Convergence of the tail probability for weighted sums of negatively orthant dependent random variables

summary:In this research, strong convergence properties of the tail probability for weighted sums of negatively orthant dependent random variables are discussed. Some sharp theorems for weighted sums of arrays of rowwise negatively orthant dependent random variables are established. These results not only extend the corresponding ones of Cai [4], Wang et al. [19] and Shen [13], but also improve them, respectively

Machine Learning for Predictive Deployment of UAVs with Multiple Access

In this paper, a machine learning based deployment framework of unmanned aerial vehicles (UAVs) is studied. In the considered model, UAVs are deployed as flying base stations (BS) to offload heavy traffic from ground BSs. Due to time-varying traffic distribution, a long short-term memory (LSTM) based prediction algorithm is introduced to predict the future cellular traffic. To predict the user service distribution, a KEG algorithm, which is a joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture model (GMM), is proposed for determining the service area of each UAV. Based on the predicted traffic, the optimal UAV positions are derived and three multi-access techniques are compared so as to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24\% of the total power consumption compared to the conventional method without traffic prediction. Besides, rate splitting multiple access (RSMA) has the lower required transmit power compared to frequency domain multiple access (FDMA) and time domain multiple access (TDMA)

Photometric and Spectroscopic Studies of V582 Lyr and V1016 Oph

We present new CCD photometric light curves about two eclipsing binaries of V582 Lyr and V1016 Oph. Our observations were carried out by the SARA 91.4 cm telescope of America in 2016 and the 60 cm telescope of Chile in 2018. V582 Lyr’s spectra type was classified as K5, and its radial velocity was determined using the LAMOST spectral survey. There are absorptions in the observed Hα line and excess emissions in the subtracted Hα line, which show weak chromospheric activity. We obtained the updated ephemeris information for V582 Lr and V1016 Oph, and found that their orbital periods are both decreasing.We concluded that the decreased rate is −0.474 (±0.011)Å~10−7 days yr−1 for V582 Lyr and 3.460 (±0.014)Å~10−7 days yr−1 for V1016 Oph. For V582 Lyr, the period variation was interpreted as a mass transfer from the secondary component to the primary one, and the corresponding rate is dM2/dt=−1.10 (±0.03)Å~10−7 Me yr−1. For V1016 Oph, we explain it by transferring from the primary component to the secondary one, and the corresponding rate is dM1/dt=−2.69 (±0.04)Å~10−7 Me yr−1. The photometric solution of V1016 Oph was obtained by analyzing the CCD photometry with the Wilson–Devinney program. We also obtained the orbital parameters of V1016 Oph by simultaneously analyzing our BVRI light curves and radial-velocity curve from the LAMOST low-resolution spectral survey. Finally, our orbital solution shows that they are contact eclipsing binaries with contact factors of 3.35 (±0.08)% for V582 Lyr and 41.0 (±0.1)% for V1016 Oph

EVNet: An Explainable Deep Network for Dimension Reduction

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the pool generalization performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called EVNet, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. EVNet starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained EVNet parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that EVNet consistently outperforms the state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC

Endophytic Beauveria bassiana promotes plant biomass growth and suppresses pathogen damage by directional recruitment

IntroductionEntomopathogenic fungi (EPF) can colonize and establish symbiotic relationships with plants as endophytes. Recently, EPF have been reported to suppress plant pathogens and induce plant resistance to diseases. However, the potential mechanisms via which EPF as endophytes control major plant diseases in situ remain largely unknown.MethodsPot and field experiments were conducted to investigate the mechanisms via which an EPF, Beauveria bassiana, colonizes tomato, under Botrytis cinerea infection stress. B. bassiana blastospores were inoculated into tomato plants by root irrigation. Tomato resistance to tomato gray mold caused by B. cinerea was evaluated by artificial inoculation, and B. bassiana colonization in plants and rhizosphere soil under B. cinerea infection stress was evaluated by colony counting and quantitative PCR. Furthermore, the expression levels of three disease resistance-related genes (OXO, CHI, and atpA) in tomato leaves were determined to explore the effect of B. bassiana colonization on plant disease resistance performance in pot experiments.ResultsB. bassiana colonization could improve resistance of tomato plants to gray mold caused by B. cinerea. The incidence rate, lesion diameter, and disease index of gray mold decreased in both the pot and field experiments following B. bassiana colonization. B. bassiana was more likely to accumulate in the pathogen infected leaves, while decreasing in the rhizosphere soil, and induced the expression of plant resistance genes, which were up-regulated in leaves.DiscussionThe results indicated that plants could “recruit” B. bassiana from rhizosphere soil to diseased plants as directional effects, which then enhanced plant growth and resistance against pathogens, consequently inhibiting pathogen infection and multiplication in plants. Our findings provide novel insights that enhance our understanding of the roles of EPF during pathogen challenge

Increased Activity Imbalance in Fronto-Subcortical Circuits in Adolescents with Major Depression

BACKGROUND: A functional discrepancy exists in adolescents between frontal and subcortical regions due to differential regional maturational trajectories. It remains unknown how this functional discrepancy alters and whether the influence from the subcortical to the frontal system plays a primacy role in medication naïve adolescent with major depressive disorder (MDD). METHODOLOGY/PRINCIPAL FINDINGS: Eighteen MDD and 18 healthy adolescents were enrolled. Depression and anxiety severity was assessed by the Short Mood and Feeling Questionnaire (SMFQ) and Screen for Child Anxiety Related Emotional Disorders (SCARED) respectively. The functional discrepancy was measured by the amplitude of low-frequency fluctuations (ALFF) of resting-state functional MRI signal. Correlation analysis was carried out between ALFF values and SMFQ and SCARED scores. Resting brain activity levels measured by ALFF was higher in the frontal cortex than that in the subcortical system involving mainly (para) limbic-striatal regions in both HC and MDD adolescents. The difference of ALFF values between frontal and subcortical systems was increased in MDD adolescents as compared with the controls. CONCLUSIONS/SIGNIFICANCE: The present study identified an increased imbalance of resting-state brain activity between the frontal cognitive control system and the (para) limbic-striatal emotional processing system in MDD adolescents. The findings may provide insights into the neural correlates of adolescent MDD

Machine Learning for Predictive Deployment of UAVs With Multiple Access

This paper presents a machine learning-based framework for the predictive deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to offload heavy traffic from ground BSs. To account for time-varying traffic distribution, a long short-term memory (LSTM)-based prediction algorithm is introduced to predict future cellular traffic. A joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture models (GMM) is proposed to determine the service area of each UAV based on the predicted user service distribution. Based on the predicted traffic, the optimal positions of UAVs are derived, and four multiple access techniques, namely, rate splitting multiple access (RSMA), frequency domain multiple access (FDMA), time domain multiple access (TDMA), and non-orthogonal multiple access (NOMA), are compared to minimize the total transmit power. Simulation results show that the proposed method can reduce up to 24% of the total power consumption compared to the conventional method without traffic prediction. Furthermore, RSMA is found to require the lowest transmit power among the four multiple access techniques. Therefore, this paper focuses on the comparison of multiple access techniques for UAV deployment, which is essential for the efficient and effective use of UAVs as flying BSs