We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equation (eSDE) in terms of physically meaningful coarse mean-field variables through a deep-learning ResNet architecture inspired by numerical stochastic integrators. We construct an approximate effective bifurcation diagram based on the identified drift term of the eSDE and contrast it with the mean-field SIS model bifurcation diagram. We observe a subcritical Hopf bifurcation in the evolving network's effective SIS dynamics, that causes the tipping point behavior; this takes the form of large amplitude collective oscillations that spontaneously -- yet rarely -- arise from the neighborhood of a (noisy) stationary state. We study the statistics of these rare events both through repeated brute force simulations and by using established mathematical/computational tools exploiting the right-hand-side of the identified SDE. We demonstrate that such a collective SDE can also be identified (and the rare events computations also performed) in terms of data-driven coarse observables, obtained here via manifold learning techniques, in particular Diffusion Maps. The workflow of our study is straightforwardly applicable to other complex dynamics problems exhibiting tipping point dynamics.

Wearable startup Humane AI has been dripping details about its upcoming device, the AI Pin, for months now. We firs saw it at a TED Talk in May and, more recently, got a glimpse of its promised capabilities at Paris Fashion Week, ahead of Thursday's official unveiling. However many questions regarding how the wearable AI will actually do what it says it will remain to be answered. Here's what we do know: The Humane AI Pin is a pocket-worn wearable AI assistant that can reportedly perform the tasks that many modern cellphones and digital assistants do, but in a radically different form factor. It has no screen, instead reportedly operating primarily through voice commands and occasionally through a virtual screen projected onto the user's hand.

The Ai Pin from Humane, a much-hyped startup founded by former Apple employees, will cost $699. That'a according to The Verge, which obtained documents about the device ahead of its official launch on November 9. In addition, the Pin will reportedly have a monthly $24 subscription fee for access to T-Mobile's cellular network and large language models from OpenAI and Microsoft to power its smarts. The Ai Pin is a device that's about the size of a large business card that clips on to your clothing magnetically and acts as a personalized assistant controlled via voice and touch. Notably, it doesn't have a screen.

The evolution of wireless mobile networks towards cloudification, where Radio Access Network (RAN) functions can be hosted at either a central or distributed locations, offers many benefits like low cost deployment, higher capacity, and improved hardware utilization. Nevertheless, the flexibility in the functional deployment comes at the cost of stringent fronthaul (FH) capacity and latency requirements. One possible approach to deal with these rigorous constraints is to use FH compression techniques. To ensure that FH capacity and latency requirements are met, more FH compression is applied during high load, while less compression is applied during medium and low load to improve FH utilization and air interface performance. In this paper, a model-free deep reinforcement learning (DRL) based FH compression (DRL-FC) framework is proposed that dynamically controls FH compression through various configuration parameters such as modulation order, precoder granularity, and precoder weight quantization that affect both FH load and air interface performance. Simulation results show that DRL-FC exhibits significantly higher FH utilization (68.7% on average) and air interface throughput than a reference scheme (i.e. with no applied compression) across different FH load levels. At the same time, the proposed DRL-FC framework is able to meet the predefined FH latency constraints (in our case set to 260 $\mu$s) under various FH loads.

In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evident that isolated hardware will be insufficient. Increasingly complex AI tasks demand shared resources, cross-device collaboration, and multiple data types, all without compromising user privacy or quality of experience. To address this, we introduce a novel paradigm centered around EdgeAI-Hub devices, designed to reorganise and optimise compute resources and data access at the consumer edge. To this end, we lay a holistic foundation for the transition from on-device to Edge-AI serving systems in consumer environments, detailing their components, structure, challenges and opportunities.

