Fractal Metamaterial Absorber with Three-Order Oblique Cross Dipole Slot Structure and its Application for In-band RCS Reduction of Array Antennas

To miniaturize the perfect metamaterial absorber, a fractal three-order oblique cross dipole slot structure is proposed and investigated in this paper. The fractal perfect metamaterial absorber (FPMA) consists of two metallic layers separated by a lossy dielectric substrate. The top layer etched a three-order oblique fractal-shaped cross dipole slot set in a square patch and the bottom one is a solid metal. The parametric study is performed for providing practical design guidelines. A prototype with a thickness of 0.0106λ (λ is the wavelength at 3.18 GHz) of the FPMA was designed, fabricated, measured, and is loaded on a 1×10 guidewave slot array antennas to reduce the in-band radar cross section (RCS) based on their surface current distribution. Experiments are carried out to verify the simulation results, and the experimental results show that the absorption at normal incidence is above 90% from 3.17 to 3.22GHz, the size for the absorber is 0.1λ×0.1λ, the three-order FPMA is miniaturized 60% compared with the zero-order ones, and the array antennas significantly obtain the RCS reduction without the radiation deterioration

A Survey of Document-Level Information Extraction

Document-level information extraction (IE) is a crucial task in natural language processing (NLP). This paper conducts a systematic review of recent document-level IE literature. In addition, we conduct a thorough error analysis with current state-of-the-art algorithms and identify their limitations as well as the remaining challenges for the task of document-level IE. According to our findings, labeling noises, entity coreference resolution, and lack of reasoning, severely affect the performance of document-level IE. The objective of this survey paper is to provide more insights and help NLP researchers to further enhance document-level IE performance

Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks

Backward propagation (BP) is widely used to compute the gradients in neural network training. However, it is hard to implement BP on edge devices due to the lack of hardware and software resources to support automatic differentiation. This has tremendously increased the design complexity and time-to-market of on-device training accelerators. This paper presents a completely BP-free framework that only requires forward propagation to train realistic neural networks. Our technical contributions are three-fold. Firstly, we present a tensor-compressed variance reduction approach to greatly improve the scalability of zeroth-order (ZO) optimization, making it feasible to handle a network size that is beyond the capability of previous ZO approaches. Secondly, we present a hybrid gradient evaluation approach to improve the efficiency of ZO training. Finally, we extend our BP-free training framework to physics-informed neural networks (PINNs) by proposing a sparse-grid approach to estimate the derivatives in the loss function without using BP. Our BP-free training only loses little accuracy on the MNIST dataset compared with standard first-order training. We also demonstrate successful results in training a PINN for solving a 20-dim Hamiltonian-Jacobi-Bellman PDE. This memory-efficient and BP-free approach may serve as a foundation for the near-future on-device training on many resource-constraint platforms (e.g., FPGA, ASIC, micro-controllers, and photonic chips)

Associations between plasma metal mixture exposure and risk of hypertension: A cross-sectional study among adults in Shenzhen, China

BackgroundMetal exposure affects human health. Current studies mainly focus on the individual health effect of metal exposure on hypertension (HTN), and the results remain controversial. Moreover, the studies assessing overall effect of metal mixtures on hypertension risk are limited.MethodsA cross-sectional study was conducted by recruiting 1,546 Chinese adults who attended routine medical check-ups at the Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen. The plasma levels of 13 metals were measured using inductively coupled plasma mass spectrometry. Multivariate logistic regression model, restricted cubic spline (RCS) model and the Bayesian Kernel Machine Regression (BKMR) model were applied to explore the single and combined effect of metals on the risk of HTN.ResultsA total of 642 (41.5%) participants were diagnosed with HTN. In the logistic regression model, the adjusted odds ratios (ORs) were 0.71 (0.52, 0.97) for cobalt, 1.40 (1.04, 1.89) for calcium, 0.66 (0.48, 0.90), and 0.60 (0.43, 0.83) for aluminum in the second and third quartile, respectively. The RCS analysis showed a V-shaped or an inverse V-shaped dose-response relationship between metals (aluminum or calcium, respectively) and the risk of HTN (P for non-linearity was 0.017 or 0.009, respectively). However, no combined effect was found between metal mixture and the risk of hypertension.ConclusionsPlasma levels of cobalt, aluminum and calcium were found to be associated with the risk of HTN. Further studies are needed to confirm our findings and their potential mechanisms with prospective studies and experimental study designs

Implications for Cation Selectivity and Evolution by a Novel Cation Diffusion Facilitator Family Member From the Moderate Halophile Planococcus dechangensis

In the cation diffusion facilitator (CDF) family, the transported substrates are confined to divalent metal ions, such as Zn2+, Fe2+, and Mn2+. However, this study identifies a novel CDF member designated MceT from the moderate halophile Planococcus dechangensis. MceT functions as a Na+(Li+, K+)/H+ antiporter, together with its capability of facilitated Zn2+ diffusion into cells, which have not been reported in any identified CDF transporters as yet. MceT is proposed to represent a novel CDF group, Na-CDF, which shares significantly distant phylogenetic relationship with three known CDF groups including Mn-CDF, Fe/Zn-CDF, and Zn-CDF. Variation of key function-related residues to “Y44-S48-Q150” in two structural motifs explains a significant discrimination in cation selectivity between Na-CDF group and three major known CDF groups. Functional analysis via site-directed mutagenesis confirms that MceT employs Q150, S158, and D184 for the function of MceT as a Na+(Li+, K+)/H+ antiporter, and retains D41, D154, and D184 for its facilitated Zn2+ diffusion into cells. These presented findings imply that MceT has evolved from its native CDF family function to a Na+/H+ antiporter in an evolutionary strategy of the substitution of key conserved residues to “Q150-S158-D184” motif. More importantly, the discovery of MceT contributes to a typical transporter model of CDF family with the unique structural motifs, which will be utilized to explore the cation-selective mechanisms of secondary transporters

Limb development genes underlie variation in human fingerprint patterns

Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized “pattern-block” correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning