Small subgraphs (graphlets) are important features to describe fundamental units of a large network. The calculation of the subgraph frequency distributions has a wide application in multiple domains including biology and engineering. Unfortunately due to the inherent complexity of this task, most of the existing methods are computationally intensive and inefficient. In this work, we propose GNNS, a novel representational learning framework that utilizes graph neural networks to sample subgraphs efficiently for estimating their frequency distribution. Our framework includes an inference model and a generative model that learns hierarchical embeddings of nodes, subgraphs, and graph types. With the learned model and embeddings, subgraphs are sampled in a highly scalable and parallel way and the frequency distribution estimation is then performed based on these sampled subgraphs. Eventually, our methods achieve comparable accuracy and a significant speedup by three orders of magnitude compared to existing methods.

As fishermen have noticed this behaviour, they have used both natural and man-made floating objects, or drifting Fish Aggregating Devices (dFADs), as a tool for finding and catching tropical tunas. The use of dFADs in tuna purse-seine fisheries has gradually increased since the 1980s to the present time, where vessels using dFADs now contribute to 36% of the world's total tropical tuna catch (Davies et al., 2014; Wain et al., 2021; ISSF, 2021). These widespread changes have highlighted the need to better understand the potential ecological effects of dFADs on tuna ecology and the marine environment, in order to ensure adequate management of fish stocks and dFAD usage. Indeed, both the dynamics of how and why tuna associate to dFADs are still poorly understood. Regarding the reasons behind tuna aggregation to dFADs, a number of hypotheses have been suggested (Fréon and Dagorn, 2000; Dempster and Taquet, 2004; Castro et al., 2002). Of these, two have gained traction: the "meeting-point" hypothesis, which considers that dFADs facilitate the encounter between individuals or schools, thus constituting larger schools that could benefit survival rates (Castro et al., 2002); and the "indicator-log" hypothesis, by which tunas may be safeguarding the survival of their eggs, larvae and juvenile stages by using drifting objects as indicators of areas where plankton and food is readily available (Hall et al., 1992). This scenario has led some authors to postulate that man-made dFADs could have detrimental effects on tuna populations by creating a so-called "ecological trap" which would lead tuna to remain associated to dFADs even as these drift into areas that could negatively affect the tuna's behaviour and biology (Marsac et al., 2000; Hallier and Gaertner, 2008). To the best of our knowledge, there is yet no sufficient evidence to either confirm or reject this hypothesis (see Dagorn et al. (2012) and references therein). Given the concerns around the widespread use of dFADs in tuna fisheries today, it is not surprising that a considerable amount of research has been devoted to characterizing the dynamics at play when tunas aggregate to dFADs.

On the one hand, the dictionary is too large and the computational complexity In recent years, deep dictionary learning (DDL)has attracted a is too high for large-scale classification problems. On the great amount of attention due to its effectiveness for representation other hand, the original input training samples may contain learning and visual recognition. However, most existing noise, which leads to the inappropriate dictionary and suffers methods focus on unsupervised deep dictionary learning, failing from the problem of poor robustness. To address the abovementioned to further explore the category information. To make full issues, several supervised dictionary learning algorithms use of the category information of different samples, we propose such as D-KSVD [6] and LC-KSVD [7] have been a novel deep dictionary learning model with an intraclass proposed by introducing category information for dictionary constraint (DDLIC) for visual classification.

New research that harnessed the power of artificial intelligence has identified the specific crater on Mars that ejected the ancient Black Beauty meteorite. The researchers named the Mars crater after the Australian city of Karratha, which is home to one of the oldest terrestrial rocks. The discovery offers never-known details about the Martian meteorite NWA 7034, nicknamed'Black Beauty,' which was found in Africa in 2011, according to researchers. 'For the first time, we know the geological context of the only brecciated Martian sample available on Earth,' says Dr. Anthony Lagain. 'For the first time, we know the geological context of the only brecciated Martian sample available on Earth, 10 years before the NASA's Mars Sample Return mission is set to send back samples collected by the Perseverance rover currently exploring the Jezero crater,' lead author Dr. Anthony Lagain, from Curtin University's Space Science and Technology Center in the School of Earth and Planetary Sciences, says in a statement.

