This paper proposes a novel and scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. In most previous works on reinforcement learning for network optimization, routing and spectrum access are tackled as separate tasks; further, the wireless links in the network are assumed to be fixed, and a different agent is trained for each transmission node -- this limits scalability and generalizability. In this paper, we account for the inherent signal-to-interference-plus-noise ratio (SINR) in the physical layer and propose a more scalable approach in which a single agent is associated with each flow. Specifically, a single agent makes all routing and spectrum access decisions as it moves along the frontier nodes of each flow. The agent is trained according to the physical layer characteristics of the environment using the future bottleneck SINR as a novel reward definition. This allows a highly effective routing strategy based on the geographic locations of the nodes in the wireless ad-hoc network. The proposed deep reinforcement learning strategy is capable of accounting for the mutual interference between the links. It learns to avoid interference by intelligently allocating spectrum slots and making routing decisions for the entire network in a scalable manner.

The report referenced an "interoperability test [in which] Huawei and Megvii jointly provided a face-recognition solution based on Huawei's video cloud solution. In the solution, Huawei provided servers, storage, network equipment, its FusionSphere cloud platform, cameras and other software and hardware, [while] Megvii provided its dynamic facial-recognition system software".

Despite everything we've all been through, though, there were a few bright spots in the world of tech. Console makers blessed us with mouthwatering next-gen hardware, while Apple wowed the industry with the prowess of its own M1 CPU. Google also delivered an excellent phone for just $350, demonstrating an ability to not just read the room, but also to think of a world beyond a well-heeled tech-savvy audience. There are also companies that flourished during the global lockdown, and though truth continued to be contested throughout the US elections, we thankfully saw social media step up their efforts to combat misinformation. Clearly, staying home gave some of us the freedom to produce great products and fight for good. Apple's M1 system on a chip (SOC) may be tiny, but its impact on the computing industry will be felt for years to come. The first of Apple's silicon to reach Macs, the M1 is a powerhouse, with 8 CPU cores and up to 8 GPU cores. Both the M1-equipped MacBook Air and MacBook Pro blew away comparable Intel or AMD-based PCs in the Geekbench 5 benchmark.

Not only has 2020 been one of the most challenging years in the history of humanity, but it has also completely transformed our way of thinking and working. Many of the top technology trends help us navigate the crisis. Throughout the year, COVID-19 has hurt all areas of life, causing businesses to close, economies collapse, and people to be anxious. Amid panic and uncertainty, technology and connectivity have become vital elements that have given people and companies hope, courage, optimism, and the competence to continue. The year was marked by many technological trends surrounding the cloud, robotics, the Internet of Things (IoT), and augmented reality (AR), leading companies through the crisis.

"Two roads diverged in a wood and I – I took the one less traveled by." It may not be obvious at first, but Robert Frost's poem about standing at the crossroads of choice applies particularly well to the telecommunications industry. With the rise of 5G cellular networks and the need for more agility during COVID-19, Telco companies have to make a crucial decision: stay in the traditional lanes of providing connectivity or evolve with AI-powered digital transformation. For many Telcos, the "road less traveled" via AI isn't just a question of innovation; it's critical to developing new business models that are sustainable and scalable for the future. The Telco industry's AI reinvention lies in three key strategies: monetizing at the edge, saving costs through automation and improving customer engagement.

Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Massive multiple-input multiple-output (MIMO) for 5G. Since artificial intelligence (AI) is the defining technology of our time, it is natural to ask what role it could play for 6G. While it is clear that 6G must cater to the needs of large distributed learning systems, it is less certain if AI will play a defining role in the design of 6G itself. The goal of this article is to paint a vision of a new air interface which is partially designed by AI to enable optimized communication schemes for any hardware, radio environment, and application.

In this paper, a deep reinforcement learning (DRL)-based approach to the Lyapunov optimization is considered to minimize the time-average penalty while maintaining queue stability. A proper construction of state and action spaces is provided to form a proper Markov decision process (MDP) for the Lyapunov optimization. A condition for the reward function of reinforcement learning (RL) for queue stability is derived. Based on the analysis and practical RL with reward discounting, a class of reward functions is proposed for the DRL-based approach to the Lyapunov optimization. The proposed DRL-based approach to the Lyapunov optimization does not required complicated optimization at each time step and operates with general non-convex and discontinuous penalty functions. Hence, it provides an alternative to the conventional drift-plus-penalty (DPP) algorithm for the Lyapunov optimization. The proposed DRL-based approach is applied to resource allocation in edge computing systems with queue stability and numerical results demonstrate its successful operation.

SoftBank Group will sell an 80% stake in robotics firm Boston Dynamics to Hyundai, the trio said Friday, in a deal that values the U.S. company at $1.1 billion (¥114 billion). Boston Dynamics has drawn huge attention with viral videos of its humanoid and dog-like robots, whose uncanny movements and impressive tricks have helped stoke fears that androids could one day become a threat to humans. The engineering firm was founded in 1992 and bought in 2013 by Google, which sold the company on to SoftBank three years ago. The Japanese conglomerate will keep a 20% stake through one of its affiliates and will work with South Korea's Hyundai to "propel development and commercialization of advanced robots", the companies said. The financial terms of the deal were not disclosed.

Huawei and its partners have provided some of these surveillance products to authorities in the northwest Xinjiang region, where the Chinese Communist Party has sought for decades to control and assimilate the Uighurs, a Turkic ethnic minority, most recently through a massive "reeducation" program. Among them, according to documents from Huawei's website, was a facial recognition system used by police in the Xinjiang capital Urumqi, and a highway surveillance camera system for the region.

The telecom industry is no longer limited to providing primary phone and internet service. It is at the epicentre of technological growth, led by mobile and broadcast services in the internet of things (IoT) era. This growth is expected to continue, with Technavio estimating that the global telecom IoT market will post an impressive CAGR of over 42% by 2020. The driver of this growth is artificial intelligence (AI). AI has helped the telecom sector redefine customer experience, bringing new opportunities but also complicating business models.