conflict requires a coupled socioecological systems approach, drawing from fields as diverse as ecology, global change biology, human demography, political science, public policy, history, and economics. Although the impact of climate change on human-wildlife conflict has arguably received relatively little research attention, governmental bodies are increasingly recognizing this phenomenon and developing forward-looking policies to explicitly incorporate climate into the management of certain conflicts ([ 3 ][4], [ 4 ][5]). For example, the state of California in the US recently implemented a Risk Assessment and Mitigation Program that assimilates climatic, oceanographic, biological, and economic indices to inform dynamic fisheries management to reduce the risk of whale entanglements ([ 6 ][7]). Knowledge of climate impacts on human-wildlife conflict can also aid long-term planning efforts and public outreach. For instance, livestock compensation programs, one of the most widely implemented tools to mitigate human-carnivore conflict, could plan funding allocations to anticipate higher spending in years with anomalous climate conditions. Furthermore, given early warning from climate predictions or emerging efforts to predict human-wildlife conflicts using artificial intelligence ([ 14 ][15]), governments or nongovernmental organizations can educate and warn the public about possible increased interactions with wildlife ([ 12 ][13]). As climate change continues to drive both increased climate variability and directional change ([ 15 ][16]), climate-driven human-wildlife conflict can be expected to be a recurring challenge. To protect wildlife and humans alike, it is vital that a diverse body of research and institutions considers the role of a changing climate in shaping the complex socioecological dynamics of conflict. 1. [↵][17]1. P. J. Nyhus , Annu. Rev. Environ. Resour. 41, 143 (2016). [OpenUrl][18] 2. [↵][19]1. J. Terborgh, 2. J. A. Estes 1. J. S. Brashares, 2. L. R. Prugh, 3. C. J. Stoner, 4. C. W. Epps , in Trophic Cascades, J. Terborgh, J. A. Estes, Eds. (Island Press, 2010), pp. 221–240. 3. [↵][20]1. J. R. Bhatt, 2. A. Das, 3. K. Shanker , Eds., Biodiversity and Climate Change: An Indian Perspective (Ministry of Environment, Forest and Climate Change, Government of India, New Delhi, 2018), pp. 1–138. 4. [↵][21]Botswana Vulnerability Assessment Committee, (Botswana Ministry of Local Government and Rural Development, 2019); . 5. [↵][22]1. B. K. Chapman, 2. D. McPhee , Ocean Coast. Manage. 133, 72 (2016). [OpenUrl][23] 6. [↵][24]1. J. A. Santora et al ., Nat. Commun. 11, 536 (2020). [OpenUrl][25] 7. [↵][26]1. L. Towns et al ., Polar Biol. 32, 1529 (2009). [OpenUrl][27][CrossRef][28] 8. [↵][29]1. A. Aryal et al ., Theor. Appl. Climatol. 115, 517 (2013). [OpenUrl][30] 9. [↵][31]1. J. M. Mukeka, 2. J. O. Ogutu, 3. E. Kanga, 4. E. Røskaft , Glob. Ecol. Conserv. 18, e00620 (2019). [OpenUrl][32] 10. [↵][33]1. M. Schiess-Meier, 2. S. Ramsauer, 3. T. Gabanapelo, 4. B. Konig , J. Wildl. Manage. 71, 1267 (2007). [OpenUrl][34] 11. [↵][35]1. S. P. Vargas et al ., Oryx 55, 275 (2021). [OpenUrl][36] 12. [↵][37]1. C. S. Zack et al ., Wildl. Soc. Bull. 31, 517 (2003). [OpenUrl][38] 13. [↵][39]1. J. M. Mukeka et al ., Hum. Wildl. Interact. 14, 255 (2020). [OpenUrl][40] 14. [↵][41]1. P. Variyar , Can Artificial Intelligence Predict Human-Wildlife Conflict? (Wildlife Conservation Trust, 2021); [www.wildlifeconservationtrust.org/can-artificial-intelligence-predict-human-wildlife-conflict/][42]. 15. [↵][43]1. D. Coumou, 2. S. Rahmstorf , Nat. Clim. Chang. 2, 491 (2012). [OpenUrl][44] Acknowledgments: I thank K. Gaynor, A. McInturff, E. Pikitch, and J. Samhouri for valuable discussions and comments. 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