Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators

Novel negative regulation of LIN-12/Notch in Caenorhabditis elegans

Proper cell fate specification is crucial for development, and dysregulation in cellular signaling pathways can lead to deleterious effects like cancer. The conserved LIN- 12/Notch signaling pathway mediates fate specification in many cellular contexts, and multiple regulatory mechanisms ensures appropriate LIN-12/Notch activity in each context. Here, I have identified several cis-regulatory domains and trans-acting factors that contribute to the negative regulation of LIN-12/Notch in Caenorhabditis elegans. In this thesis, I find that LIN-12/Notch requires binding to LAG-1/CSL and association with the nuclear complex for protein turnover in the C. elegans vulval precursor cells (VPCs). I also identify two layers of negative regulation in the VPCs and their descendants. The E3 ubiquitin ligase SEL-10/Fbw7 mediates degradation of LIN-12/Notch via the PEST domain in the VPCs, while a novel structural conformation in the C-terminal end of the LIN-12/Notch intracellular domain is required for downregulation in the descendants. Through an RNAi screen for negative regulators, I isolated 13 conserved kinases that downregulate LIN-12/Notch activity. Of these 13 kinases, CDK-8 had been previously implicated in Notch turnover, while the other 12 are novel negative regulators. I provide evidence that 5 of the kinases regulate LIN-12/Notch through modulation of the intracellular domain. Furthermore, I conduct a deeper investigation into CDK-8, which is the kinase component of the Mediator complex. I determine that CDK-8 acts with the rest of the Cdk8 Kinase Module and independent of the Mediator core to negatively regulate LIN-12/Notch, and that CDK-8 kinase activity is required for this process. Lastly, I find that sur-2/MED23 and lin-25/MED24 are required for LIN-12/Notch ligand transcription, independent of the Cdk8 Kinase Module and Mediator Head and Tail components

Discovery of New Antibacterial Accramycins from a Genetic Variant of the Soil Bacterium, Streptomyces sp. MA37

Funding: F.M. is thankful to the University of the Philippines Faculty, Reps, and Staff Development Program (FRAS DP) for funding the doctoral studies. H.D. and K.K. are grateful for the financial support of the Leverhulme Trust-Royal Society Africa award (AA090088) and the jointly funded UK Medical Research Council–UK Department for International Development (MRC/DFID) Concordat Agreement African Research Leaders Award (MR/S00520X/1). Supplementary Materials: The following are available online at http://www.mdpi.com/2218-273X/10/10/1464/s1.Peer reviewedPublisher PD

Dynamically Grown Generative Adversarial Networks

Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator. It enjoys the benefits of both eased training because of progressive growing and improved performance because of broader architecture design space. Experimental results demonstrate new state-of-the-art of image generation. Observations in the search procedure also provide constructive insights into the GAN model design such as generator-discriminator balance and convolutional layer choices.Comment: Accepted to AAAI 202

Continuity of family of Calder\'on projections

We consider a continuous family of linear elliptic differential operators of arbitrary order over a smooth compact manifold with boundary. Assuming constant dimension of the spaces of inner solutions, we prove that the orthogonalized Calder\'on projections of the underlying family of elliptic operators form a continuous family of projections. Hence, its images (the Cauchy data spaces) form a continuous family of closed subspaces in the relevant Sobolev spaces. We use only elementary tools and classical results: basic manipulations of operator graphs and other closed subspaces in Banach spaces; elliptic regularity; Green's formula and trace theorems for Sobolev spaces; well-posed boundary conditions; duality of spaces and operators in Hilbert space; and the interpolation theorem for operators in Sobolev spaces. \keywords{Calder{\'o}n projection\and Cauchy data spaces \and Elliptic differential operators \and Green's formula\and Interpolation theorem\and Manifolds with boundary\and Parameter dependence \and Trace theorem \and Variational propertie

Highly-Accurate Electricity Load Estimation via Knowledge Aggregation

Mid-term and long-term electric energy demand prediction is essential for the planning and operations of the smart grid system. Mainly in countries where the power system operates in a deregulated environment. Traditional forecasting models fail to incorporate external knowledge while modern data-driven ignore the interpretation of the model, and the load series can be influenced by many complex factors making it difficult to cope with the highly unstable and nonlinear power load series. To address the forecasting problem, we propose a more accurate district level load prediction model Based on domain knowledge and the idea of decomposition and ensemble. Its main idea is three-fold: a) According to the non-stationary characteristics of load time series with obvious cyclicality and periodicity, decompose into series with actual economic meaning and then carry out load analysis and forecast. 2) Kernel Principal Component Analysis(KPCA) is applied to extract the principal components of the weather and calendar rule feature sets to realize data dimensionality reduction. 3) Give full play to the advantages of various models based on the domain knowledge and propose a hybrid model(XASXG) based on Autoregressive Integrated Moving Average model(ARIMA), support vector regression(SVR) and Extreme gradient boosting model(XGBoost). With such designs, it accurately forecasts the electricity demand in spite of their highly unstable characteristic. We compared our method with nine benchmark methods, including classical statistical models as well as state-of-the-art models based on machine learning, on the real time series of monthly electricity demand in four Chinese cities. The empirical study shows that the proposed hybrid model is superior to all competitors in terms of accuracy and prediction bias

Human Ecology, Process Philosophy and the Global Ecological Crisis

This paper argues that human ecology, based on process philosophy and challenging scientific materialism, is required to effectively confront the global ecological crisis now facing us