Networks are evolving to meet user demands. Main qualities which make conventional internet successful are heterogeneity and generality combining with user transparency and rich functionality for end-to-end systems. In today's world networks display characteristics of unstable convoluted systems. Till date most networks are murky to its applications and providing only best effort delivery of packets with little or zero information about the reliability and performance characteristics of different paths. Granting, this design works well for simple server-client model, many emerging technologies such as: NFV (Network Function Virtualization [8], IoT (Internet of Things) [9], Software Defined Networking [10], CDN (Content Delivery Networks) [11] and LTE (Long-Term Evolution) [12] and 5G Cellular Networks [13] heavily depend on affluent information about the state of the network. For example, author in [14] described, if VNFs (Virtual Network Functions) [15] are not aware of the traffic on virtio interfaces assisting hypervisor, then this might result in a bottleneck in NFV infrastructure. In other words, VNFs should know the state of the network (in terms of traffic) to accelerate applications hosted across VNFs in NFV infrastrucutre. Authors in [16] explained the need of the data storage as the number of connected IoT devices are increasing on unprecedented level [17]. In order to optimize the data storage, it is imperative for IoT nodes to know about the other nodes and their transportation method of moving data among networks.

Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently. Most of the relevant literature is focused on the single cell setting where a single IRS is deployed, while static and perfect channel state information (CSI) is assumed. In this work, we develop a novel methodology for multi-IRS-assisted multi-cell networks in the uplink. We formulate the sum-rate maximization problem aiming to jointly optimize the IRS reflect beamformers, base station (BS) combiners, and user equipment (UE) transmit powers. In this optimization, we consider the scenario in which (i) channels are dynamic and (ii) only partial CSI is available at each BS; specifically, scalar effective channels of local UEs and some of the interfering UEs. In casting this as a sequential decision making problem, we propose a multi-agent deep reinforcement learning algorithm to solve it, where each BS acts as an independent agent in charge of tuning the local UEs transmit powers, the local IRS reflect beamformer, and its combiners. We introduce an efficient message passing scheme that requires limited information exchange among the neighboring BSs to cope with the non-stationarity caused by the coupling of actions taken by multiple BSs. Our numerical simulations show that our method obtains substantial improvement in average data rate compared to several baseline approaches, e.g., fixed UEs transmit power and maximum ratio combining.

The agreements followed the announcement of Saudi Arabia's National Strategy for Data and Artificial Intelligence, launched during the Global AI Summit Saudi Arabia has signed a series of partnership agreements with international tech companies to advance artifical intelligence (AI) in the kingdom. The agreements, which were rigned at the virtual Global AI Summit held in Riyadh, are underpinned by Saudi Arabia's newly-launched National Strategy for Data and Artificial Intelligence (NSDAI). Saudi Arabia's National Center for Artificial Intelligence (NCAI) announced a memorandum of understanding (MoU) with China's Huawei to enable strategic cooperation on the kingdom's National AI Capability Development Program. Under the MoU, Huawei will support the NCAI to train Saudi AI engineers and students, and to address Arabic language AI-related capabilities. NCAI and Huawei will also explore the creation of an AI Capability Platform to localise technology solutions.

Perhaps humans weren't meant to be the early adopters of 5G. At Bosch Rexroth in Bavaria, Germany, wheeled robots that zoom between manufacturing machines and robotic arms that help hoist and connect components come with an unusual new feature--5G modems. The division of Bosch that sells advanced manufacturing equipment sees 5G as a big future trend--and not just for gaming or superfast movie downloads. The company has developed a modular production line where every piece of equipment--plus high-precision power tools--is connected via 5G. The new wireless standard may seem underwhelming so far to smartphone users, but it's gaining followers at some factories, office compounds, and remote workspaces, with good reason.

Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation. This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpreting of knowledge for both human and machine. Therefore, KGs continue to be used as a main driver to tackle a plethora of real-life problems in dissimilar domains. However, there is no consensus on a plausible and inclusive definition to domain KG. Further, in conjunction with several limitations and deficiencies, various domain KG construction approaches are far from perfection. This survey is the first to provide an inclusive definition to the notion of domain KG. Also, a comprehensive review of the state-of-the-art approaches drawn from academic works relevant to seven dissimilar domains of knowledge is provided. The scrutiny of the current approaches reveals a correlated array of limitations and deficiencies. The set of improvements to address the limitations of the current approaches are introduced followed by recommendations and opportunities for future research directions.

In this paper, a novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance multi-user downlink transmissions over millimeter wave (mmWave) frequencies. In order to maximize the downlink sum-rate, the optimal precoding matrix (at the base station) and reflection coefficient (at the IR) are jointly derived. Next, to address the uncertainty of mmWave channels and maintain line-of-sight links in a real-time manner, a distributional reinforcement learning approach, based on quantile regression optimization, is proposed to learn the propagation environment of mmWave communications, and, then, optimize the location of the UAV-IR so as to maximize the long-term downlink communication capacity. Simulation results show that the proposed learning-based deployment of the UAV-IR yields a significant advantage, compared to a non-learning UAV-IR, a static IR, and a direct transmission schemes, in terms of the average data rate and the achievable line-of-sight probability of downlink mmWave communications.

In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, \emph{MAB with cost subsidy}, which models many real-life applications where the learning agent has to pay to select an arm and is concerned about optimizing cumulative costs and rewards. We present two applications, \emph{intelligent SMS routing problem} and \emph{ad audience optimization problem} faced by a number of businesses (especially online platforms) and show how our problem uniquely captures key features of these applications. We show that naive generalizations of existing MAB algorithms like Upper Confidence Bound and Thompson Sampling do not perform well for this problem. We then establish fundamental lower bound of $\Omega(K^{1/3} T^{2/3})$ on the performance of any online learning algorithm for this problem, highlighting the hardness of our problem in comparison to the classical MAB problem (where $T$ is the time horizon and $K$ is the number of arms). We also present a simple variant of \textit{explore-then-commit} and establish near-optimal regret bounds for this algorithm. Lastly, we perform extensive numerical simulations to understand the behavior of a suite of algorithms for various instances and recommend a practical guide to employ different algorithms.

The pandemic has sent shockwaves through global supply chains, forcing business leaders to reexamine and recalibrate their procurement and outsourcing strategies. Manufacturing and logistics will likely be disrupted for many months to come. And in the long term, there will almost certainly be a greater emphasis on supply chain resilience to better withstand future crises. Businesses will be looking to technology, and 5G in particular, to provide some of the answers. One of the trends emerging out of the pandemic is that supply chains will become shorter and more regional, with less demand for suppliers based in hubs like China.

Happy Halloween to everybody, hope people had a good bank holiday weekend and didn't miss us this week. Kicking off the talks we're joined by Ali Karaali who is going to share his research on deep fakes and how to spot them. We're joined by a couple of folks from Huawei who are join tell about their efforts in putting machine at scale on mobile devices. AGENDA: [18:40 - 19:00] Getting Online [19:00 - 19:10] Welcome [19:10 - 19:30] Ali Karaali, Postdoctoral Research@ Sigmedia Group & ADAPT Centre, TCD How to spot fake videos of real people [19:30 - 19:50] Giovanni Laquidara, Developer Advocate @ Huawei Mindspore is an all-scenario deep learning framework optimized for parallel distributed training, easy adaptable for IOT and open source [19:50- 20:10] William Zhang, Product Manager @ Huawei Machine Learning Kit Open machine learning service inspiring your life This event is strictly for Machine Learning professionals, researchers and students only • If you finally can't make it, please RSVP to "NO" as soon as possible so that other people can take your place.

We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradient updates decrease the expressivity of the current value network. We characterize this loss of expressivity in terms of a drop in the rank of the learned value network features, and show that this corresponds to a drop in performance. We demonstrate this phenomenon on widely studies domains, including Atari and Gym benchmarks, in both offline and online RL settings. We formally analyze this phenomenon and show that it results from a pathological interaction between bootstrapping and gradient-based optimization. We further show that mitigating implicit under-parameterization by controlling rank collapse improves performance.