Physics-guided Residual Learning for Probabilistic Power Flow Analysis

Probabilistic power flow (PPF) analysis is critical to power system operation and planning. PPF aims at obtaining probabilistic descriptions of the state of the system with stochastic power injections (e.g., renewable power generation and load demands). Given power injection samples, numerical methods repeatedly run classic power flow (PF) solvers to find the voltage phasors. However, the computational burden is heavy due to many PF simulations. Recently, many data-driven based PF solvers have been proposed due to the availability of sufficient measurements. This paper proposes a novel neural network (NN) framework which can accurately approximate the non-linear AC-PF equations. The trained NN works as a rapid PF solver, significantly reducing the heavy computational burden in classic PPF analysis. Inspired by residual learning, we develop a fully connected linear layer between the input and output in the multilayer perceptron (MLP). To improve the NN training convergence, we propose three schemes to initialize the NN weights of the shortcut connection layer based on the physical characteristics of AC-PF equations. Specifically, two model-based methods require the knowledge of system topology and line parameters, while the purely data-driven method can work without power grid parameters. Numerical tests on five benchmark systems show that our proposed approaches achieve higher accuracy in estimating voltage phasors than existing methods. In addition, three meticulously designed initialization schemes help the NN training process converge faster, which is appealing under limited training time.Comment: Probabilistic power flow, data-driven, residual learning, neural network, physics-guided initializatio

Performance of the Monitoring Light Source for the CMS Lead Tungstate Crystal Calorimeter

Light monitoring will play a crucial role in maintaining energy resolution for the CMS lead tungstate crystal calorimeter at LHC. In the last several years, a laser based monitoring light source was designed and constructed at Caltech, and was installed and commissioned at CERN. This paper presents the design of the CMS ECAL monitoring light source and its performance during beam tests. Issues related to the monitoring precision are discussed

Joint Mirror Procedure: Controlling False Discovery Rate for Identifying Simultaneous Signals

In many applications, identifying a single feature of interest requires testing the statistical significance of several hypotheses. Examples include mediation analysis which simultaneously examines the existence of the exposure-mediator and the mediator-outcome effects, and replicability analysis aiming to identify simultaneous signals that exhibit statistical significance across multiple independent experiments. In this work, we develop a novel procedure, named joint mirror (JM), to detect such features while controlling the false discovery rate (FDR) in finite samples. The JM procedure iteratively shrinks the rejection region based on partially revealed information until a conservative false discovery proportion (FDP) estimate is below the target FDR level. We propose an efficient algorithm to implement the method. Extensive simulations demonstrate that our procedure can control the modified FDR, a more stringent error measure than the conventional FDR, and provide power improvement in several settings. Our method is further illustrated through real-world applications in mediation and replicability analyses

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems

Inhibition of AKT2 Enhances Sensitivity to Gemcitabine via Regulating PUMA and NF-κB Signaling Pathway in Human Pancreatic Ductal Adenocarcinoma

Invasion, metastasis and resistance to conventional chemotherapeutic agents are obstacles to successful treatment of pancreatic cancer, and a better understanding of the molecular basis of this malignancy may lead to improved therapeutics. In the present study, we investigated whether AKT2 silencing sensitized pancreatic cancer L3.6pl, BxPC-3, PANC-1 and MIAPaCa-2 cells to gemcitabine via regulating PUMA (p53-upregulated modulator of apoptosis) and nuclear factor (NF)-κB signaling pathway. MTT, TUNEL, EMSA and NF-κB reporter assays were used to detect tumor cell proliferation, apoptosis and NF-κB activity. Western blotting was used to detect different protein levels. Xenograft of established tumors was used to evaluate primary tumor growth and apoptosis after treatment with gemcitabine alone or in combination with AKT2 siRNA. Gemcitabine activated AKT2 and NF-κB in MIAPaCa-2 and L3.6pl cells in vitro or in vivo, and in PANC-1 cells only in vivo. Gemcitabine only activated NF-κB in BxPC-3 cells in vitro. The presence of PUMA was necessary for gemcitabine-induced apoptosis only in BxPC-3 cells in vitro. AKT2 inhibition sensitized gemcitabine-induced apoptosis via PUMA upregulation in MIAPaCa-2 cells in vitro, and via NF-κB activity inhibition in L3.6pl cells in vitro. In PANC-1 and MIAPaCa-2 cells in vivo, AKT2 inhibition sensitized gemcitabine-induced apoptosis and growth inhibition via both PUMA upregulation and NF-κB inhibition. We suggest that AKT2 inhibition abrogates gemcitabine-induced activation of AKT2 and NF-κB, and promotes gemcitabine-induced PUMA upregulation, resulting in chemosensitization of pancreatic tumors to gemcitabine, which is probably an important strategy for the treatment of pancreatic cancer

UWSpeech: Speech to Speech Translation for Unwritten Languages

Existing speech to speech translation systems heavily rely on the text of target language: they usually translate source language either to target text and then synthesize target speech from text, or directly to target speech with target text for auxiliary training. However, those methods cannot be applied to unwritten target languages, which have no written text or phoneme available. In this paper, we develop a translation system for unwritten languages, named as UWSpeech, which converts target unwritten speech into discrete tokens with a converter, and then translates source-language speech into target discrete tokens with a translator, and finally synthesizes target speech from target discrete tokens with an inverter. We propose a method called XL-VAE, which enhances vector quantized variational autoencoder (VQ-VAE) with cross-lingual (XL) speech recognition, to train the converter and inverter of UWSpeech jointly. Experiments on Fisher Spanish-English conversation translation dataset show that UWSpeech outperforms direct translation and VQ-VAE baseline by about 16 and 10 BLEU points respectively, which demonstrate the advantages and potentials of UWSpeech

Implementation of a Software Feedback Control for the CMS Monitoring Lasers

Light monitoring will play a crucial role in maintaining energy resolution for the CMS lead tungstate crystal calorimeter in situ at LHC. Since 2003, a laser based monitoring system in its final design has been installed and used in beam tests at CERN. While the stability of the laser pulse energy and FWHM width, measured in 24 hours, is at 3% level, a long term degradation and a drift of the laser pulse center timing at 2 ns/day were observed. The degradation and drift were caused by the aging of the DC Kr lamp used to pump the Nd:YLF laser, and would affect the monitoring precision. This paper presents the design and implementation of a software feedback control which stabilizes laser pulse energy, width and timing by trimming the Nd:YLF laser pumping current. For laser runs lasted for more than 650 hours a stability of pulse energy and FWHM width at 3% level and a pulse timing jitter at 2 ns have been achieved when the laser pulse center timing is used as the feedback parameter