Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the presence of missing pixels is a very common but challenging issue due to errors in the acquisition process or manufacturer defects. RPCA and RTPCA are not able to recover the background and foreground simultaneously with missing pixels. The objective of this study is to address the problem of background/foreground separation with missing pixels by combining the video recovery, background/foreground separation into a single framework. To achieve this, a smooth robust tensor completion (SRTC) model is proposed to recover the data and decompose it into the static background and smooth foreground, respectively. An efficient algorithm based on tensor proximal alternating minimization (tenPAM) is implemented to solve the proposed model with global convergence guarantee under very mild conditions. Extensive experiments on real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation with missing pixels.

As an example of the efficiency that AI can introduce, let's consider production-line workers and engineers who manage reject rates in the pulley assembly process. This used to be a multi-step, manual process,

AI is not anymore a tool of the media industry where it just serves to solve simple use cases with simple AI algorithms. "For example, in the communications sector, connecting everyone, connecting everything, everywhere, at any time, on demand, is an enormously complex task, with equally complex infrastructure and technology," says Todd Ashton(T.A), Head of Ericsson South and East Africa. T.A: As a multinational networking and telecommunications company, Artificial Intelligence is a vital skill domain and technology for creating business value in terms of improved performance, higher efficiency, enhanced customer experience as well as creating new business models and use cases for 5G, IoT and enterprises across Africa. AI and automation will help address the complexity of 5G networks, drive efficiencies and improve customer experience as well as open new revenue streams for communications service providers (CSPs). However, smarter, AI fueled networks will accelerate Africa's digital agenda, and drive the progress and prospects of 5G in Africa.

Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems from various angles, in most computer vision related cases these approaches can be generalized to investigations of the effects of distribution shifts in image data. In this context, we propose to study the shifts in the learned weights of trained CNN models. Here we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a dataset with over 1.4 billion filters from hundreds of trained CNNs, using a wide range of datasets, architectures, and vision tasks. In a first use case of the proposed dataset, we can show highly relevant properties of many publicly available pre-trained models for practical applications: I) We analyze distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like visual category of the dataset, task, architecture, or layer depth. Based on these results, we conclude that model pre-training can succeed on arbitrary datasets if they meet size and variance conditions. II) We show that many pre-trained models contain degenerated filters which make them less robust and less suitable for fine-tuning on target applications. Data & Project website: https://github.com/paulgavrikov/cnn-filter-db

We study Langevin dynamics for recovering the planted signal in the spiked matrix model. We provide a "path-wise" characterization of the overlap between the output of the Langevin algorithm and the planted signal. This overlap is characterized in terms of a self-consistent system of integro-differential equations, usually referred to as the Crisanti-Horner-Sommers-Cugliandolo-Kurchan (CHSCK) equations in the spin glass literature. As a second contribution, we derive an explicit formula for the limiting overlap in terms of the signal-to-noise ratio and the injected noise in the diffusion. This uncovers a sharp phase transition -- in one regime, the limiting overlap is strictly positive, while in the other, the injected noise overcomes the signal, and the limiting overlap is zero.

Artificial Intelligence (AI) is reforming economies all across the world by proffering novel products and services which creates an avenue for the generation of greater productivity gains, improved efficiency and lower costs. This is a radical change from the usual practice and such that has the tendency to permeate every aspect of the economy of any given nation. Studies accentuate that Artificial Intelligence has a vital economic impact on developing economies in the world. Recent research conducted on 12 developed economies in the world, all of which together generate more than 0.5 % of the world's economic output, projected that by the year 2035, AI could double the annual global economic growth rates.1 This is because Artificial Intelligence has a massive impact on healthcare, communication, financial, legal and commercial services to mention but a few.

Worldwide Artificial intelligence (AI) software revenue is forecast to total $62.5 billion in 2022, an increase of 21.3% from 2021, according to a new forecast from Gartner, Inc. Many enterprises are boosting spending on AI as they seek better processes to develop applications. Today's leading AI companies are expanding their technological reach through other technology categories and operations, ranging from predictive analytics to business intelligence to data warehouse tools to deep learning, alleviating several industrial and personal pain points. "The AI software market is picking up speed, but its long-term trajectory will depend on enterprises advancing their AI maturity," said Alys Woodward, senior research director at Gartner. The AI software market encompasses applications with AI embedded in them, such as computer vision software, as well as software that is used to build AI systems.

The metaverse: while some of us are still coming to terms with the idea that we're likely to spend increasing amounts of time in a 3D version of the internet, companies are already scrambling to define the space, carve out their niche, and even snap up virtual real estate. The shift to the metaverse is likely to have a positive business impact, according to 71% of respondents to an Accenture survey, and 42% say it will be "breakthrough" or "transformational." The metaverse will infiltrate every sector in the coming years, culminating in a market opportunity worth more than $1 trillion in annual revenues, according to JP Morgan. Mark Zuckerberg's Meta says the metaverse will be "the biggest opportunity for modern business since the creation of the internet". He has outlined plans to spend more than $10 billion on developing virtual reality software and hardware.

Beneath the buzz, the metaverse is arriving in both predictable and unexpected ways. Some new experiences using headsets and mixed reality will be in your face – quite literally – but other implications will be harder to spot. As with all new categories, we'll see intended and unintended innovations and experiences, and the security stakes will be higher than we imagine at first. There is an inherent social engineering advantage with the novelty of any new technology. In the metaverse, fraud and phishing attacks targeting your identity could come from a familiar face – literally – like an avatar who impersonates your coworker, instead of a misleading domain name or email address.

Arising out of fusion problems that exist in a variety of fields in the natural and life sciences, the methods available to fuse multiple data sets have expanded dramatically in recent years. Older methods, rooted in psychometrics and chemometrics, also exist. Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems. Many of the included methods are illustrated by practical examples and are accompanied by a freely available R-package.