Cloud Robotics is helping to create a new generation of robots that leverage the nearly unlimited resources of large data centers (i.e., the cloud), overcoming the limitations imposed by on-board resources. Different processing power, capabilities, resource sizes, energy consumption, and so forth, make scheduling and task allocation critical components. The basic idea of task allocation and scheduling is to optimize performance by minimizing completion time, energy consumption, delays between two consecutive tasks, along with others, and maximizing resource utilization, number of completed tasks in a given time interval, and suchlike. In the past, several works have addressed various aspects of task allocation and scheduling. In this paper, we provide a comprehensive overview of task allocation and scheduling strategies and related metrics suitable for robotic network cloud systems. We discuss the issues related to allocation and scheduling methods and the limitations that need to be overcome. The literature review is organized according to three different viewpoints: Architectures and Applications, Methods and Parameters. In addition, the limitations of each method are highlighted for future research.

Art is an expression of oneself and is often used to convey a message, tell a story or evoke emotions. The advancement of technology has given artists more tools to enhance their creations, and one such tool is Stable Diffusion Animation. This innovative technology is a game-changer, allowing artists to bring their static artwork to life with fluid, mesmerizing animations that add an extra dimension to their work. In this article, we will discuss what Stable Diffusion Animation is, how it works, and the benefits of incorporating it into your artwork. Stable Diffusion Animation is a technique that enables artists to create dynamic animations from static images.

The gaming industry has come a long way from its early days of basic graphics and limited gameplay options. Today, games feature lifelike graphics and captivating narratives, thanks in part to the incorporation of ML and AI in game development. These cutting-edge technologies are revolutionizing the design, development, and play of games, leading to a more personalized and entertaining experience. The popularity of podcasts where gamers discuss the future of AI in gaming shows that players are becoming increasingly interested in AI and ML-based games. The focus of this article is on the developments of ML and AI in Game Development, not AI designed to play games at a superhuman level. Like in other industries, these technologies are also restructuring the gaming landscape, which was already an enormous industry. Machine learning and AI in game development can benefit the industry even more in numerous ways.

Images generated using Adobe Firefly. Today marks the beginning of an important new chapter for our creative products with the introduction of Adobe Firefly, a family of generative AI models for creative expression. Firefly will mix the power of our applications with the promise of generative AI in ways that empower you to express your creative ideas with greater efficiency and without constraints. We're entering a world where you'll be able to bring your creative vision to life simply by describing what you want in your own words, or with a simple gesture in your app. A world where you can modify a video or cut an audio track simply by describing a desired mood.

Technology has taken another turn with the dawn of artificial Intelligence which has quickly become the topic of many companies today. Businesses are constantly searching for ways to apply this piece of technology to increase their efficiency and effectiveness. Leadzen.ai is an innovative and dynamic software company that specializes in lead generation and customer engagement solutions. The company is dedicated to helping businesses and industries to maximize their potential by providing cutting-edge technology that simplifies and streamlines the lead generation process. At Leadzen.ai they understand that the key to success in any business is having a steady stream of qualified leads. That's why they have developed a state-of-the-art lead generation platform that utilizes artificial intelligence and machine learning to generate high-quality leads quickly and efficiently.

Although deep learning has made great progress in recent years, the exploding economic and environmental costs of training neural networks are becoming unsustainable. To address this problem, there has been a great deal of research on *algorithmically-efficient deep learning*, which seeks to reduce training costs not at the hardware or implementation level, but through changes in the semantics of the training program. In this paper, we present a structured and comprehensive overview of the research in this field. First, we formalize the *algorithmic speedup* problem, then we use fundamental building blocks of algorithmically efficient training to develop a taxonomy. Our taxonomy highlights commonalities of seemingly disparate methods and reveals current research gaps. Next, we present evaluation best practices to enable comprehensive, fair, and reliable comparisons of speedup techniques. To further aid research and applications, we discuss common bottlenecks in the training pipeline (illustrated via experiments) and offer taxonomic mitigation strategies for them. Finally, we highlight some unsolved research challenges and present promising future directions.

Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.

We consider the approximation of a class of dynamic partial differential equations (PDE) of second order in time by the physics-informed neural network (PINN) approach, and provide an error analysis of PINN for the wave equation, the Sine-Gordon equation and the linear elastodynamic equation. Our analyses show that, with feed-forward neural networks having two hidden layers and the $\tanh$ activation function, the PINN approximation errors for the solution field, its time derivative and its gradient field can be effectively bounded by the training loss and the number of training data points (quadrature points). Our analyses further suggest new forms for the training loss function, which contain certain residuals that are crucial to the error estimate but would be absent from the canonical PINN loss formulation. Adopting these new forms for the loss function leads to a variant PINN algorithm. We present ample numerical experiments with the new PINN algorithm for the wave equation, the Sine-Gordon equation and the linear elastodynamic equation, which show that the method can capture the solution well.

As Machine Learning (ML) gains adoption across industries and new use cases, practitioners increasingly realize the challenges around effectively developing and iterating on ML systems: reproducibility, debugging, scalability, and documentation are elusive goals for real-world pipelines outside tech-first companies. In this paper, we review the nature of ML-oriented workloads and argue that re-purposing existing tools won't solve the current productivity issues, as ML peculiarities warrant specialized development tooling. We then introduce Metaflow, an open-source framework for ML projects explicitly designed to boost the productivity of data practitioners by abstracting away the execution of ML code from the definition of the business logic. We show how our design addresses the main challenges in ML operations (MLOps), and document through examples, interviews and use cases its practical impact on the field.

We present an AI-based ecosystem simulator that uses three-dimensional models of the terrain and animal models controlled by deep reinforcement learning. The simulations take place in a game engine environment, which enables continuous visual observation of the ecosystem model. The terrain models are generated from geographic data with altitudes and land cover type. The animal models combine three-dimensional conformation models with animation schemes and decision-making mechanisms trained with deep reinforcement learning in increasingly complex environments (curriculum learning). We show how AI tools of this kind can be used for modeling the development of specific ecosystems with and without different forms of economic activities. In particular, we show how they might be used for modeling local biodiversity effects of land cover change, exploitation of natural resources, pollution, invasive species, and climate change.