Personalization, automation and digitalization have redefined the fundamentals of business across nearly every industry, yet as Deloitte explains, the utility industry is transforming on a great scale. Utilities are investing in information technology (IT) to not just accommodate evolving customer needs and expectations but also improve operational workflows throughout the organization, including in marketing. Advanced IT solutions in the utility industry aim to reduce redundancies and unify technology systems. But in order to justify the increase in spending on vertically-aligned cloud software, utility leaders should maximize ROI by optimizing and extending their existing IT stack to ensure it enhances efficiency utility-wide rather than in siloed units. As the CMO of a company that offers AI-powered solutions for utilities, I've found that one of the simplest ways for utility CMOs to expand the efficiency of IT resources is by working with a CRM partner to integrate existing customer data and energy insights into a singular platform.

On any given day, the electric power industry's operations are complex and its responsibilities vast. As the industry continues to play a critical role in supporting global climate goal challenges, it must simultaneously support demand increases, surges in smart appliance adoption, and decentralized operating system expansions. Behind the scenes, there's the power grid operator, whose role is to monitor the electricity network 24 hours per day, 365 days per year. As a larger number of lower capacity systems (such as renewables) come online and advanced network components are integrated into the grid, generation becomes exponentially more complex, decentralized and variable, stretching control room operators to their limits. More locally, building owners and controllers (Figure 1) are being challenged to deploy grid-interactive intelligent elements that can flexibly participate in grid level operations to economically enhance grid resiliency (while also saving money for the building owner).

In the scope of a future-oriented collaboration in the field of industrial production, the Fraunhofer-Gesellschaft is cooperating with the VSB – Technical University of Ostrava (VSB-TUO). The partners research and develop the potential offered by energy management technologies, artificial intelligence (AI) and intelligent production in industry. The collaboration provides production companies with innovative solutions, which they can in turn use to develop innovative and sustainable solutions for reducing greenhouse gas emissions. This builds on over five years of successful collaboration between the Fraunhofer Institute for Machine Tools and Forming Technology IWU, the Fraunhofer Institute for Chemical Technology ICT and the VSB – Technical University of Ostrava (VSB-TUO). The ambitious venture "Fraunhofer Innovation Platform for Applied Artificial Intelligence for Materials & Manufacturing at VSB – Technical University of Ostrava FIP-AI@VSB-TUO" commenced operation on June 1, 2021.

COMSovereign Holding Corp. (NASDAQ: COMS) ("COMSovereign" or "Company"), a U.S.-based developer of 4G LTE Advanced and 5G Communication Systems and Solutions, today announced that it has executed an agreement to acquire RVision, Inc. ("RVision"), a developer of technologically advanced, environmentally hardened video and communications products and physical security solutions designed for government and private sector commercial industries. Terms of the transaction include total consideration of approximately $5.58 million consisting exclusively of shares of restricted common stock. The transaction is expected to close within approximately 15 days subject to traditional closing conditions. Smart Cities and Smart Campuses (educational and industrial) are urban areas designed to integrate advanced technologies including IoT ("Internet of Things"), AI ("Artificial Intelligence"), machine learning, Big Data, and sustainable or "green" energy systems to benefit and secure the daily lives of its residents. Around the world today, these technologies are being deployed to efficiently improve public services and safety through enhancements to everything from mass transportation and waste management to the real-time monitoring of environmental conditions including air and water quality.

Building on a project she began as an undergraduate, Higgins started the data-gathering process at Concordia's Building Aerodynamics/Wind Tunnel Lab. It can simulate wind gusts on large buildings with a 1 to 100 or smaller-scale model of a block of downtown Montreal, as well as on individual buildings of different shapes -- square, rectangular, U-shaped, T-shaped or L-shaped, and in different configurations. The lab also has a scale model of a section of the Louis-Hippolyte Lafontaine Bridge-Tunnel in east-end Montreal. "This preliminary work involved a lot of wind tunnel experiments with various building configurations," explains Stathopoulos, a professor in the Department of Building, Civil and Environmental Engineering at the Gina Cody School of Engineering and Computer Science. "Stéphanie ran tests for each of them, with wind coming from different directions, as it would in real life, and tried to predict what the amplification of the wind would be at each location. This particular experimentation was interesting because we are trying to see where we can get the highest wind speed. This is the opposite of what we usually do, which is to try to reduce exposure to wind to protect buildings from natural disasters."

