A simple device, just a heat and movement sensor attached to digital camera, has revolutionised the way that conservationists learn about animals in the wild. Camera traps are a very simple solution to the task of working out when, where and how wildlife interacts with its environment. Monitoring populations without damaging habitats, these relatively simple devices have provided some astonishing finds including revealing species previously hidden in the untouched depths of the forest. Elusive new creatures aren't their only speciality, however, as in 2015, similar devices helped reveal that the critically endangered Javan rhinoceros was breeding and significantly adding to its tiny population. After identifying a likely area for a sighting, usually with the help of local guides, traps are placed at animal height on trees and posts and left to wait until wildlife walks by.

In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.

As the transformative potential of AI has become increasingly salient as a matter of public and political interest, there has been growing discussion about the need to ensure that AI broadly benefits humanity. This in turn has spurred debate on the social responsibilities of large technology companies to serve the interests of society at large. In response, ethical principles and codes of conduct have been proposed to meet the escalating demand for this responsibility to be taken seriously. As yet, however, few institutional innovations have been suggested to translate this responsibility into legal commitments which apply to companies positioned to reap large financial gains from the development and use of AI. This paper offers one potentially attractive tool for addressing such issues: the Windfall Clause, which is an ex ante commitment by AI firms to donate a significant amount of any eventual extremely large profits. By this we mean an early commitment that profits that a firm could not earn without achieving fundamental, economically transformative breakthroughs in AI capabilities will be donated to benefit humanity broadly, with particular attention towards mitigating any downsides from deployment of windfall-generating AI.

A stunning panorama of Mars shows the final resting place of NASA's Opportunity. The image is a series of 354 individual pictures snapped by the rover over a 29-day period before it shutdown completely and declared'dead' by the American space agency earlier this year. The desolate Martian landscape known as Perseverance Valley was the last thing the rover saw and now serves as its graveyard. The panorama is composed of 354 individual images provided by the rover's Panoramic Camera (Pancam) from May 13 through June 10, or sols (Martian days) 5,084 through 5,111. The panorama combines images from three different Pancam filters, which admit light centered on wavelengths of 753 nanometers (near-infrared), 535 nanometers (green) and 432 nanometers (violet). A stunning panorama of Mars shows the final resting place of NASA's Opportunity.

KUALA LUMPUR – Disney has cut a lesbian kiss from the latest "Star Wars" movie, Singapore's media regulator said on Tuesday, so that more children can watch it. The two minor female characters embrace but do not kiss in the version of "The Rise of Skywalker" shown in Singapore, local media said, as the ninth film in the celebrated science-fiction series rakes in millions from loyal fans. "The applicant has omitted a brief scene which under the Film Classification Guidelines would require a higher rating," a spokeswoman from Singapore's Infocomm Media Development Authority said. Disney, which owns the "Star Wars" production company Lucasfilm, did not respond to a request for comment on its decision to cut the scene from the last installment of the second highest-grossing movie franchise of all time. It concludes a story that began in 1977, when filmmaker George Lucas introduced a young hero named Luke Skywalker and delighted audiences with a galaxy of robots, furry warriors known as Wookiees and a host of other eclectic characters.

These and many other fascinating insights are from an analysis of AI startups' funding rounds in 2019 using Crunchbase Pro research. AI startups who have had seed, early-stage venture or late-stage venture funding since December 31, 2018, and are U.S.-based are included in the analysis which is provided here. Crunchbase Pro found 499 startups meeting the search criteria as of today. Their AI strategies include improve every aspect of the customer's lifecycle from pricing through scheduling post-stay cleans. The company manages a growing portfolio of more than 14,000 vacation homes in the U.S, Europe, Central, and South America, and South Africa.

To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, computer intelligence algorithms take the role that operators have in Traditional Travel Surveys; since we train each algorithm on data, performances rest on the data quality, thus on the ground truth. Inaccurate validations affect negatively: labels, algorithms' training, travel diaries precision, and therefore data validation, within a very critical loop. Interestingly, boundaries are proven burdensome to push even for Machine Learning methods. To support optimal investment decisions for practitioners, we expose the drivers they should consider when assessing what they need against what they get. This paper highlights and examines the critical aspects of the underlying research and provides some recommendations: (i) from the device perspective, on the main physical limitations; (ii) from the application perspective, the methodological framework deployed for the automatic generation of travel diaries; (iii)from the ground truth perspective, the relationship between user interaction, methods, and data.

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph. To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models. Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy. Our DIAMNet can further improve existing representation learning models for this more global problem.

On Sharing Models Instead of Data using Mimic learning for Smart Health Applications Mohamed Baza, Andrew Salazar †, Mohamed Mahmoud, Mohamed Abdallah ‡, Kemal Akkaya ‡ Department of Computer Science, Tennessee Tech University, Cookeville, TN, USA ‡ Department of Information and Decision Sciences, California State San Bernardino, San Bernardino, CA, USA ‡ division of Information and Computing Technology, College of Science and Engineering, HBKU, Doha, Qatar § Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA Abstract --Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model.

Artificial Intelligence has spread rapidly across markets in recent years as traders constantly strive to gain the upper hand, while regulators have given a guarded welcome to the cutting-edge technology. High-frequency trading propelled by algorithms has reigned over the past decade, as banks and funds take advantage of small price fluctuations on many markets to carry out thousands of deals in a fraction of a second. Complex mathematical equations have long been used to conduct certain operations -- for example, selling or buying a security if it breaches a certain level. Yet algorithms have come under fierce criticism over "flash crashes", such as a dizzying slump in the British pound in October 2016 that was widely blamed on high-frequency deals. Artificial Intelligence now seeks to take trading into new realms, where "machine learning" (ML) software compares dozens of databases in the blink of an eye to monitor risk.