Deep Learning on Smart Meter Data: Non-Intrusive Load Monitoring and Stealthy Black-Box Attacks

Climate change and environmental concerns are instigating widespread changes in modern electricity sectors due to energy policy initiatives and advances in sustainable technologies. To raise awareness of sustainable energy usage and capitalize on advanced metering infrastructure (AMI), a novel deep learning non-intrusive load monitoring (NILM) model is proposed to disaggregate smart meter readings and identify the operation of individual appliances. This model can be used by Electric power utility (EPU) companies and third party entities, and then utilized to perform active or passive consumer power demand management. Although machine learning (ML) algorithms are powerful, these remain vulnerable to adversarial attacks. In this thesis, a novel stealthy black-box attack that targets NILM models is proposed. This work sheds light on both effectiveness and vulnerabilities of ML models in the smart grid context and provides valuable insights for maintaining security especially with increasing proliferation of artificial intelligence in the power system

Food Processing Degrees: Evidence from Beijing Household Survey

Food Consumption/Nutrition/Food Safety, Teaching/Communication/Extension/Profession,

Got (Safe) Milk? Chinese Consumers’ Valuation for Select Food Safety Attributes

Food safety issues often arise from problems of asymmetric information between consumers and suppliers of food with regards to product-specific attributes or characteristics. Food safety concerns in China are having a drastic impact on consumer behavior, commodity markets, international trade and food security. An additional challenge to the problem of asymmetric information lies in the inherent structure of the governing bodies which oversee food safety and quality. Unlike the United States and other developed countries, China’s food safety is regulated by several government entities with different and sometimes overlapping responsibilities. As a result consumers don’t have a comprehensive food safety and quality system on which to base their economic decisions. In an effort to maintain the food supply of the world’s largest economy safe, China’s government has approved a series of tougher food safety laws and regulations. Although publicized as a tough approach to food safety, it is unclear whether this latest effort will make China’s food safer to eat and improve the country’s image to its agricultural trading partners. While much attention has focused on the problems plaguing China’s food inspection system, little research has been dedicated to analyze consumers’ concerns over food safety. In this paper we measure consumer preferences for select food safety attributes in milk. More specifically we estimate consumer’s willingness to pay for government certification, an independent (third party) certification program, national brand, and a product’s shelf-life using a choice experiment approach. We compare and contrast several modeling strategies to capture heterogeneity of consumer preferences. The data used in this study was collected from a choice experiment administered in seven major metropolitan cities in China, yielding a statistical sample of 6,720 observations. Our results suggest that Chinese consumers have the highest willingness-to-pay for a government certification program, followed by national brand, private certification, and longer shelf-life products. We find that Chinese consumers are very concerned about the safety of the milk they purchase and are willing to pay a high premium to assure that their food is safe. The high level of concern regarding milk safety is linked to recent food safety incidents involving dairy products, most notably the Melamine-adulterated milk products. Heterogeneity of consumer preferences and willingness to pay for the select food safety attributes was found by implementing a latent class logit model based on attitudinal responses as well as a mixed logit model. Although it might appear that Chinese consumers’ confidence on the government is eroding, as reported in the wake of recent food safety scandals, our research found that consumers were less confident on non-government food safety control measures. This result indicates that there is a strong need for the Chinese government to provide adequate food safety and quality control. Our findings call upon the direct involvement of the Chinese government in the food safety system. A more strict monitoring system via certification is necessary. If realized, such government efforts will provide higher welfare to consumers in the short-run and will restore consumers’ trust increasing social welfare in the long run. Policy implications of our results are discussed with particular attention given to food safety and security issues.China, Choice experiment, Mixed logit, Latent class logit, Food safety, Preference heterogeneity, Willingness-to-pay, Food Consumption/Nutrition/Food Safety, International Relations/Trade, Marketing, Q11, Q18,

Plasmonic and metamaterial biosensors: A game-changer for virus detection

One of the most important processes in the fight against current and future pandemics is the rapid diagnosis and initiation of treatment of viruses in humans. In these times, the development of high-sensitivity tests and diagnostic kits is an important research area. Plasmonic platforms, which control light in subwavelength volumes, have opened up exciting prospects for biosensing applications. Their significant sensitivity and selectivity allow for the non-invasive and rapid detection of viruses. In particular, plasmonic-assisted virus detection platforms can be achieved by various approaches, including propagating surface and localized plasmon resonances, as well as surface-enhanced Raman spectroscopy. In this review, we discuss both the fundamental principles governing a plasmonic biosensor and prospects for achieving improved sensor performance. We highlight several nanostructure schemes to combat virus-related diseases. We also examine technological limitations and challenges of plasmonic-based biosensing, such as reducing the overall cost and handling of complex biological samples. Finally, we provide a future prospective for opportunities to improve plasmonic-based approaches to increase their impact on global health issues.Comment: 1

