We propose a data-driven method for controlling the frequency and convergence rate of black-box nonlinear dynamical systems based on the Koopman operator theory. With the proposed method, a policy network is trained such that the eigenvalues of a Koopman operator of controlled dynamics are close to the target eigenvalues. The policy network consists of a neural network to find a Koopman invariant subspace, and a pole placement module to adjust the eigenvalues of the Koopman operator. Since the policy network is differentiable, we can train it in an end-to-end fashion using reinforcement learning. We demonstrate that the proposed method achieves better performance than model-free reinforcement learning and model-based control with system identification.

Wireless sensor networks consist of randomly distributed sensor nodes for monitoring targets or areas of interest. Maintaining the network for continuous surveillance is a challenge due to the limited battery capacity in each sensor. Wireless power transfer technology is emerging as a reliable solution for energizing the sensors by deploying a mobile charger (MC) to recharge the sensor. However, designing an optimal charging path for the MC is challenging because of uncertainties arising in the networks. The energy consumption rate of the sensors may fluctuate significantly due to unpredictable changes in the network topology, such as node failures. These changes also lead to shifts in the importance of each sensor, which are often assumed to be the same in existing works. We address these challenges in this paper by proposing a novel adaptive charging scheme using a deep reinforcement learning (DRL) approach. Specifically, we endow the MC with a charging policy that determines the next sensor to charge conditioning on the current state of the network. We then use a deep neural network to parametrize this charging policy, which will be trained by reinforcement learning techniques. Our model can adapt to spontaneous changes in the network topology. The empirical results show that the proposed algorithm outperforms the existing on-demand algorithms by a significant margin.

Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices. To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON). Unlike GANs, GONs use a single network to both discriminate input and generate samples, significantly reducing their memory footprint. Leveraging the low memory footprint of GONs, we propose a decentralized fault-tolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation. Extensive experiments with real-world edge computing benchmarks on multiple Raspberry-Pi based federated edge configurations show that DRAGON can outperform the baseline methods in fault-detection and Quality of Service (QoS) metrics. Specifically, the proposed method gives higher F1 scores for fault-detection than the best deep learning (DL) method, while consuming lower memory than the heuristic methods. This allows for improvement in energy consumption, response time and service level agreement violations by up to 74, 63 and 82 percent, respectively.

The convergence of 5G architecture and deep learning has gained a lot of research interests in both the fields of wireless communication and artificial intelligence. This is because deep learning technologies have been identified to be the potential driver of the 5G technologies, that make up the 5G architecture. Hence, there have been extensive surveys on the convergence of 5G architecture and deep learning. However, most of the existing survey papers mainly focused on how deep learning can converge with a specific 5G technology, thus, not covering the full spectrum of the 5G architecture. Although there is a recent survey paper that appears to be robust, a review of that paper shows that it is not well structured to specifically cover the convergence of deep learning and the 5G technologies. Hence, this paper provides a robust overview of the convergence of the key 5G technologies and deep learning. The challenges faced by such convergence are discussed. In addition, a brief overview of the future 6G architecture, and how it can converge with deep learning is also discussed.

How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be two critical issues in building a task-oriented dialogue system. In this paper, we propose a novel manual-guided dialogue scheme to alleviate these problems, where the agent learns the tasks from both dialogue and manuals. The manual is an unstructured textual document that guides the agent in interacting with users and the database during the conversation. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains. We then contribute a fully-annotated multi-domain dataset MagDial to support our scheme. It introduces three dialogue modeling subtasks: instruction matching, argument filling, and response generation. Modeling these subtasks is consistent with the human agent's behavior patterns. Experiments demonstrate that the manual-guided dialogue scheme improves data efficiency and domain scalability in building dialogue systems. The dataset and benchmark will be publicly available for promoting future research.

We have been chosen as winners at Climate Hackathon 2022 competition organized by Microsoft. The aim of this competition was to find new solutions to prevent climate change by utilizing new technologies. We entered the competition with a solution that we had already started designing and working on, but this hackathon gave us some needed urgency to finalize it. Going forward, we are ready to continue turning the proposed solution into a marketable product, that can help other companies improve their environmental sustainability. The competition had three distinct challenges, from which teams could choose one to solve.

It sounds like the stuff of science fiction, but how worried should we be about artificial intelligence systems running rogue and potentially turning against us? All of these are at least 20 years old, with the latter being written approximately 3000 years ago, so if you've not caught up on them yet, you've only yourself to blame.) In the 1999 film The Matrix, which is set in the near future, the human race - worried by the increasing sentience and potential villainy of the artificial intelligence (AI) machines it's created - makes the decision to scorch the sky. They reason that without an energy source as abundant as the sun, the machines - which rely on solar power - will be crippled. "The human body generates more bioelectricity than a 120-volt battery, and over 25,000 BTUs of body heat," says one of the film's main characters, Lawrence Fishburne's Morpheus, in a voiceover.

Microscopic robots in the body could harvest energy from ultrasound to provide on-board control of autonomous behaviors such as measuring and communicating diagnostic information and precisely delivering drugs. This paper evaluates the acoustic power available to micron-size robots that collect energy using pistons. Acoustic attenuation and viscous drag on the pistons are the major limitations on the available power. Frequencies around 100kHz can deliver hundreds of picowatts to a robot in low-attenuation tissue within about 10cm of transducers on the skin, but much less in high-attenuation tissue such as a lung. However, applications of microscopic robots could involve such large numbers that the robots significantly increase attenuation, thereby reducing power for robots deep in the body. This paper describes how robots can collectively manage where and when they harvest energy to mitigate this attenuation so that a swarm of a few hundred billion robots can provide tens of picowatts to each robot, on average.

Abstract-- Voxel grids are a minimal and efficient environment representation that is used for robot motion planning in numerous tasks. Many state-of-the-art planning algorithms use voxel grids composed of free, occupied and unknown voxels. In this paper we propose a new GPU accelerated algorithm for partitioning the space into a voxel grid with occupied, free and unknown voxels. The proposed approach is low latency and suitable for high speed navigation. I. INTRODUCTION Many sensors (RGB-D cameras, stereo-matching...) output dense pointclouds as measurements and need to be processed and turned into an environment model/representation for motion planning.

In this paper, we propose a new method for multirotor planning in dynamic environments. The environment is represented as a temporal occupancy grid which gives the current as well as the future/predicted state of all the obstacles. The method builds on previous works in Safe Corridor generation and multirotor planning to avoid moving and static obstacles. It first generates a global path to the goal that doesn't take into account the dynamic aspect of the environment. We then use temporal Safe Corridors to generate safe spaces that the robot can be in at discrete instants in the future. Finally we use the temporal Safe Corridors in an optimization formulation that accounts for the multirotor dynamics as well as all the obstacles to generate the trajectory that will be executed by the multirotor's controller. We show the performance of our method in simulations.