During the Coronavirus 2019 (the covid-19) pandemic, schools continuously strive to provide consistent education to their students. Teachers and education policymakers are seeking ways to re-open schools, as it is necessary for community and economic development. However, in light of the pandemic, schools require customized schedules that can address the health concerns and safety of the students considering classroom sizes, air conditioning equipment, classroom systems, e.g., self-contained or compartmentalized. To solve this issue, we developed the School-Virus-Infection-Simulator (SVIS) for teachers and education policymakers. SVIS simulates the spread of infection at a school considering the students' lesson schedules, classroom volume, air circulation rates in classrooms, and infectability of the students. Thus, teachers and education policymakers can simulate how their school schedules can impact current health concerns. We then demonstrate the impact of several school schedules in self-contained and departmentalized classrooms and evaluate them in terms of the maximum number of students infected simultaneously and the percentage of face-to-face lessons. The results show that increasing classroom ventilation rate is effective, however, the impact is not stable compared to customizing school schedules, in addition, school schedules can differently impact the maximum number of students infected depending on whether classrooms are self-contained or compartmentalized. It was found that one of school schedules had a higher maximum number of students infected, compared to schedules with a higher percentage of face-to-face lessons. SVIS and the simulation results can help teachers and education policymakers plan school schedules appropriately in order to reduce the maximum number of students infected, while also maintaining a certain percentage of face-to-face lessons.

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research efforts have explored a higher level of optimization by making the quantum circuits themselves resilient to noise. We propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing QML and quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search and parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates. Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines. For QML, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real QC. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD. We also open-source TorchQuantum (https://github.com/mit-han-lab/torchquantum) for fast training of parameterized quantum circuits to facilitate future research.

Among the goals of information operations are to change the overall information environment vis-\'a-vis specific actors. For example, "trolling campaigns" seek to undermine the credibility of specific public figures, leading others to distrust them and intimidating these figures into silence. To accomplish these aims, information operations frequently make use of "trolls" -- malicious online actors who target verbal abuse at these figures. In Brazil, in particular, allies of Brazil's current president have been accused of operating a "hate cabinet" -- a trolling operation that targets journalists who have alleged corruption by this politician and other members of his regime. Leading approaches to detecting harmful speech, such as Google's Perspective API, seek to identify specific messages with harmful content. While this approach is helpful in identifying content to downrank, flag, or remove, it is known to be brittle, and may miss attempts to introduce more subtle biases into the discourse. Here, we aim to develop a measure that might be used to assess how targeted information operations seek to change the overall valence, or appraisal, of specific actors. Preliminary results suggest known campaigns target female journalists more so than male journalists, and that these campaigns may leave detectable traces in overall Twitter discourse.

Acquiring information on large areas on the earth's surface through satellite cameras allows us to see much more than we can see while standing on the ground. This assists us in detecting and monitoring the physical characteristics of an area like land-use patterns, atmospheric conditions, forest cover, and many unlisted aspects. The obtained images not only keep track of continuous natural phenomena but are also crucial in tackling the global challenge of severe deforestation. Among which Amazon basin accounts for the largest share every year. Proper data analysis would help limit detrimental effects on the ecosystem and biodiversity with a sustainable healthy atmosphere. This report aims to label the satellite image chips of the Amazon rainforest with atmospheric and various classes of land cover or land use through different machine learning and superior deep learning models. Evaluation is done based on the F2 metric, while for loss function, we have both sigmoid cross-entropy as well as softmax cross-entropy. Images are fed indirectly to the machine learning classifiers after only features are extracted using pre-trained ImageNet architectures. Whereas for deep learning models, ensembles of fine-tuned ImageNet pre-trained models are used via transfer learning. Our best score was achieved so far with the F2 metric is 0.927.

