Japan's major annual electronics show involving more than 300 companies opened Tuesday, with the spotlight on cutting-edge technologies designed to achieve carbon neutrality. As was the case last year, organizers decided to hold the Combined Exhibition of Advanced Technologies online as a precaution against the coronavirus. The event through Friday, under the theme of "Toward Society 5.0 with the New Normal," is accessible by the public with pre-registration. Rechargeable batteries to store renewable energy and carbon recycling technologies are among exhibited products that may help Japan and other countries reach the goal of net zero carbon emissions in the next several decades. The concept of Society 5.0 to incorporate innovative technologies such as artificial intelligence and robots into society has been promoted by Japanese industries and the government.

Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by having a nonlinear temporal dependence structure. We propose different statistical nonlinear models to manage these challenges of hard type datasets and forecast 15-min frequency electricity load up to 2-days ahead. We show that the Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) models applied to the production line of a chemical production facility outperform several other predictive models in terms of out-of-sample forecasting accuracy by the Diebold-Mariano (DM) test with several metrics. The predictive information is fundamental for the risk and production management of electricity consumers.

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is managed by Triad, a public service oriented, national security science organization equally owned by its three founding members: Battelle Memorial Institute (Battelle), the Texas A&M University System (TAMUS), and the Regents of the University of California (UC) for the Department of Energy's National Nuclear Security Administration. Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

To reach the Paris climate targets, global emission levels will need to be cut in half by 2030, reach net-zero by 2050 and stay net negative throughout the second half of the century. Yet, a just released BCG survey of 1,300 companies around the globe found that only 11% reduced their emissions in line with their stated ambitions over the past five years. "Measurement is a key roadblock with 91% of companies failing to measure the full scope of their emissions," Sylvain Duranton, the global leader of BCG GAMMA, a division dedicated to AI, data science and advanced analytics applied to business, said during an October 13 press conference. In a new study BCG maintains that artificial intelligence can help companies not only more accurately measure but also reduce carbon emissions. But using technology to measure carbon footprints and develop mitigation strategies is not enough.

Any discussion of quantum computing sounds like a leap in science fiction. Each new piece of information seems to call into question our accomplishments and our understanding of the world. In a world as captivating as that of quantum computing, there are certain truths hidden behind the difficulty of understanding quantum computers, yet they are worth highlighting. As we showed you in a previous article, quantum computers work thanks to " qubit, the quantitative equivalent ofBit of our modern computers. When we talk about the number of qubits of a quantum computer, we are generally talking about the number of logical qubits and not the number of physical qubits. In fact, due to the large error rate of the current qubits, it takes a large number of physical qubits to create a single "functional" logical qubit. To be more clear, imagine for example an old bot, you can ask it a question. It will only respond 7 out of 10 times and crashes regularly when you ask it your question.

Whether protecting against surges on electric networks, locating designs amongst previous criminal offenses, or even improving sources in the treatment of significantly bad people, Duke University computer system expert Cynthia Rudin desires expert system (AI) to reveal its own job. When it is actually creating choices that profoundly impact individuals's lifestyles, particularly. " I would like to give thanks to AAAI and also Squirrel AI for making this honor that I understand will definitely be actually a game-changer for the area," Rudin pointed out. "To possess a'Nobel Prize' for artificial intelligence to assist culture creates it ultimately crystal clear undeniably that this subject matter -- AI help the advantage for community -- is really significant." Dark container designs are actually the contrast of Rudin's straightforward codes.

Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNNs has a significant impact on model utility in the context of private deep learning, whereas its effect is largely unexplored in previous studies. In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS. To integrate private learning with architecture search, we delicately design a novel search space and propose a DP-aware method for training candidate models. We empirically certify the effectiveness of the proposed framework. The searched model DPNASNet achieves state-of-the-art privacy/utility trade-offs, e.g., for the privacy budget of $(\epsilon, \delta)=(3, 1\times10^{-5})$, our model obtains test accuracy of $98.57\%$ on MNIST, $88.09\%$ on FashionMNIST, and $68.33\%$ on CIFAR-10. Furthermore, by studying the generated architectures, we provide several intriguing findings of designing private-learning-friendly DNNs, which can shed new light on model design for deep learning with differential privacy.

The past decade has witnessed a surge of interest in practical techniques for projected model counting. Despite significant advancements, however, performance scaling remains the Achilles' heel of this field. A key idea used in modern counters is to count models projected on an \emph{independent support} that is often a small subset of the projection set, i.e. original set of variables on which we wanted to project. While this idea has been effective in scaling performance, the question of whether it can benefit to count models projected on variables beyond the projection set, has not been explored. In this paper, we study this question and show that contrary to intuition, it can be beneficial to project on variables beyond the projection set. In applications such as verification of binarized neural networks, quantification of information flow, reliability of power grids etc., a good upper bound of the projected model count often suffices. We show that in several such cases, we can identify a set of variables, called upper bound support (UBS), that is not necessarily a subset of the projection set, and yet counting models projected on UBS guarantees an upper bound of the true projected model count. Theoretically, a UBS can be exponentially smaller than the smallest independent support. Our experiments show that even otherwise, UBS-based projected counting can be more efficient than independent support-based projected counting, while yielding bounds of very high quality. Based on extensive experiments, we find that UBS-based projected counting can solve many problem instances that are beyond the reach of a state-of-the-art independent support-based projected model counter.

Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges. Data-driven decision-making algorithms from reinforcement learning (RL) offer a solution towards efficiently operating a clean energy system. Although RL algorithms achieve promising performance compared to model-based control models, there has been limited investigation of RL robustness in safety-critical physical systems. In this work, we first show that several competition-winning, state-of-the-art RL agents proposed for power system control are vulnerable to adversarial attacks. Specifically, we use an adversary Markov Decision Process to learn an attack policy, and demonstrate the potency of our attack by successfully attacking multiple winning agents from the Learning To Run a Power Network (L2RPN) challenge, under both white-box and black-box attack settings. We then propose to use adversarial training to increase the robustness of RL agent against attacks and avoid infeasible operational decisions. To the best of our knowledge, our work is the first to highlight the fragility of grid control RL algorithms, and contribute an effective defense scheme towards improving their robustness and security.

Given a Boolean formula $\varphi$ over the set of variables $X$ and a projection set $\mathcal{P} \subseteq X$, a subset of variables $\mathcal{I}$ is independent support of $\mathcal{P}$ if two solutions agree on $\mathcal{I}$, then they also agree on $\mathcal{P}$. The notion of independent support is related to the classical notion of definability dating back to 1901, and have been studied over the decades. Recently, the computational problem of determining independent support for a given formula has attained importance owing to the crucial importance of independent support for hashing-based counting and sampling techniques. In this paper, we design an efficient and scalable independent support computation technique that can handle formulas arising from real-world benchmarks. Our algorithmic framework, called Arjun, employs implicit and explicit definability notions, and is based on a tight integration of gate-identification techniques and assumption-based framework. We demonstrate that augmenting the state of the art model counter ApproxMC4 and sampler UniGen3 with Arjun leads to significant performance improvements. In particular, ApproxMC4 augmented with Arjun counts 387 more benchmarks out of 1896 while UniGen3 augmented with Arjun samples 319 more benchmarks within the same time limit.