Extreme eigenvalues of sample covariance matrices under generalized elliptical models with applications

We consider the extreme eigenvalues of the sample covariance matrix $Q=YY^*$ under the generalized elliptical model that $Y=\Sigma^{1/2}XD.$ Here $\Sigma$ is a bounded $p \times p$ positive definite deterministic matrix representing the population covariance structure, $X$ is a $p \times n$ random matrix containing either independent columns sampled from the unit sphere in $\mathbb{R}^p$ or i.i.d. centered entries with variance $n^{-1},$ and $D$ is a diagonal random matrix containing i.i.d. entries and independent of $X.$ Such a model finds important applications in statistics and machine learning. In this paper, assuming that $p$ and $n$ are comparably large, we prove that the extreme edge eigenvalues of $Q$ can have several types of distributions depending on $\Sigma$ and $D$ asymptotically. These distributions include: Gumbel, Fr\'echet, Weibull, Tracy-Widom, Gaussian and their mixtures. On the one hand, when the random variables in $D$ have unbounded support, the edge eigenvalues of $Q$ can have either Gumbel or Fr\'echet distribution depending on the tail decay property of $D.$ On the other hand, when the random variables in $D$ have bounded support, under some mild regularity assumptions on $\Sigma,$ the edge eigenvalues of $Q$ can exhibit Weibull, Tracy-Widom, Gaussian or their mixtures. Based on our theoretical results, we consider two important applications. First, we propose some statistics and procedure to detect and estimate the possible spikes for elliptically distributed data. Second, in the context of a factor model, by using the multiplier bootstrap procedure via selecting the weights in $D,$ we propose a new algorithm to infer and estimate the number of factors in the factor model. Numerical simulations also confirm the accuracy and powerfulness of our proposed methods and illustrate better performance compared to some existing methods in the literature.Comment: 90 pages, 6 figures, some typos are correcte

Scale-wise Convolution for Image Restoration

While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Naively applying those scale-invariant techniques (e.g. multi-scale testing, random-scale data augmentation) to image restoration tasks usually leads to inferior performance. In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. Inspired from spatial-wise convolution for shift-invariance, "scale-wise convolution" is proposed to convolve across multiple scales for scale-invariance. In our scale-wise convolutional network (SCN), we first map the input image to the feature space and then build a feature pyramid representation via bi-linear down-scaling progressively. The feature pyramid is then passed to a residual network with scale-wise convolutions. The proposed scale-wise convolution learns to dynamically activate and aggregate features from different input scales in each residual building block, in order to exploit contextual information on multiple scales. In experiments, we compare the restoration accuracy and parameter efficiency among our model and many different variants of multi-scale neural networks. The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal. Code and models are available at: https://github.com/ychfan/scn_srComment: AAAI 202

Benzene Metabolite, Hydroquinone, Activates the Aryl Hydrocarbon Receptor Pathway in Trophoblasts

Introduction: Benzene is the 6th most produced chemical in the world and a major pollutant that has been shown to have adverse effects on pregnancy. We previously showed that maternal benzene exposure during pregnancy induces maternal immune activation and also leads to an increase in fetal reabsorptions. The molecular mechanism in which benzene induces these negative effects is poorly understood. Here we developed a cellular model of benzene exposure to understand the molecular effect of benzene on trophoblast cells. Specifically, cells were exposed hydroquinone, a major benzene metabolite, in order to determine the molecular mechanism behind the observed maternal immune activation during benzene exposure. Method: Trophoblast cells (Sw.71) were exposed to 25μM of hydroquinone for 2, 4, 8, 16, 24 and 48 hours. Cells were collected at the time intervals specified above for RNA extraction and qPCR analysis. Results: Our data has shown that: 1) hydroquinone treatment activates AhR pathway as CYP1A1 is significantly induced in trophoblast cells; 2) hydroquinone treatment leads to inflammation in the trophoblast cells, which is shown as the significant increase of IL-6 and IL1- gene expression 24 hours after treatment; 3) hydroquinone treatment has a major impact on the ER stress that we reveal increased CHOP expression as early as 2 hours and in the later time points. Additionally, we see an initial increase in BIP. However, BIP is decreased at 24 hours of hydroquinone treatment. This expression pattern suggests that ER stress is being induced after hydroquinone treatment in a short time. 4) hydroquinone treatment induces interferon stimulated genes (ISGs) 24 hours after treatment, such as ISG20, Mx1. Conclusion: Our findings indicate that exposure to the major benzene metabolite, hydroquinone, induces activation of the AhR pathway in trophoblast cells. Activation of this pathway is known to lead to inflammation and ER stress. These processes were observed here by increased levels of inflammatory cytokines (IL-6 and IL1-) and transcription of genes associated with the unfolded protein response. Trophoblast cells exposed to hydroquinone also had higher levels of ISGs. The findings here suggest a potential molecular mechanism by which benzene and its metabolites exert detrimental effects on pregnancy

Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements

With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction methods encounter substantial difficulties when grappling with long texts, primarily due to the presence of redundant and irrelevant information, which impedes the model's capacity to capture pivotal insights from the text. To address this issue, we introduce a novel approach to long-text classification and prediction. Initially, we employ embedding techniques to condense the long texts, aiming to diminish the redundancy therein. Subsequently,the Bidirectional Encoder Representations from Transformers (BERT) embedding method is utilized for text classification training. Experimental outcomes indicate that our method realizes considerable performance enhancements in classifying long texts of Preferential Trade Agreements. Furthermore, the condensation of text through embedding methods not only augments prediction accuracy but also substantially reduces computational complexity. Overall, this paper presents a strategy for long-text prediction, offering a valuable reference for researchers and engineers in the natural language processing sphere.Comment: AI4TS Workshop@AAAI 2024 accepted publicatio

Species‐specific plant‐mediated effects between herbivores converge at high damage intensity

Plants are often exposed to multiple herbivores and densities of these attackers (or corresponding damage intensities) often fluctuate greatly in the field. Plant-mediated interactions vary among herbivore species and with changing feeding intensity, but little is known about how herbivore identity and density interact to determine plant responses and herbivore fitness. Here, we investigated this question using Triadica sebifera (tallow) and two common and abundant specialist insect herbivores, Bikasha collaris (flea beetle) and Heterapoderopsis bicallosicollis (weevil). By manipulating densities of leaf-feeding adults of these two herbivore species, we tested how variations in the intensity of leaf damage caused by flea beetle or weevil adults affected the performance of root-feeding flea beetle larvae and evaluated the potential of induced tallow root traits to predict flea beetle larval performance. We found that weevil adults consistently decreased the survival of flea beetle larvae with increasing leaf damage intensities. In contrast, conspecific flea beetle adults increased their larval survival at low damage then decreased larval survival at high damage, resulting in a unimodal pattern. Chemical analyses showed that increasing leaf damage from weevil adults linearly decreased root carbohydrates and increased root tannin, whereas flea beetle adults had opposite effects as weevil adults at low damage and similar effects as them at high damage. Furthermore, across all feeding treatments, flea beetle larval survival correlated positively with concentrations of carbohydrates and negatively with concentration of tannin, suggesting that root primary and secondary metabolism might underlie the observed effects on flea beetle larvae. Our study demonstrates that herbivore identity and density interact to determine systemic plant responses and plant-mediated effects on herbivores. In particular, effects are species-specific at low densities, but converge at high densities. These findings emphasize the importance of considering herbivore identity and density simultaneously when investigating factors driving plant-mediated interactions between herbivores, which advances our understanding of the structure and composition of herbivore communities and terrestrial food webs

PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment

Two-party computation (2PC) is promising to enable privacy-preserving deep learning (DL). However, the 2PC-based privacy-preserving DL implementation comes with high comparison protocol overhead from the non-linear operators. This work presents PASNet, a novel systematic framework that enables low latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by integrating the hardware latency of the cryptographic building block into the neural architecture search loss function. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) as a case study. The experimental results demonstrate that our light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms and 228 ms latency on private inference on ImageNet, which are 147 and 40 times faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and more than 1000 times higher energy efficiency.Comment: DAC 2023 accepeted publication, short version was published on AAAI 2023 workshop on DL-Hardware Co-Design for AI Acceleration: RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inferenc

Lifestyle interventions to prevent adverse pregnancy outcomes in women at high risk for gestational diabetes mellitus: a randomized controlled trial

ObjectiveTo examine the effects of lifestyle interventions, including dietary guidance, health education and weight management, on pregnancy outcomes in women at high risk of gestational diabetes mellitus (GDM).MethodsOur study included 251 women at high risk of GDM and 128 randomized to lifestyle interventions (dietary guidance, health education, and weight management); One hundred and twenty-three people were randomly assigned to a control group (regular pregnancy check-ups). Counts between groups were compared using either chi-square test or Fisher’s exact test.ResultsCompared with the control group, the risk of GDM was reduced by 46.9% (16.4% vs 30.9%, P = 0.007) and the risk of pregnancy induced hypertension (PIH) was reduced by 74.2% (2.3% vs 8.9%, P = 0.034) in the intervention group. There were no significant differences in macrosomia, cesarean section, or preterm birth (P >0.05).ConclusionThe lifestyle intervention in this study helped pregnant women to better understand knowledge related to pregnancy, reduce stress and anxiety, and increase intake of adequate prenatal nutrition. This intervention prevented metabolic abnormalities that may occur due to inadequate nutrient intake during pregnancy. In addition, it helped women to control weight gain, maintain appropriate weight gain during pregnancy, and reduce the risk of excessive or insufficient weight gain, ultimately lowering the incidence of GDM and PIH. This highlights the importance of early screening and intervention for high-risk pregnant women.Clinical Trial Registrationhttps://www.chictr.org.cn, identifier ChiCTR2300073766