Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning

The current learning process of deep learning, regardless of any deep neural network (DNN) architecture and/or learning algorithm used, is essentially a single resolution training. We explore multiresolution learning and show that multiresolution learning can significantly improve robustness of DNN models for both 1D signal and 2D signal (image) prediction problems. We demonstrate this improvement in terms of both noise and adversarial robustness as well as with small training dataset size. Our results also suggest that it may not be necessary to trade standard accuracy for robustness with multiresolution learning, which is, interestingly, contrary to the observation obtained from the traditional single resolution learning setting

Empirical Tm modeling in the region of Guangxi

Abstract:This paper presents three strategies for modeling the regional empirical Tm (the weighted mean temperature of the atmosphere) to obtain more accurate determinations in a regional empirical model that is better adapted to the geographical and climatic characteristics of the applied area. The proposed models utilize data from four radiosonde stations in Guangxi, at Nanning, Guilin, Wuzhou and Baise, over an 11 month period (from Jan. to Nov. of 2011). The experimental results demonstrated the following: (1) there is no significant difference between monthly and annual regression results at each site; (2) it is more reasonable and feasible to use the proposed regional Hybrid model for the area far away from the radiosonde site; (3) from the analysis of the possible temperature conditions, the precision of the proposed regional Hybrid model is higher than that of the well-known Bevis formula and of some other existing models and can reach an accuracy within 1mm for the GPS-derived PWV estimates for the applied region

Identification of a New γ\gamma-ray-emitting narrow-line Seyfert 1 Galaxy, at Redshift ∼1\sim1

We report on the identification of a new $\gamma$-ray-emitting narrow-line Seyfert 1 (NLS1) galaxy, SDSS J122222.55+041315.7, which increases the number of known objects of this remarkable but rare type of active galactic nuclei (AGN) to seven. Its optical spectrum, obtained in the Sloan Digital Sky Survey-Baryon Oscillation Spectroscopic Survey, reveals a broad H $\beta$ emission line with a width (FWHM) of 1734$\pm$104 km s$^{-1}$. This, along with strong optical Fe II multiplets [$R_{4570}=0.9$] and a weak [O III] $\lambda 5007$ emission line, makes the object a typical NLS1. On the other hand, the source exhibits a high radio brightness temperature, rapid infrared variability, and a flat X-ray spectrum extending up to $\sim$200 keV. It is associated with a luminous $\gamma$-ray source detected significantly with {\it Fermi}/LAT. Correlated variability with other wavebands has not yet been tested. The spectral energy distribution can be well modelled by a one-zone leptonic jet model. This new member is by far the most distant $\gamma$-ray-emitting NLS1, at a redshift of $z=0.966$.Comment: 5 pages, published on MNRA

Mesoscopic Interactions and Species Coexistence in Evolutionary Game Dynamics of Cyclic Competitions

Date of Acceptance: 27/11/2014Peer reviewedPublisher PD

From Smart Meter Data to Pricing Intelligence -- Visual Data Mining towards Real-Time BI

The deployment of smart metering in the electricity industry has opened up the opportunity for real-time BI-enabled innovative business applications, such as demand response. Taking a holistic view of BI, this study introduced a visual data mining driven application in order to exemplify the potentials of real-time BI to the electricity businesses. The empirical findings indicate that such an application is capable of extracting actionable insights about customer’s electricity consumption patterns, which will lead to turn timely measured data into pricing intelligence. Based on the findings, we proposed a real-time BI framework, and discussed how it will facilitate the formulation of strategic initiatives for transforming the electricity utility towards sustainable growth. Our research is conducted by following the design science research paradigm. By addressing an emerging issue in the problem domain, it adds empirical knowledge to the BI research landscape

Documented Evidence of Agricultural Injury in China

Objective: To describe the documented evidence concerning agricultural injury in China and to identify topics for future research.Method: Literature search and review were conducted to collect publications that were relevant to agricultural injury in China. The process included defining agricultural injury for the purpose of this study, selecting articles according to inclusion criteria and extracting data from each paper. Descriptive methods were used to analyze the contents, research approaches, distribution of authors, and cooperation percentage of agricultural injury studies.Results: After applying the inclusion criteria, 89 articles were included in this study. The author collaboration percentage (number of articles with more than one author divided by number of total articles) and the institutional collaboration percentage (number of articles with more than one organization divided by number of total articles) among the 89 articles were 85.4% and 42.7%, respectively. Most of the authors are affiliated with a Center for Disease Control and Prevention (CDC) or an academic institution located in 10 of the 31 provinces in mainland China. Among the 89 articles, only 6 were on injuries related to agricultural work, the rest (83) dealt with injuries among rural residents with or without clarifying occupations or ongoing activities. Conclusions: Research on agricultural injuries in China is currently in its early stage. More research is needed to obtain evidence that can be used in policy making for agricultural injury control. Our study is the first to describe the documented evidence on agricultural injuries in China and identify topics for future research

