In this paper, we present a Stochastic Deep Neural Network-based Model Reference Adaptive Control. Building on our work "Deep Model Reference Adaptive Control", we extend the controller capability by using Bayesian deep neural networks (DNN) to represent uncertainties and model non-linearities. Stochastic Deep Model Reference Adaptive Control uses a Lyapunov-based method to adapt the output-layer weights of the DNN model in real-time, while a data-driven supervised learning algorithm is used to update the inner-layers parameters. This asynchronous network update ensures boundedness and guaranteed tracking performance with a learning-based real-time feedback controller. A Bayesian approach to DNN learning helped avoid over-fitting the data and provide confidence intervals over the predictions. The controller's stochastic nature also ensured "Induced Persistency of excitation," leading to convergence of the overall system signal.

Modern hardware design trends have shifted towards specialized hardware acceleration for computationally intensive tasks like machine learning and computer vision. While these complex workloads can be accelerated by commercial GPUs, domain-specific hardware is far more optimal when needing to meet the stringent memory, throughput, and power constraints of mobile and embedded devices. This paper proposes and evaluates a Binary-Enabled Architecture for Neural Network Acceleration (BEANNA), a neural network hardware accelerator capable of processing both floating point and binary network layers. Through the use of a novel 16x16 systolic array based matrix multiplier with processing elements that compute both floating point and binary multiply-adds, BEANNA seamlessly switches between high precision floating point and binary neural network layers. Running at a clock speed of 100MHz, BEANNA achieves a peak throughput of 52.8 GigaOps/second when operating in high precision mode, and 820 GigaOps/second when operating in binary mode. Evaluation of BEANNA was performed by comparing a hybrid network with floating point outer layers and binary hidden layers to a network with only floating point layers. The hybrid network accelerated using BEANNA achieved a 194% throughput increase, a 68% memory usage decrease, and a 66% energy consumption decrease per inference, all this at the cost of a mere 0.23% classification accuracy decrease on the MNIST dataset.

Seeking to represent natural language, researchers have found language models (LM) with Sesame Street-inspired names [1] [2] [3] to be incredibly effective methods of producing language representations (LR). These LM's have leverage transfer learning by training on a large text corpus to learn a good representation of language which can then be used in a down steam task like Question Answering or Entity Resolution. While these LMs have shown to be excellent methods to enable language understanding, the ability to train these models is becoming increasingly computationally expensive [4]. Since model performance is closely tied to the size of training data, model size, and compute used to train [5] the bulk of existing research has focused on scaling these aspects without much focus on increasing efficiency of training. Seeking to explore what methods could be used to make LM training more efficient we study the effect of curriculum learning by training ELMo with a wide variety of curricula.

Humanitarian mapping from space with machine learning helps policy-makers to timely and accurately identify people in need. However, recent concerns around fairness and transparency of algorithmic decision-making are a significant obstacle for applying these methods in practice. In this paper, we study if humanitarian mapping approaches from space are prone to bias in their predictions. We map village-level poverty and electricity rates in India based on nighttime lights (NTLs) with linear regression and random forest and analyze if the predictions systematically show prejudice against scheduled caste or tribe communities. To achieve this, we design a causal approach to measure counterfactual fairness based on propensity score matching. This allows to compare villages within a community of interest to synthetic counterfactuals. Our findings indicate that poverty is systematically overestimated and electricity systematically underestimated for scheduled tribes in comparison to a synthetic counterfactual group of villages. The effects have the opposite direction for scheduled castes where poverty is underestimated and electrification overestimated. These results are a warning sign for a variety of applications in humanitarian mapping where fairness issues would compromise policy goals.

In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. However, a key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. To address this, we propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas on the terrain with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.

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ISTRIBUTED heuristics are a promising field for current and future energy systems control and optimization tasks, In [12] we showed that different communication topologies and have been designed and evaluated in recent years on have an effect on the performance of the reflected algorithm agent-based systems [1] [2] [3]. While conventional control class: Highly meshed topologies converged into good solutions systems - centralized or hierarchical in their control paradigm - reliably and quickly, but increased communication overhead and perfectly fit to centralized generation and transmission systems, premature convergence. In contrast, results for sparsely meshed distributed renewable energy systems show properties that topologies were much less reliable. In the application domain promote the application of distributed optimization systems: of energy systems as critical infrastructures, this behavior is First, future energy systems can be regarded as complex highly unwanted. We presume that dynamically adjusting the systems of systems, sometimes framed as cyber-physical multienergy topology during runtime leads to a beneficial transition of systems, coupling communication systems, power, heat exploration and exploitation of the search space for distributed and gas systems.

In many systems motion occurs on deformed and deformable surfaces, setting up the possibility for dynamical interactions solely mediated by the coupling of the entities with their environment. Here we study the "two-body" dynamics of robot locomotion on a highly deformable spandex membrane in two scenarios: one in which a robot orbits a large central depression and the other where the two robots affect each other's motion solely through mutual environmental deformations. Inspired by the resemblance of the orbits of the single robot with those of general relativistic orbits around black holes, we recast the vehicle plus membrane dynamics in physical space into the geodesic motion of a "test particle" in a fiducial curved space-time and demonstrate how this framework facilitates understanding the observed dynamics. The two-robot problem also exhibits a resemblance with Einstein's general relativistic view of gravity, which in the words of Wheeler: "spacetime tells matter how to move; matter tells spacetime how to curve." We generalize this case the mapping to include a reciprocal coupling that translates into robotic curvature-based control schemes which modify interaction (promoting avoidance or aggregation) without long-range sensing. Our work provides a starting point for developing a mechanical analog gravity system as well as develops a framework that can provide insights into active matter in deformable environments and robot exploration in complex landscapes.

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

Environmental, social and governance (ESG) factors should be key considerations for CTOs and technology leaders scaling next generation companies from day one. Investors are increasingly prioritizing startups that focus on ESG, with the growth of sustainable investing skyrocketing. It's simple: Consumers are no longer willing to support companies that don't prioritize sustainability. According to a survey conducted by IBM, the COVID-19 pandemic has elevated consumers' focus on sustainability and their willingness to pay out of their own pockets for a sustainable future. In tandem, federal action on climate change is increasing, with the U.S. rejoining the Paris Climate Agreement and a recent executive order on climate commitments.