Deterministic frequency deviations (DFDs) critically affect power grid frequency quality and power system stability. A better understanding of these events is urgently needed as frequency deviations have been growing in the European grid in recent years. DFDs are partially explained by the rapid adjustment of power generation following the intervals of electricity trading, but this intuitive picture fails especially before and around noonday. In this article, we provide a detailed analysis of DFDs and their relation to external features using methods from explainable Artificial Intelligence. We establish a machine learning model that well describes the daily cycle of DFDs and elucidate key interdependencies using SHapley Additive exPlanations (SHAP). Thereby, we identify solar ramps as critical to explain patterns in the Rate of Change of Frequency (RoCoF).

Litigating Artificial Intelligence: When Does AI Violate Our Legal Rights? Read full article May 27, 2021, 3:20 PM ·3 min read From the minds of Canada's leading law and technology experts comes a playbook for understanding the multi-faceted intersection of AI and the law TORONTO, May 27, 2021 (GLOBE NEWSWIRE) -- We are living in an Artificial Intelligence (AI) boom. Self-driving cars, personal voice assistants, and facial recognition technology are just a few of the AI-enabled technologies permeating into everyday life. But what happens when AI causes harm or violates our rights? If your self-driving car gets into an accident while on autopilot, are you responsible? Emond Publishing, Canada's leading independent legal publisher, today announced the release of Litigating Artificial Intelligence, a book examining AI-informed legal determinations, AI-based lawsuits, and AI-enabled litigation tools. Anchored by the expertise of general editors Jill R. Presser, Jesse Beatson, and Gerald Chan, this title offers practical insights regarding AI's decision-making capabilities, position in evidence law and product-based lawsuits, role in automating legal work, and use by the courts, tribunals, and government agencies. For example, can government agencies use AI-powered facial recognition software to identify BLM protestors and Capitol rioters, or does this violate privacy rights? Who is liable, users, developers, or AI? What laws are in place to prevent AI-related crimes, and how do litigators prosecute the responsible parties?

Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient? Balasubramanian, an associate professor of Mechanical Engineering and Mechanics, studies the basic physics of the materials at the heart of solar energy conversion – the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed – as well as the manufacturing processes that produce commercial solar cells. Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. "When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport," Balasubramanian said.

Industrial Revolution 4.0 (IR 4.0) may not often be associated with climate change mitigation, but its use of technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI) and cloud computing can actually play a pivotal role. Smart factories equipped with IR 4.0 capabilities can be more efficient and effective than ever before, ensuring that no energy or materials are wasted, observes Datuk Mohd Abdul Karim Abdullah, CEO of Serba Dinamik Holdings Bhd. Clean energy can also be integrated with IR 4.0 to power various processes and the transport of goods to the final consumer. "Investing in research and development to bring more awareness of how technology can encourage reuse, reduce, recycle and replace principles so that there is effective use of raw materials and energy is important," he says. IR 4.0 creates more efficiency and improves the way businesses are run.

In the blind deconvolution problem, we observe the convolution of an unknown filter and unknown signal and attempt to reconstruct the filter and signal. The problem seems impossible in general, since there are seemingly many more unknowns than knowns . Nevertheless, this problem arises in many application fields; and empirically, some of these fields have had success using heuristic methods -- even economically very important ones, in wireless communications and oil exploration. Today's fashionable heuristic formulations pose non-convex optimization problems which are then attacked heuristically as well. The fact that blind deconvolution can be solved under some repeatable and naturally-occurring circumstances poses a theoretical puzzle. To bridge the gulf between reported successes and theory's limited understanding, we exhibit a convex optimization problem that -- assuming signal sparsity -- can convert a crude approximation to the true filter into a high-accuracy recovery of the true filter. Our proposed formulation is based on L1 minimization of inverse filter outputs. We give sharp guarantees on performance of the minimizer assuming sparsity of signal, showing that our proposal precisely recovers the true inverse filter, up to shift and rescaling. There is a sparsity/initial accuracy tradeoff: the less accurate the initial approximation, the greater we rely on sparsity to enable exact recovery. To our knowledge this is the first reported tradeoff of this kind. We consider it surprising that this tradeoff is independent of dimension. We also develop finite-$N$ guarantees, for highly accurate reconstruction under $N\geq O(k \log(k) )$ with high probability. We further show stable approximation when the true inverse filter is infinitely long and extend our guarantees to the case where the observations are contaminated by stochastic or adversarial noise.

Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the accuracy gains are often based on specialized model designs using additional 32-bit components. Furthermore, almost all previous BNNs use 32-bit for feature maps and the shortcuts enclosing the corresponding binary convolution blocks, which helps to effectively maintain the accuracy, but is not friendly to hardware accelerators with limited memory, energy, and computing resources. Thus, we raise the following question: How can accuracy and energy consumption be balanced in a BNN network design? We extensively study this fundamental problem in this work and propose a novel BNN architecture without most commonly used 32-bit components: \textit{BoolNet}. Experimental results on ImageNet demonstrate that BoolNet can achieve 4.6x energy reduction coupled with 1.2\% higher accuracy than the commonly used BNN architecture Bi-RealNet. Code and trained models are available at: https://github.com/hpi-xnor/BoolNet.

Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories. However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous timesteps efficiently, as not all states from previous timesteps are preserved. We demonstrate that Expire-Span can help models identify and retain critical information and show it can achieve strong performance on reinforcement learning tasks specifically designed to challenge this functionality. Next, we show that Expire-Span can scale to memories that are tens of thousands in size, setting a new state of the art on incredibly long context tasks such as character-level language modeling and a frame-by-frame moving objects task. Finally, we analyze the efficiency of Expire-Span compared to existing approaches and demonstrate that it trains faster and uses less memory.

There is particular interest in Spike-based learning in plastic neuronal networks is deep learning, which is a central tool in modern machine playing increasingly key roles in both theoretical neuroscience learning. Deep learning relies on a layered, feedforward and neuromorphic computing. The brain learns network similar to the early layers of the visual cortex, in part by modifying the synaptic strengths between neurons with threshold nonlinearities at each layer that resemble and neuronal populations. While specific synaptic mean-field approximations of neuronal integrate-and-fire plasticity or neuromodulatory mechanisms may vary in models. While feedforward networks are readily translated different brain regions, it is becoming clear that a significant to neuromorphic hardware [6-8], the far more computationally level of dynamical coordination between disparate intensive training of these networks'on chip' neuronal populations must exist, even within an individual has proven elusive as the structure of backpropagation neural circuit [1]. Classically, backpropagation (BP, makes the algorithm notoriously difficult to implement and other learning algorithms) has been essential for supervised in a neural circuit [9, 10]. A feasible neural implementation learning in artificial neural networks (ANNs). of the backpropagation algorithm has gained renewed Although the question of whether or not BP operates in scrutiny with the rise of new neuromorphic computational the brain is still an outstanding issue [2], BP does solve architectures that feature local synaptic plasticity the problem of how a global objective function can be [5, 11-13]. Because of the well-known difficulties, neuromorphic related to local synaptic modification in a network.

This course will give an overview of all the topics we shall be looking at in this course. We shall begin by describing the oil value chain – the exploration and development, how oil is produced, shipped, and marketed. Moving further, we will learn about the importance of oil in the industry, both as a fuel and as a raw material in various forms in the global economy. Then, we will go through a brief history of oil – how it all began, and the different'kinds' of oil discoverers. We will be introduced to the major players in the oil market – the top producers and the major consumers. We will then see how oil is formed, how it sits deep within the earth and how we discover and refine it. We will learn about the different types of oils, and the methods employed to extract them. This will be followed by a brief overview of the different means of transporting oil, and the risks and benefits associated with the different methods of oil transport. Lastly, we shall look into the different oil benchmarks that prevail globally.

Cloud and edge computing are coming together as never before, leading to huge opportunities for developers and organizations around the world. Digital twins, mixed reality, and autonomous systems are at the core of a massive wave of innovation from which our customers already benefit. From the outside, it's not always apparent how this technology converges or the benefits that can be harnessed by bringing these capabilities together. This is why at Microsoft Build we talk about the possibilities this convergence creates, how customers are already benefitting, and our journey to making this technology easier to use and within reach of every developer and organization. Imagine taking any complex environment and applying the power of technology to create awe-inspiring experiences and reach new business heights that were previously unimaginable.