We consider the problem of pilot-aided, uplink channel estimation in a distributed massive multiple-input multiple-output (MIMO) architecture, in which the access points are connected to a central processing unit via fiber-optical fronthaul links, carrying a two-level-quantized version of the received analog radio-frequency signal. We adapt to this architecture the deep-learning-based channel-estimation algorithm recently proposed by Nguyen et al. (2023), and explore its robustness to the additional signal distortions (beyond 1-bit quantization) introduced in the considered architecture by the automatic gain controllers (AGCs) and by the comparators. These components are used at the access points to generate the two-level analog waveform from the received signal. Via simulation results, we illustrate that the proposed channel-estimation method outperforms significantly the Bussgang linear minimum mean-square error channel estimator, and it is robust against the additional impairments introduced by the AGCs and the comparators.

Connected devices are part of everyday life. The proliferation of connected portable devices such as mobile phones, laptop, smart watches, tablets, or non-portable connected devices such as TV, video game console saturates the environment with RF signals. In parallel to the reception of desired data from its communication partner(s), such connected devices receive also unwanted signals, so called interferers. The interferers, especially from Wi-Fi signals, can occur in a random manner in the form of a signal burst of variable duration and have a signal strength possibly much higher than the desired signal. Interferers with a high signal strength can cause saturation of the receiver preventing proper reception of the desired data. Some techniques tackle this issue by continuously monitoring the received signal strength and adjust immediately the receiver gain to avoid saturation whilst still maintaining the highest sensitivity level. However, when operating popular wireless communication protocols such as Wireless PAN (Bluetooth, BLE, Zigbee...), the receiver is not allowed to adjust the gain during the data payload. RF receivers for these communication protocols adjust then the gain during a time interval prior to the payload reception based on the real-time received signal and freeze the gain just before switching to the payload reception period. This is illustrated in figure 1. Due to the random nature in occurrence and strength level, interferers may appear during the data payload, receiver may saturate causing data loss.

This paper proposes an adaptive gravity compensation (AGC) control strategy for a cable-driven upper-limb exosuit intended to assist the wearer with lifting tasks. Unlike most model-based control techniques used for this human-robot interaction task, the proposed control design does not assume knowledge of the anthropometric parameters of the wearer's arm and the payload. Instead, the uncertainties in human arm parameters, such as mass, length, and payload, are estimated online using an indirect adaptive control law that compensates for the gravity moment about the elbow joint. Additionally, the AGC controller is agnostic to the desired joint trajectory followed by the human arm. For the purpose of controller design, the human arm is modeled using a 1-DOF manipulator model. Further, a cable-driven actuator model is proposed that maps the assistive elbow torque to the actuator torque. The performance of the proposed method is verified through a co-simulation, wherein the control input realized in MATLAB is applied to the human bio-mechanical model in OpenSim under varying payload conditions. Significant reductions in human effort in terms of human muscle torque and metabolic cost are observed with the proposed control strategy. Further, simulation results show that the performance of the AGC controller converges to that of the gravity compensation (GC) controller, demonstrating the efficacy of AGC-based online parameter learning.

Google has traditionally been against automatically generated content (AGC) stating that it is against their webmaster guidelines and falls into the category of spam. Given AI written content is also auto-generated, does Google considers AI-written content spam and violating guidelines? Has the position changed over time? As per the latest updates, the position seems to have changed. Let's explore Google's current position and updates over time in this article.

A new report from Allianz Global Corporate and Speciality (AGCS) has concluded that while AI-based technologies will bring in benefits for insurers, they will also create new loss and liability scenarios. Technologies such as chatbots, autonomous vehicles and connected machines are becoming more common in everyday life for many people. Allianz's report The Rise of Artificial Intelligence: Future Outlook and Emerging Riskspoints out that while AI brings many advantages for businesses – increased efficiency, fewer repetitive tasks, better customer experience – it could leave companies open to cyberattacks or technical failures that would cause large disruptions, leading to'extraordinary financial losses'. Michael Bruch, Head of Emerging Trends at AGCS, advised: "Active risk management strategies will be needed to maximise the net benefits of a full introduction of advanced AI applications into society." It is already estimated that a major global cyberattack has the potential to trigger losses in excess of $50 billion but even a half-day outage at a cloud service provider has the potential to generate losses of around $850 million, AGCS said.