Deb Campbell, a senior staff member in the HADR Systems Group, started the session with a discussion of how to accelerate the national and global response to climate change. "Because the timeline is so short and challenges so complex, it is essential to make good, evidence-based decisions on how to get to where we need to go," she said. "We call this approach systems analysis and architecture, and by taking this approach we can create a national climate change resilience roadmap." This roadmap implements more of what we already know how to do, for example utilizing wind and solar energy, and identifies gaps where research and development are needed to reach specific goals. One example is the transition to a fully zero-emission vehicle (ZEV) fleet in the United States in the coming decades; California has already directed that all of the state's new car sales be ZEV by 2035.

Identifying academic plagiarism is a pressing problem, among others, for research institutions, publishers, and funding organizations. Detection approaches proposed so far analyze lexical, syntactical, and semantic text similarity. These approaches find copied, moderately reworded, and literally translated text. However, reliably detecting disguised plagiarism, such as strong paraphrases, sense-for-sense translations, and the reuse of non-textual content and ideas, is an open research problem. The thesis addresses this problem by proposing plagiarism detection approaches that implement a different concept: analyzing non-textual content in academic documents, specifically citations, images, and mathematical content. To validate the effectiveness of the proposed detection approaches, the thesis presents five evaluations that use real cases of academic plagiarism and exploratory searches for unknown cases. The evaluation results show that non-textual content elements contain a high degree of semantic information, are language-independent, and largely immutable to the alterations that authors typically perform to conceal plagiarism. Analyzing non-textual content complements text-based detection approaches and increases the detection effectiveness, particularly for disguised forms of academic plagiarism. To demonstrate the benefit of combining non-textual and text-based detection methods, the thesis describes the first plagiarism detection system that integrates the analysis of citation-based, image-based, math-based, and text-based document similarity. The system's user interface employs visualizations that significantly reduce the effort and time users must invest in examining content similarity.

Adoption of deep neural networks in fields such as economics or finance has been constrained by the lack of interpretability of model outcomes. This paper proposes a generative neural network architecture -- the parameter encoder neural network (PENN) -- capable of estimating local posterior distributions for the parameters of a regression model. The parameters fully explain predictions in terms of the inputs and permit visualization, interpretation and inference in the presence of complex heterogeneous effects and feature dependencies. The use of Bayesian inference techniques offers an intuitive mechanism to regularize local parameter estimates towards a stable solution, and to reduce noise-fitting in settings of limited data availability. The proposed neural network is particularly well-suited to applications in economics and finance, where parameter inference plays an important role. An application to an asset pricing problem demonstrates how the PENN can be used to explore nonlinear risk dynamics in financial markets, and to compare empirical nonlinear effects to behavior posited by financial theory.

This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminative representation. We argue that for high-dimensional multi-class data, the optimal linear discriminative representation maximizes the coding rate difference between the whole dataset and the average of all the subsets. We show that the basic iterative gradient ascent scheme for optimizing the rate reduction objective naturally leads to a multi-layer deep network, named ReduNet, which shares common characteristics of modern deep networks. The deep layered architectures, linear and nonlinear operators, and even parameters of the network are all explicitly constructed layer-by-layer via forward propagation, although they are amenable to fine-tuning via back propagation. All components of so-obtained "white-box" network have precise optimization, statistical, and geometric interpretation. Moreover, all linear operators of the so-derived network naturally become multi-channel convolutions when we enforce classification to be rigorously shift-invariant. The derivation in the invariant setting suggests a trade-off between sparsity and invariance, and also indicates that such a deep convolution network is significantly more efficient to construct and learn in the spectral domain. Our preliminary simulations and experiments clearly verify the effectiveness of both the rate reduction objective and the associated ReduNet. All code and data are available at https://github.com/Ma-Lab-Berkeley. Keywords: rate reduction, linear discriminative representation, white-box deep network, multi-channel convolution, sparsity and invariance trade-off "What I cannot create, I do not understand."

The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities". There has been substantial recent progress in characterizing the fundamental statistical limits of community detection under simple stochastic block models. However, in real-world applications, the network structure is typically dynamic, with nodes that join over time. In this setting, we would like a detection algorithm to perform only a limited number of updates at each node arrival. While standard voting approaches satisfy this constraint, it is unclear whether they exploit the network information optimally. We introduce a simple model for networks growing over time which we refer to as streaming stochastic block model (StSBM). Within this model, we prove that voting algorithms have fundamental limitations. We also develop a streaming belief-propagation (StreamBP) approach, for which we prove optimality in certain regimes. We validate our theoretical findings on synthetic and real data.