It poses technical difficulty to achieve stable tracking even for single mismatched nonlinear strict-feedback systems when intermittent state feedback is utilized. The underlying problem becomes even more complicated if such systems are networked with directed communication and state-triggering setting. In this work, we present a fully distributed neuroadaptive tracking control scheme for multiple agent systems in strict-feedback form using triggered state from the agent itself and the triggered states from the neighbor agents. To circumvent the non-differentiability of virtual controllers stemming from state-triggering, we first develop a distributed continuous control scheme under regular state feedback, upon which we construct the distributed event-triggered control scheme by replacing the states in the preceding scheme with the triggered ones. Several useful lemmas are introduced to allow the stability condition to be established with such replacement, ensuring that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the output tracking error converging to a residual set around zero. Besides, with proper choices of the design parameters, the tracking performance in the mean square sense can be improved. Numerical simulation verifies the benefits and efficiency of the proposed method.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Could analog artificial intelligence (AI) hardware – rather than digital – tap fast, low-energy processing to solve machine learning's rising costs and carbon footprint? Researchers say yes: Logan Wright and Tatsuhiro Onodera, research scientists at NTT Research and Cornell University, envision a future where machine learning (ML) will be performed with novel physical hardware, such as those based on photonics or nanomechanics. These unconventional devices, they say, could be applied in both edge and server settings.

Cate Lawrence is an Australian tech journo living in Berlin. She focuses on all things mobility: ebikes, autonomous vehicles, VTOL, smart ci (show all) Cate Lawrence is an Australian tech journo living in Berlin. She focuses on all things mobility: ebikes, autonomous vehicles, VTOL, smart cities, and the future of alternative energy sources like electric batteries, solar, and hydrogen. This week farmers found big chunks of metal from a SpaceX Crew-1 Trunk in a remote paddock in rural Australia. While it's not an everyday occurrence, rocket body reentries (parts of space debris returning to Earth) are a trend that's likely to increase. Dr. Brad Tucker, Astrophysicist, and Cosmologist at Mt Stromlo Observatory at the Australian National University, went to check it out.

With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference platforms typically assume a homogeneous infrastructure and do not take into account the more complex and tiered computing infrastructure that includes edge devices, local hubs, edge datacenters, and cloud datacenters. On the other hand, recent AutoML efforts have provided viable solutions for model compression, pruning and quantization for heterogeneous environments; for a machine learning model, now we may easily find or even generate a series of models with different tradeoffs between accuracy and efficiency. We design and implement JellyBean, a system for serving and optimizing machine learning inference workflows on heterogeneous infrastructures. Given service-level objectives (e.g., throughput, accuracy), JellyBean picks the most cost-efficient models that meet the accuracy target and decides how to deploy them across different tiers of infrastructures. Evaluations show that JellyBean reduces the total serving cost of visual question answering by up to 58%, and vehicle tracking from the NVIDIA AI City Challenge by up to 36% compared with state-of-the-art model selection and worker assignment solutions. JellyBean also outperforms prior ML serving systems (e.g., Spark on the cloud) up to 5x in serving costs.

How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural circuitry. Many such algorithms utilize the framework of energy-based models (EBMs), in which all free variables in the model are optimized to minimize a global energy function. However, in the literature, these algorithms exist in isolation and no unified theory exists linking them together. Here, we provide a comprehensive theory of the conditions under which EBMs can approximate BP, which lets us unify many of the BP approximation results in the literature (namely, predictive coding, equilibrium propagation, and contrastive Hebbian learning) and demonstrate that their approximation to BP arises from a simple and general mathematical property of EBMs at free-phase equilibrium. This property can then be exploited in different ways with different energy functions, and these specific choices yield a family of BP-approximating algorithms, which both includes the known results in the literature and can be used to derive new ones.

While the global ESG agenda (Environment, Social, and Corporate Governance) is guided by agreements established between countries[1]), the development of ESG principles is happening through corporate, research, and academic standards. Many companies have started to develop their ESG strategies, allocating full-fledged functions and departments dedicated to the agenda, publishing annual reports on sustainable development, providing additional funds for research, including digital technologies and AI. Despite growing influence of ESG agenda, it remains the problem of transparent and objective quantitative evaluation of ESG progress in particular in environmental protection. This is of great importance for IT industry, as about one percent of the world's electricity is consumed by cloud computing, and its share continues to grow.[2] Artificial Intelligence (AI) and machine learning (ML) being a big part of today's IT industry are rapidly evolving technologies with massive potential for disruption. There are number of ways in which AI and ML could mitigate environmental problems and human-induced impact.

We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based method for obtaining approximate solutions to explicit model predictive control (MPC) problems. In DPC, the predictive control policy parametrized by a neural network is optimized offline via direct policy gradients obtained by automatic differentiation of the MPC problem. The proposed approach exploits a new form of sampled-data barrier function to enforce offline and online safety requirements in DPC settings while only interrupting the neural network-based controller near the boundary of the safe set. The effectiveness of the proposed approach is demonstrated in simulation.

The implementation of energy communities represents a cross-disciplinary phenomenon that has the potential to support the energy transition while fostering citizens' participation throughout the energy system and their exploitation of renewables. An important role is played by online information sources in engaging people in this process and increasing their awareness of associated benefits. In this view, this work analyses online news data on energy communities to understand people's awareness and the media importance of this topic. We use the Semantic Brand Score (SBS) indicator as an innovative measure of semantic importance, combining social network analysis and text mining methods. Results show different importance trends for energy communities and other energy and society-related topics, also allowing the identification of their connections. Our approach gives evidence to information gaps and possible actions that could be taken to promote a low-carbon energy transition.

We propose a novel method for training a neural network for image classification to reduce input data dynamically, in order to reduce the costs of training a neural network model. As Deep Learning tasks become more popular, their computational complexity increases, leading to more intricate algorithms and models which have longer runtimes and require more input data. The result is a greater cost on time, hardware, and environmental resources. By using data reduction techniques, we reduce the amount of work performed, and therefore the environmental impact of AI techniques, and with dynamic data reduction we show that accuracy may be maintained while reducing runtime by up to 50%, and reducing carbon emission proportionally.

Lean combustion is environment friendly with low NOx emissions and also provides better fuel efficiency in a combustion system. However, approaching towards lean combustion can make engines more susceptible to lean blowout. Lean blowout (LBO) is an undesirable phenomenon that can cause sudden flame extinction leading to sudden loss of power. During the design stage, it is quite challenging for the scientists to accurately determine the optimal operating limits to avoid sudden LBO occurrence. Therefore, it is crucial to develop accurate and computationally tractable frameworks for online LBO detection in low NOx emission engines. To the best of our knowledge, for the first time, we propose a deep learning approach to detect lean blowout in combustion systems. In this work, we utilize a laboratory-scale combustor to collect data for different protocols. We start far from LBO for each protocol and gradually move towards the LBO regime, capturing a quasi-static time series dataset at each condition. Using one of the protocols in our dataset as the reference protocol and with conditions annotated by domain experts, we find a transition state metric for our trained deep learning model to detect LBO in the other test protocols. We find that our proposed approach is more accurate and computationally faster than other baseline models to detect the transitions to LBO. Therefore, we recommend this method for real-time performance monitoring in lean combustion engines.