Complex Graph Laplacian Regularizer for Inferencing Grid States

In order to maintain stable grid operations, system monitoring and control processes require the computation of grid states (e.g. voltage magnitude and angles) at high granularity. It is necessary to infer these grid states from measurements generated by a limited number of sensors like phasor measurement units (PMUs) that can be subjected to delays and losses due to channel artefacts, and/or adversarial attacks (e.g. denial of service, jamming, etc.). We propose a novel graph signal processing (GSP) based algorithm to interpolate states of the entire grid from observations of a small number of grid measurements. It is a two-stage process, where first an underlying Hermitian graph is learnt empirically from existing grid datasets. Then, the graph is used to interpolate missing grid signal samples in linear time. With our proposal, we can effectively reconstruct grid signals with significantly smaller number of observations when compared to existing traditional approaches (e.g. state estimation). In contrast to existing GSP approaches, we do not require knowledge of the underlying grid structure and parameters and are able to guarantee fast spectral optimization. We demonstrate the computational efficacy and accuracy of our proposal via practical studies conducted on the IEEE 118 bus system

Demethylation of the miR-146a promoter by 5-Aza-2’-deoxycytidine correlates with delayed progression of castration-resistant prostate cancer

BACKGROUND: Androgen deprivation therapy is the primary strategy for the treatment of advanced prostate cancer; however, after an initial regression, most patients will inevitably develop a fatal androgen-independent tumor. Therefore, understanding the mechanisms of the transition to androgen independence prostate cancer is critical to identify new ways to treat older patients who are ineligible for conventional chemotherapy. METHODS: The effects of 5-Aza-2’-deoxycytidine (5-Aza-CdR) on the viability and the apoptosis of the androgen-dependent (LNCaP) and androgen-independent (PC3) cell lines were examined by MTS assay and western blot analysis for the activation of caspase-3. The subcutaneous LNCaP xenografts were established in a nude mice model. MiR-146a and DNMTs expressions were analyzed by qRT-PCR and DNA methylation rates of LINE-1 were measured by COBRA-IRS to determine the global DNA methylation levels. The methylation levels of miR-146a promoter region in the different groups were quantified by the bisulfite sequencing PCR (BSP) assay. RESULTS: We validated that 5-Aza-CdR induced cell death and increased miR-146a expression in both LNCaP and PC3 cells. Notably, the expression of miR-146a in LNCaP cells was much higher than in PC3 cells. MiR-146a inhibitor was shown to suppress apoptosis in 5-Aza-CdR-treated cells. In a castrate mouse LNCaP xenograft model, 5-Aza-CdR significantly suppressed the tumors growth and also inhibited prostate cancer progression. Meanwhile, miR-146a expression was significantly enhanced in the tumor xenografts of 5-Aza-CdR-treated mice and the androgen-dependent but not the androgen-independent stage of castrated mice. In particular, the expression of miR-146a was significantly augmented in both stages of the combined treatment (castration and 5-Aza-CdR). Additionally, the methylation percentage of the two CpG sites (−444 bp and −433 bp), which were around the NF-κB binding site at miR-146a promoter, showed the lowest methylation levels among all CpG sites in the combined treatment tumors of both stages. CONCLUSION: Up-regulating miR-146a expression via the hypomethylation of the miR-146a promoter by 5-Aza-CdR was correlated with delayed progression of castration-resistant prostate cancers. Moreover, site-specific DNA methylation may play an important role in miR-146a expression in androgen-dependent prostate cancer progression to androgen-independent prostate cancer and therefore provides a potentially useful biomarker for assessing drug efficacy in prostate cancer

Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index SPI . We demonstrate model application by four stations in the Haihe river basin, China. The random-forest-RF-based forecast model has consistently shown better predictive skills than the ARIMA model for both long and short drought forecasting. The confidence intervals derived from the proposed model generally have good coverage, but still tend to be conservative to predict some extreme drought events

Adversarial Monte Carlo Denoising with Conditioned Auxiliary Feature Modulation

Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to achieve better rendering quality with less computational cost. However, most of these methods rely on handcrafted optimization objectives, which lead to artifacts such as blurs and unfaithful details. In this paper, we present an adversarial approach for denoising Monte Carlo rendering. Our key insight is that generative adversarial networks can help denoiser networks to produce more realistic high-frequency details and global illumination by learning the distribution from a set of high-quality Monte Carlo path tracing images. We also adapt a novel feature modulation method to utilize auxiliary features better, including normal, albedo and depth. Compared to previous state-of-the-art methods, our approach produces a better reconstruction of the Monte Carlo integral from a few samples, performs more robustly at different sample rates, and takes only a second for megapixel images