Autonomous flight is a grand challenge in aviation -- and a gold mine. The first company to crack it at scale stands to reap handsome profits from transportation and logistics alone. In 2020, the size of the global cargo airline industry was $110.8 billion, according to Statista, and one source estimates that it'll generate hundreds of billions in revenue by 2027. Xwing is one of the startups chasing after self-flying planes, as is Reliable Robotics, Pyka and the unicorn Volocopter. Roughly a year ago, Boston-based Merlin Labs emerged from stealth with an autonomous flight system designed to be installed in existing aircraft.

Climate change has been linked with a widespread decline in the ability of many of the world's forests to bounce back after events such as drought and logging. Forests around the world differ in their resilience to disturbances, but relatively little is know about how that resilience is changing over time. To tease out any shifts, Giovanni Forzieri at the University of Florence, Italy, and his colleagues ran a machine learning algorithm on satellite data of global vegetation from 2000 to 2020 to calculate a metric of resilience. Resilience was defined by a forest's ability to avoid shifting state, such as becoming savannah, and withstand perturbations, such as an influx of insect pests. The researchers found that more than half of forests in arid, tropical and temperate regions – where the majority of the world's trees are found – showed a significant decrease in resilience over the two decades.

The Australian state of New South Wales (NSW) has announced a new AI (artificial intelligence) technology that is set to automate and revolutionise the way the state's roads are maintained and repaired. The project announced on Tuesday would fund a 2.9-million-Australian dollar ($1.96-million) trial from AI company, Asset AI, which would install sensors on 32 public buses with routes across greater Sydney. The sensors use AI to combine visual data with local weather conditions to predict the rate of deterioration in the city's roads -- meaning it would in theory be able to alert maintenance teams before potholes or other road damages pose a risk to traffic. "There will always be cracks in the road and there will always be potholes but with smart tech like this we can predict deterioration, streamline maintenance and get to better outcomes much faster," said NSW Minister for Customer Service and Digital Government, Victor Dominello. At present, road damages and defects rely on reports from residents.

New Curtin-led research has pinpointed the exact home of the oldest and most famous Martian meteorite for the first time ever, offering critical geological clues about the earliest origins of Mars. Using a multidisciplinary approach involving a machine learning algorithm, the new research – published today in Nature Communications – identified the particular crater on Mars that ejected the so-called'Black Beauty' meteorite, weighing 320 grams, and paired stones, which were first reported as being found in northern Africa in 2011. The researchers have named the specific Mars crater after the Pilbara city of Karratha, located more than 1500km north of Perth in Western Australia, which is home to one of the oldest terrestrial rocks. Lead author Dr Anthony Lagain, from Curtin's Space Science and Technology Centre in the School of Earth and Planetary Sciences, said the exciting discovery offered never-before-known details about the Martian meteorite NWA 7034, known as'Black Beauty', which is widely studied across the globe. Beauty is the only brecciated Martian sample available on Earth, meaning it contains angular fragments of multiple rock types cemented together which is different from all other Martian meteorites that contain single rock types.

Unpaired image-to-image translation aims to find a mapping between the source domain and the target domain. To alleviate the problem of the lack of supervised labels for the source images, cycle-consistency based methods have been proposed for image structure preservation by assuming a reversible relationship between unpaired images. However, this assumption only uses limited correspondence between image pairs. Recently, contrastive learning (CL) has been used to further investigate the image correspondence in unpaired image translation by using patch-based positive/negative learning. Patch-based contrastive routines obtain the positives by self-similarity computation and recognize the rest patches as negatives. This flexible learning paradigm obtains auxiliary contextualized information at a low cost. As the negatives own an impressive sample number, with curiosity, we make an investigation based on a question: are all negatives necessary for feature contrastive learning? Unlike previous CL approaches that use negatives as much as possible, in this paper, we study the negatives from an information-theoretic perspective and introduce a new negative Pruning technology for Unpaired image-to-image Translation (PUT) by sparsifying and ranking the patches. The proposed algorithm is efficient, flexible and enables the model to learn essential information between corresponding patches stably. By putting quality over quantity, only a few negative patches are required to achieve better results. Lastly, we validate the superiority, stability, and versatility of our model through comparative experiments.

The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and are used to issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for a variety of applications in industries such as medical, environmental monitoring, home automation etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and also the convert dataset into Resource Description Framework (RDF). This research describes the various steps involved in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation, supports reasoning and the general semantics of the open-world followed by all the ontologies