Amazon Web Services brought its compute and storage capabilities to the mobile edge this week for developers in Boston and the San Francisco Bay area with its new AWS Wavelength on Verizon's 5G network. AWS' first Wavelength Zones use Verizon's 5G Edge mobile compute platform to allow developers and enterprises to deploy ultra-low latency-dependent applications to mobile and connected wireless devices at the edge of Verizon's 5G Ultra Wideband network. Use cases for 5G mobile edge compute using AWS Wavelength include machine learning (ML) inference at the edge, autonomous industrial equipment, connected cars, smart cities and factories, the Internet of Things, live and interactive video and game streaming, and virtual reality. Developers can build applications that provide near-real-time analytics for instant decision-making and automated robotic systems for manufacturing facilities. AWS Wavelength's debut comes eight months after AWS CEO Andy Jassy and Verizon CEO Hans Vestberg (pictured) unveiled the new service at the AWS re:Invent conference in Las Vegas in December.

Open-ended survey data constitute an important basis in research as well as for making business decisions. Collecting and manually analysing free-text survey data is generally more costly than collecting and analysing survey data consisting of answers to multiple-choice questions. Yet free-text data allow for new content to be expressed beyond predefined categories and are a very valuable source of new insights into people's opinions. At the same time, surveys always make ontological assumptions about the nature of the entities that are researched, and this has vital ethical consequences. Human interpretations and opinions can only be properly ascertained in their richness using textual data sources; if these sources are analyzed appropriately, the essential linguistic nature of humans and social entities is safeguarded. Natural Language Processing (NLP) offers possibilities for meeting this ethical business challenge by automating the analysis of natural language and thus allowing for insightful investigations of human judgements. We present a computational pipeline for analysing large amounts of responses to open-ended questions in surveys and extract keywords that appropriately represent people's opinions. This pipeline addresses the need to perform such tasks outside the scope of both commercial software and bespoke analysis, exceeds the performance to state-of-the-art systems, and performs this task in a transparent way that allows for scrutinising and exposing potential biases in the analysis. Following the principle of Open Data Science, our code is open-source and generalizable to other datasets. I CONTEXT AND MOTIVATION Leaders, managers, and decision-makers critically rely on information and feedback. Decisionmakers first need information about the current set of circumstances which provide the context of the decision, and then need feedback on how the decision could play out. To get such information in a format that allows them to appropriately understand the entity they are seeking to comprehend is of critical importance to come to a high-quality decision. Often only qualitative insight into the opinions, interpretations and assumptions of large numbers of people will allow us to understand a set of circumstances properly and are therefore required to make high-quality decisions and consequently outcomes.

We propose a novel stochastic network model, called Fractal Gaussian Network (FGN), that embodies well-defined and analytically tractable fractal structures. Such fractal structures have been empirically observed in diverse applications. FGNs interpolate continuously between the popular purely random geometric graphs (a.k.a. the Poisson Boolean network), and random graphs with increasingly fractal behavior. In fact, they form a parametric family of sparse random geometric graphs that are parametrized by a fractality parameter $\nu$ which governs the strength of the fractal structure. FGNs are driven by the latent spatial geometry of Gaussian Multiplicative Chaos (GMC), a canonical model of fractality in its own right. We asymptotically characterize the expected number of edges and triangle in FGNs. We then examine the natural question of detecting the presence of fractality and the problem of parameter estimation based on observed network data, in addition to fundamental properties of the FGN as a random graph model. We also explore fractality in community structures by unveiling a natural stochastic block model in the setting of FGNs.

Widespread deployment of 5G brings with it the promise of a network of connectivity with greatly improved latency and downtime. To reach full 5G capabilities, telecom service providers would be required to reach enough density to monetize the network by installing 5,000 to 20,000 5G small cells in every major city within the next five to ten years. This brings the network much closer to mobile phones and every type of IoT sensor or device that needs connectivity. The explosive increase in bandwidth will open new possibilities, from remote surgeries guided from New York City to a small African village, to enabling new ways for humans and robots to work together on a factory floor. Full 5G coverage is an ambitious goal, requiring the biggest telecom players to invest $20 billion annually in the US.

Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making, and thereby react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.

Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number of specialised qualitative reasoning tools have been developed so far, an important barrier to the wider adoption of these tools is that only qualitative reasoning is supported natively, when real-world problems most often require a combination of qualitative and other forms of reasoning. In this work, we propose to overcome this barrier by using ASP as a unifying formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. These encodings are experimentally evaluated using a real-world dataset based on a case study of determining optimal coverage of telecommunication antennas, and compared with the performance of two well-known dedicated reasoners. Experimental results show that the proposed encodings outperform one of the two reasoners, but fall behind the other, an acceptable trade-off given the added benefits of handling any type of reasoning as well as the interpretability of logic programs. This paper is under consideration for acceptance in TPLP.

The potential held by the gargantuan volumes of data being generated across networks worldwide has been truly unlocked by machine learning techniques and more recently Deep Learning. The advantages offered by the latter have seen it rapidly becoming a framework of choice for various applications. However, the centralization of computational resources and the need for data aggregation have long been limiting factors in the democratization of Deep Learning applications. Edge Computing is an emerging paradigm that aims to utilize the hitherto untapped processing resources available at the network periphery. Edge Intelligence (EI) has quickly emerged as a powerful alternative to enable learning using the concepts of Edge Computing. Deep Learning-based Edge Intelligence or Deep Edge Intelligence (DEI) lies in this rapidly evolving domain. In this article, we provide an overview of the major constraints in operationalizing DEI. The major research avenues in DEI have been consolidated under Federated Learning, Distributed Computation, Compression Schemes and Conditional Computation. We also present some of the prevalent challenges and highlight prospective research avenues.

Network traffic demand matrix is a critical input for capacity planning, anomaly detection and many other network management related tasks. The demand matrix is often computed from link load measurements. The traffic matrix (TM) estimation problem is the determination of the traffic demand matrix from link load measurements. The relationship between the link loads and the traffic matrix that generated the link load can be modeled as an under-determined linear system and has multiple feasible solutions. Therefore, prior knowledge of the traffic demand pattern has to be used in order to find a potentially feasible demand matrix. In this paper, we consider the TM estimation problem where we have information about the distribution of the demand sizes. This information can be obtained from the analysis of a few traffic matrices measured in the past or from operator experience. We develop an iterative projection based algorithm for the solution of this problem. If large number of past traffic matrices are accessible, we propose a Generative Adversarial Network (GAN) based approach for solving the problem. We compare the strengths of the two approaches and evaluate their performance for several networks using varying amounts of past data.

Huawei is also now the world's largest supplier of smartphones, surpassing Samsung Electronics Co. Qualcomm also announced a super-fast charging platform this week for Android devices that is supposed to charge a battery to 50% full in 5 minutes, and 100% full in 15 minutes. Xilinx wants to help drive open, interoperable, and adaptable Radio Access Network (RAN) 5G technologies. The company this week joined the Open RAN Policy Coalition, an organization that advocates for open and interoperable solutions in RAN. Xilinx is already a member of O-RAN alliance and is a contributor to the 3GPP specifications for 5G mobile networks. Xilinx offers silicon that supports multiple standards, bands, carriers and sub-networks for Open RAN, the company said in its press release.

AI is the superset of various techniques that allow machines to be artificially intelligent. Machine learning refers to a machine's ability to think without being externally programmed. While devices have traditionally been programmed with a set of rules for how to act, machine learning enables devices to learn directly from the data itself and become more intelligent over time as more data is collected. Deep learning is a machine learning technique that uses multiple neural network layers to progressively extract higher level features from the raw input data. For example, in image processing, lower layers of the neural network may identify edges, while higher layers may identify the concepts relevant to a human such as letters or faces.