The association of depression status with menopause symptoms among rural midlife women in China

Objective: This study aims to evaluate the association of depression with menopausal status and some menopause symptoms (vasomotor symptoms and poor sleep).Methods: A total of 743 participants aged 40-60 years were recruited. Depression status was evaluated by using Self-Rating Depression Scale (SDS). Sleep quality and vasomotor symptoms were evaluated by specific symptoms questionnaire.Results: The prevalence of depression among participants was 11.4%. Depression was found more likely to occur in participants with poor sleep (OR, 6.02; 95%CI, 3.61, 10.03) or with vasomotor symptoms (VMS) (OR, 2.03; 95%CI, 1.20, 3.44) after controlling for age, education level, marital status, menopause status, monthly family income and chronic diseases. Menopause status was not associated with depression. Stratification analysis showed a significant association between poor sleep and depression across different menopause stages, while VMS were associated with depression only in premenopausal status.Conclusion: The majority of Chinese rural midlife women do not experience depression. The relationship between depression, VMS and sleep disturbances tends to change with menopausal status in Chinese rural midlife women.Keywords: depression, poor sleep, vasomotor symptoms, menopause, rural wome

Down-regulation of NRIP1 alleviates pyroptosis in human lens epithelial cells exposed to hydrogen peroxide by inhibiting NF-κB activation

Purpose: To investigate the role of nuclear receptor-interacting protein 1 (NRIP1) in oxidative stressinduced apoptosis and pyroptosis in cataract disease.Methods: Human lens epithelial cells (HLE-B3 cells) were exposed to hydrogen peroxide (H2O2). NRIP1 expression in hydrogen peroxide (H2O2)-treated HLE-B3 cells was determined by western blotting and quantitative reverse transcription polymerase chain reaction (qRT-PCR). CCK8 and EdU staining were used to assess cell viability. Flow cytometry and western blotting were used to assess pyroptosis.Results: NRIP1 was significantly up-regulated in HLE-B3 cells post-H2O2 incubation (p < 0.01). Hydrogen peroxide incubation reduced cell viability and proliferation of HLE-B3 cells, while NRIP1 knockdown enhanced cell viability and proliferation. NRIP1 silencing attenuated the H2O2-induced increase in NLRP3, N-terminal domain of gasdermin D, caspase-1, interleukin (IL)-1β, and IL-18 in HLEB3 cells, but suppressed the pyroptosis of H2O2-treated HLE-B3 cells. Hydrogen peroxide incubation down-regulated protein expression of cytoplasmic NF-κB and up-regulated nuclear NF-κB, while the expression of cytoplasmic NF-κB was increased and nuclear NF-κB was decreased in HLE-B3 cells by HLE-B3 interference.Conclusion: NRIP1 down-regulation represses apoptosis and pyroptosis of H2O2-treated human lens epithelial cells by inhibiting NF-κB activation, thus, providing a potential strategy to treat cataract disease

Bioinspired and bristled microparticles for ultrasensitive pressure and strain sensors

Biological sensory organelles are often structurally optimized for high sensitivity. Tactile hairs or bristles are ubiquitous mechanosensory organelles in insects. The bristle features a tapering spine that not only serves as a lever arm to promote signal transduction, but also a clever design to protect it from mechanical breaking. A hierarchical distribution over the body further improves the signal detection from all directions. We mimic these features by using synthetic zinc oxide microparticles, each having spherically-distributed, high-aspect-ratio, and high-density nanostructured spines resembling biological bristles. Sensors based on thin films assembled from these microparticles achieve static-pressure detection down to 0.015 Pa, sensitivity up to 121 kPa−1, and a strain gauge factor \u3e104, showing supreme overall performance. Other properties including a robust cyclability \u3e2000, fast response time ~7 ms, and low-temperature synthesis compatible to various integrations further indicate the potential of this sensor technology in applying to wearable technologies and human interfaces