Cooperative utilization of Unmanned Aerial Vehicles (UAVs) in public and military surveillance applications has attracted significant attention in recent years. Most UAVs are equipped with sensors that have bounded coverage and wireless communication equipment with limited range. Such limitations pose challenging problems to monitor mobile targets. This paper examines fulfilling surveillance objectives to achieve better coverage while building a resilient network between UAVs with an extended lifetime. The multiple target tracking problem is studied by including a relay UAV within the fleet whose trajectory is autonomously calculated in order to achieve a reliable connected network among all UAVs. Optimization problems are formulated for single-hop and multi-hop communications among UAVs. Three heuristic algorithms are proposed for multi-hop communications and their performances are evaluated. A hybrid algorithm, which dynamically switches between single-hop and multi-hop communications is also proposed. The effect of the time horizon considered in the optimization problem is studied. Performance evaluation results show that the trajectories generated for the relay UAV by the hybrid algorithm can achieve network lifetimes that are within 5% of the maximum possible network lifetime which can be obtained if the entire trajectories of all targets were known a priori.

In recent years, the cellular industry has witnessed a major evolution in communication technologies. It is evident that the Next Generation of cellular networks(NGN) will play a pivotal role in the acceptance of emerging IoT applications supporting high data rates, better Quality of Service(QoS), and reduced latency. However, the deployment of NGN will introduce a power overhead on the communication infrastructure. Addressing the critical energy constraints in 5G and beyond, this study introduces an innovative load transfer method using drone-carried airborne base stations (BSs) for stable and secure power reallocation within a green micro-grid network. This method effectively manages energy deficit by transferring aerial BSs from high to low-energy cells, depending on user density and the availability of aerial BSs, optimizing power distribution in advanced cellular networks. The complexity of the proposed system is significantly lower as compared to existing power cable transmission systems currently employed in powering the BSs. Furthermore, our proposed algorithm has been shown to reduce BS power outages while requiring a minimum number of drone exchanges. We have conducted a thorough review on real-world dataset to prove the efficacy of our proposed approach to support BS during high load demand times

In this article, we provide an overview of machine learning (ML) methods, both classical and deep variants, that are used to implement self-healing for cell outages in cellular networks. Self-healing is a promising approach to network management, which aims to detect and compensate for cell outages in an autonomous way. This technology aims to decrease the expenses associated with the installation and maintenance of existing 4G and 5G, i.e. emerging 6G networks by simplifying operational tasks through its ability to heal itself. We provide an overview of the basic concepts and taxonomy for SON, self-healing, and ML techniques, in network management. Moreover, we review the state-of-the-art in literature for cell outages, with a particular emphasis on ML-based approaches.

A mobile ad hoc network is made up of a number of wireless portable nodes that spontaneously come together en route for establish a transitory network with no need for any central management. A mobile ad hoc network (MANET) is made up of a sizable and reasonably dense community of mobile nodes that travel across any terrain and rely solely on wireless interfaces for communication, not on any well before centralized management. Furthermore, routing be supposed to offer a method for instantly delivering data across a network between any two nodes. Finding the best packet routing from across infrastructure is the major issue, though. The proposed protocol's major goal is to identify the least-expensive nominal capacity acquisition that assures the transportation of realistic transport that ensures its durability in the event of any node failure. This study suggests the Optimized Route Selection via Red Imported Fire Ants (RIFA) Strategy as a way to improve on-demand source routing systems. Predicting Route Failure and energy Utilization is used to pick the path during the routing phase. Proposed work assess the results of the comparisons based on performance parameters like as energy usage, packet delivery rate (PDR), and end-to-end (E2E) delay. The outcome demonstrates that the proposed strategy is preferable and increases network lifetime while lowering node energy consumption and typical E2E delay under the majority of network performance measures and factors.

Anyone who attends a media event in the technology space does their own reporting, draws their own conclusions. And we all shared the same experiences in a small bubble on Maui. But yes, a few years of struggling to convince the world that Windows on Arm (Qualcomm) could work, Qualcomm seems to be back on track in laptops, thanks to the Oryon CPU and the Snapdragon X Elite chip. Reporters (I, among them) headed to Maui with concerns that it would happen yet again. Qualcomm projected a sense of confidence in eye-popping numbers that could double Intel's performance in various categories.