In mid-November, Moscow flexed its military muscle by test-launching a missile that struck a Russian satellite, to show that it can fight wars in space. The blast created a large debris field in low-Earth orbit that is expected to pose a threat to satellites and space activities for years to come. Russia is not the only country to have conducted such an anti-satellite missile test. The U.S., China and India have carried out similar tests in the past, and some countries are developing other means to disrupt an adversary's satellite operations, including by jamming their signals or using "killer satellites," the latter of which approach a target satellite and utilize a robotic arm or similar method to capture it and disable its functions. As the new space race escalates, protecting space assets has become vital to a nation's security and economy.

MAKKAH: Artificial intelligence is an increasingly important part of modern life and the technologies we rely on, but it is also being used to enhance awareness and understanding of more traditional and long-established aspects of our lives and cultures. For example, AI is serving the Arabic language through its use in applications that support teaching and learning and assist in the development of dictionaries and programs to help millions of people worldwide to learn and use the language. Abdullah Al-Washmi, the secretary-general of the King Salman Global Academy for Arabic Language, told Arab News that the academy aims to become a global reference resource for the language by providing the tools required to promote, teach, learn and encourage the use of Arabic, in accordance with the objectives of the Kingdom's Vision 2030 and its Human Capability Development Program. Part of the academy's strategy involves a focus on accelerating the pace of technical research in various fields relating to the Arabic language, using AI tools to aid understanding, he added. AI has become a major enabler of the teaching and learning of languages, Al-Washmi said, by helping to provide multiple learning patterns that allow students to learn Arabic in ways tailored to their needs.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN's optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

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.

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

With new technologies in biomedicine, we are able to generate and collect data of various modalities, including genomics, epigenomics, transcriptomics, and proteomics (Figure 1A). Integrating heterogeneous features on a single set of observations provides a unique opportunity to gain a comprehensive understanding of an outcome of interest. It offers the potential for making discoveries that are hidden in data analyses of a single modality and achieving more accurate predictions of the outcome (Kristensen et al. 2014, Ritchie et al. 2015, Gligorijević et al. 2016, Karczewski & Snyder 2018, Ma et al. 2020). While "multi-view data analysis" can mean different things, we use it here in the context of supervised learning, where the goal is to fuse different data views to model an outcome of interest. To give a concrete example, assume that a researcher wants to predict cancer outcomes from RNA expression and DNA methylation measurements for a set of patients. The researcher suspects that: (1) both data views could potentially have prognostic value; (2) the two views share some underlying relationship with each other, as DNA methylation regulates gene expression and can repress the expression of tumor suppressor genes or promote the expression of oncogenes. Should the researcher use both data views for downstream prediction, or just use one view or the other?

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.

The rocket ship trajectory of a startup is well known: Get an idea, build a team and slap together a minimum viable product (MVP) that you can get in front of users. However, today's startups need to reconsider the MVP model as artificial intelligence (AI) and machine learning (ML) become ubiquitous in tech products and the market grows increasingly conscious of the ethical implications of AI augmenting or replacing humans in the decision-making process. An MVP allows you to collect critical feedback from your target market that then informs the minimum development required to launch a product -- creating a powerful feedback loop that drives today's customer-led business. This lean, agile model has been extremely successful over the past two decades -- launching thousands of successful startups, some of which have grown into billion-dollar companies. From facial recognition technology that has a bias against people of color to credit-lending algorithms that discriminate against women, the past several years have seen multiple AI- or ML-powered products killed off because of ethical dilemmas that crop up downstream after millions of dollars have been funneled into their development and marketing.

Seyfarth Synopsis: A new law in New York City restricts the use of artificial intelligence ("AI") to screen candidates for employment or promotion, and will take effect on January 1, 2023. Although employers have a year to prepare, those that intend to rely on AI during the hiring process after January 2023 must submit their AI tools to a "bias audit" now. Companies that use AI as a component of their hiring process should be aware of a new law in New York City that goes into effect in early January 2023. AI tools can increase the speed and efficiency of candidate reviews. However, New York City's legislature, animated by concerns that bias may be embedded within AI tools, has regulated their use.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A base hosting U.S. troops at Baghdad's international airport came under rocket fire on Wednesday while indirect fire hit another base hosting U.S. troops in Syria, part of a string of attacks in the days after the anniversary of the death of top Iranian general Qassim Soleimani. Eight rounds of indirect fire hit the base in Syria run by the U.S.-backed Syrian Democratic Forces, causing minor damage inside the base but no casualties. No damage or casualties were reported from the Iraq attack, military officials said.