According to a new survey, six out of ten expect that products and services using artificial intelligence will profoundly change their daily life in the next three to five years and half feel that this has already happened. These are some of the findings of a 28-country survey conducted by Ipsos for the World Economic Forum of 19,504 adults under the age of 75 between November 19 and December 3, 2021. "In order to trust artificial intelligence, people must know and understand exactly what AI is, what it's doing, and its impact," said Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning at the World Economic Forum. "Leaders and companies must make transparent and trustworthy AI a priority as they implement this technology. At the World Economic Forum, we are focused on multi-stakeholder collaboration to optimize accountability, transparency, privacy and impartiality to create that trust. With the ability to solve many of society's pressing issues, we are focused on accelerating the benefits and mitigating the risks of artificial intelligence and machine learning. Only then can we gain public trust and benefit from the rewards of emerging tech like AI."

In 1970, Marvin Minsky, recipient of the Turing Award ("the Nobel Prize of Computing"), predicted that within "three to eight years we will have a machine with the general intelligence of an average human being." The fundamental roadblock is that, although computer algorithms are really, really good at identifying statistical patterns, they have no way of knowing what these patterns mean because they are confined to MathWorld and never experience the real world. It's a brown-throated thrush, but in Germany it's called a halsenflugel, and in Chinese they call it a chung ling and even if you know all those names for it, you still know nothing about the bird–you only know something about people; what they call that bird. Now that thrush sings, and teaches its young to fly, and flies so many miles away during the summer across the country, and nobody knows how it finds its way," and so forth. There is a difference between the name of the thing and what goes on.

The expedition to find the wreck of Sir Ernest Shackleton's Endurance is set to sail next month, it was announced today on the centenary of the polar explorer's death. Endurance was one of two ships used by the Imperial Trans-Antarctic expedition of 1914–1917, which hoped to make the first land crossing of the Antarctic. Carrying an expedition crew of 28 men, the 144-foot-long Endurance was a three-masted schooner barque sturdily built for operations in polar waters. Aiming to land at Vahsel Bay, the vessel became stuck in pack ice on the Weddell Sea on January 18, 1915 -- where she and her crew would remain for many months. In late October, however, a drop in temperature from 42 F to -14 F saw the ice pack begin to steadily crush the Endurance, which finally sank on November 21, 1915.

Men who only briefly catch a glimpse of a woman are much more likely to overestimate how attractive she is than a woman glimpsing a man, a study reveals. Researchers, led by Murdoch University, in Perth Australia, worked with nearly 400 volunteers, asking them to rate the attractiveness of people of the opposite-sex from a blurry image, and then from a clear image. The results showed that on average men overestimate women's attractiveness, whereas on average women underestimate men's attractiveness. If you've been having trouble finding love on dating apps, you might want to try dating one of your friends. A study looked at data from just under 2,000 couples of different demographics in Canada.

The Association for the Advancement of Artificial Intelligence's 2021 International Conference on Artificial Intelligence and Interactive Digital Entertainment was held October 11-15, 2021. There were three workshops in the program: Experimental AI in Games, Programming Languages in Entertainment, and Strategy Games. This report contains summaries of some, but not all symposia. The 2021 Experimental AI in Games Workshop helped to encourage experimentation and discovery in game AI research and game development. This year saw fourteen presentations exploring established subjects such as level and narrative generation, to theoretical and practitioner work.

Communication is defined as the act of sharing or exchanging information, ideas or feelings. To establish communication between two people, both of them are required to have knowledge and understanding of a common language. But in the case of deaf and dumb people, the means of communication are different. Deaf is the inability to hear and dumb is the inability to speak. They communicate using sign language among themselves and with normal people but normal people do not take seriously the importance of sign language. Not everyone possesses the knowledge and understanding of sign language which makes communication difficult between a normal person and a deaf and dumb person. To overcome this barrier, one can build a model based on machine learning. A model can be trained to recognize different gestures of sign language and translate them into English. This will help a lot of people in communicating and conversing with deaf and dumb people. The existing Indian Sing Language Recognition systems are designed using machine learning algorithms with single and double-handed gestures but they are not real-time. In this paper, we propose a method to create an Indian Sign Language dataset using a webcam and then using transfer learning, train a TensorFlow model to create a real-time Sign Language Recognition system. The system achieves a good level of accuracy even with a limited size dataset.