The Bank of England has again intervened to ensure there isn't a fire sale of UK government bonds by pension funds. The Institute for Fiscal Studies has published a report saying the government will have to find £60bn of spending cuts over four years to pay for the recent mini-budget. The International Monetary Fund has restated its criticism of said mini-budget indicating that the unfunded cuts will ramp up inflation. With all this going on, you might have thought that Kwasi Kwarteng and his Treasury gang might have been feeling a bit chastened. After all, it's not every chancellor who gets to screw up their first budget on such a grand scale.

Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promising clustering performance on some real attributed networks. However, there is limited understanding of how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs. Existing methods essentially use graph convolution of a fixed and low order that only takes into account neighbours within a few hops of each node, which underutilizes node relations and ignores the diversity of graphs. In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs. We establish the validity of our method by theoretical analysis and extensive experiments on benchmark datasets. Empirical results show that our method compares favourably with state-of-the-art methods.

In a new report, Allianz Global Corporate & Specialty (AGCS), a division of global insurer Allianz, has claimed that the advantages offered by increasingly integrated Artificial Intelligence (AI) applications in the re/insurance industry may be outweighed by the potential threats they bring. AGCS claims that increased vulnerability to malicious cyber-attacks and technical failure, as well as the potential for larger-scale disruptions and extraordinary financial losses, pose significant risks to re/insurers as AI becomes more widely adopted in the industry, and as societies and economies become more interconnected. Additionally, insurers and reinsurers will have to contend with new liability scenarios as decision-making responsibilities shift from humans to machines and manufacturers. However, AGCS still maintains that the growing reliance on AI applications like chatbots, autonomous vehicles, and connected machines in digital factories offers many advantages for re/insurers in terms of increased efficiencies, fewer repetitive tasks, and better customer experiences. "There is huge potential for AI to improve the insurance value chain. Initially, it will help automate insurance processes to enable better delivery to our customers. Policies can be issued, and claims processed, faster and more efficiently," said Michael Bruch, Head of Emerging Trends at AGCS.

Evans Morris Gachiri, a fifth year Mechatronics Engineering student at Dedan Kimathi University of Technology, Kenya, demonstrates how a robotic coffee machine works: Artificial intelligence is no longer science fiction. London, March 22, 2018: Chatbots, autonomous vehicles, and connected machines in digital factories foreshadow what the future will look like: The widespread implementation of Artificial Intelligence (AI) applications brings many advantages for businesses such as increased efficiencies, fewer repetitive tasks and better customer experiences. Vulnerability to malicious cyber-attacks or technical failure will increase, as will the potential for larger-scale disruptions and extraordinary financial losses as societies and economies become increasingly interconnected. Companies will also face new liability scenarios as responsibility for decision-making shifts from human to machine and manufacturer. In the new report "The Rise of Artificial Intelligence: Future Outlook and Emerging Risks", insurer Allianz Global Corporate & Specialty (AGCS) identifies both the benefits and emerging risk concerns around the growing implementation of AI in society and industry, including in the insurance sector.

Chatbots, autonomous vehicles, and connected machines in digital factories foreshadow what the future will look like: The widespread implementation of Artificial Intelligence (AI) applications brings many advantages for businesses such as increased efficiencies, fewer repetitive tasks and better customer experiences. Vulnerability to malicious cyber-attacks or technical failure will increase, as will the potential for larger-scale disruptions and extraordinary financial losses as societies and economies become increasingly interconnected. Companies will also face new liability scenarios as responsibility for decision-making shifts from human to machine and manufacturer. In the new report "The Rise of Artificial Intelligence: Future Outlook and Emerging Risks", insurer Allianz Global Corporate & Specialty (AGCS) identifies both the benefits and emerging risk concerns around the growing implementation of AI in society and industry, including in the insurance sector. AI, also referred to as machine learning, is essentially software that is able to think and learn like a human.