Network slicing achieves cost-efficient slice customization to support heterogeneous applications and services. Configuring cross-domain resources to end-to-end slices based on service-level agreements, however, is challenging, due to the complicated underlying correlations and the simulation-to-reality discrepancy between simulators and real networks. In this paper, we propose Atlas, an online network slicing system, which automates the service configuration of slices via safe and sample-efficient learn-to-configure approaches in three interrelated stages. First, we design a learning-based simulator to reduce the sim-to-real discrepancy, which is accomplished by a new parameter searching method based on Bayesian optimization. Second, we offline train the policy in the augmented simulator via a novel offline algorithm with a Bayesian neural network and parallel Thompson sampling. Third, we online learn the policy in real networks with a novel online algorithm with safe exploration and Gaussian process regression. We implement Atlas on an end-to-end network prototype based on OpenAirInterface RAN, OpenDayLight SDN transport, OpenAir-CN core network, and Docker-based edge server. Experimental results show that, compared to state-of-the-art solutions, Atlas achieves 63.9% and 85.7% regret reduction on resource usage and slice quality of experience during the online learning stage, respectively.

"Prediction" is a broad and generic term that describes any process for obtaining guesses of unseen variables based on any available information, as well as each of these guesses. On the other hand, "forecasting" is a more specific term that describes any process for issuing predictions for future variables based on information (which most commonly takes the form of time series) about the present and the past, with these particular predictions being broadly called "forecasts". Forecasting is a key theme and topic for this study. Therefore, in what follows, the general focus will be on it and not on prediction in general, although many of the statements and methods that will be referring to it are equally relevant and applicable to other prediction types. The origins of forecasting trace back to the early humans and their pronounced need for certainty in the practical endeavour of supporting their various everyday life decisions (Petropoulos et al. 2022). Thus, forecasting has met until today and still meets numerous implementations, formal and informal. Independently of their exact categorization and features, the formal implementations of forecasting rely, in principal, on concepts, theory and practice that originate from or can be attributed to the predictive branch of statistical modelling, although forecasting is also considered as an entire field on its own because of the major role that the temporal dependence plays in the formulation of its methods. The predictive branch of statistical modelling exhibits profound and fundamental differences with respect to the descriptive and explanatory ones, as it is thoroughly explained in Shmueli (2010).

Decentralized learning has been studied intensively in recent years motivated by its wide applications in the context of federated learning. The majority of previous research focuses on the offline setting in which the objective function is static. However, the offline setting becomes unrealistic in numerous machine learning applications that witness the change of massive data. In this paper, we propose \emph{decentralized online} algorithm for convex and continuous DR-submodular optimization, two classes of functions that are present in a variety of machine learning problems. Our algorithms achieve performance guarantees comparable to those in the centralized offline setting. Moreover, on average, each participant performs only a \emph{single} gradient computation per time step. Subsequently, we extend our algorithms to the bandit setting. Finally, we illustrate the competitive performance of our algorithms in real-world experiments.

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Chip Can Transmit All of the Internet's Traffic Every Second Matthew Sparkes New Scientist "A single computer chip has transmitted a record 1.84 petabits of data per second via a fiber-optic cable--enough bandwidth to download 230 million photographs in that time, and more traffic than travels through the entire internet's backbone network per second. It just goes to show that we can go so much further than we are today with internet connections,' said [Asbjørn Arvad Jørgensen]." Physicists Got a Quantum Computer to Work by Blasting It With the Fibonacci Sequence Isaac Schultz Gizmodo "In the recent research, pulsing a laser periodically at the 10 ytterbium qubits kept them in a quantum state--meaning entangled--for 1.5 seconds. But when the researchers pulsed the lasers in the pattern of the Fibonacci sequence, they found that the qubits on the edge of the system remained in a quantum state for about 5.5 seconds, the entire length of the experiment (the qubits could have remained in a quantum state for longer, but the team ended the experiment at the 5.5-second mark)." Technology That Lets Us'Speak' to Our Dead Relatives Has Arrived. Charlotte Jee MIT Technology Review "From what I could glean over a dozen conversations with my virtually deceased parents, this really will make it easier to keep close the people we love.

Machine learning (ML) projects can be complex, tedious, and time consuming. AWS and NVIDIA solve this challenge with fast, effective, and easy-to-use capabilities for your ML project. This course is designed for ML practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows. In this course, you will gain hands-on experience on building, training, and deploying scalable machine learning models with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs. Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML.

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Universities around the world are taking steps alongside major technology companies to explore ways to bolster ethics education in the artificial intelligence field in line with an initiative supported by the Vatican. The effort seeks to help those already working or aspiring to work in the tech fields understand that the development of artificial intelligence, or AI, should benefit humanity rather than pose uncontrollable challenges to human life. Participants at a global summit at the University of Notre Dame Oct. 25-26 explored ways to encompass ethics education in coursework with speakers calling for widespread integration in both technical and nontechnical curricula. Casey Fiesler, associate professor of information science at the University of Colorado, told in person and online attendees in a session that the long-held view that ethical topics are a "specialization" within technology education must be put aside. "We should not be teaching ethics in the context of computing so that it is completely separate from everything else that we are doing," Fiesler said in calling for a culture shift in higher education that can reach across society.

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