13 research outputs found
Centralized active reconfigurable intelligent surface: Architecture, path loss analysis and experimental verification
Reconfigurable intelligent surfaces (RISs) are promising candidate for the 6G
communication. Recently, active RIS has been proposed to compensate the
multiplicative fading effect inherent in passive RISs. However, conventional
distributed active RISs, with at least one amplifier per element, are costly,
complex, and power-intensive. To address these challenges, this paper proposes
a novel architecture of active RIS: the centralized active RIS (CA-RIS), which
amplifies the energy using a centralized amplifying reflector to reduce the
number of amplifiers. Under this architecture, only as low as one amplifier is
needed for power amplification of the entire array, which can eliminate the
mutual-coupling effect among amplifiers, and significantly reduce the cost,
noise level, and power consumption. We evaluate the performance of CA-RIS,
specifically its path loss, and compare it with conventional passive RISs,
revealing a moderate amplification gain. Furthermore, the proposed CA-RIS and
the path loss model are experimentally verified, achieving a 9.6 dB net gain
over passive RIS at 4 GHz. The CA-RIS offers a substantial simplification of
active RIS architecture while preserving performance, striking an optimal
balance between system complexity and the performance, which is competitive in
various scenarios
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs
While physics-informed neural networks (PINNs) have been proven effective for
low-dimensional partial differential equations (PDEs), the computational cost
remains a hurdle in high-dimensional scenarios. This is particularly pronounced
when computing high-order and high-dimensional derivatives in the
physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian
noise for stochastic smoothing of the original neural net model, enabling Monte
Carlo methods for derivative approximation, eliminating the need for costly
auto-differentiation. Despite its computational efficiency in high dimensions,
RS-PINN introduces biases in both loss and gradients, negatively impacting
convergence, especially when coupled with stochastic gradient descent (SGD). We
present a comprehensive analysis of biases in RS-PINN, attributing them to the
nonlinearity of the Mean Squared Error (MSE) loss and the PDE nonlinearity. We
propose tailored bias correction techniques based on the order of PDE
nonlinearity. The unbiased RS-PINN allows for a detailed examination of its
pros and cons compared to the biased version. Specifically, the biased version
has a lower variance and runs faster than the unbiased version, but it is less
accurate due to the bias. To optimize the bias-variance trade-off, we combine
the two approaches in a hybrid method that balances the rapid convergence of
the biased version with the high accuracy of the unbiased version. In addition,
we present an enhanced implementation of RS-PINN. Extensive experiments on
diverse high-dimensional PDEs, including Fokker-Planck, HJB, viscous Burgers',
Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off
and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are
provided for selecting biased, unbiased, or hybrid versions, depending on the
dimensionality and nonlinearity of the specific PDE problem.Comment: 21 pages, 5 figure
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation
Due to its robust and precise distance measurements, LiDAR plays an important
role in scene understanding for autonomous driving. Training deep neural
networks (DNNs) on LiDAR data requires large-scale point-wise annotations,
which are time-consuming and expensive to obtain. Instead, simulation-to-real
domain adaptation (SRDA) trains a DNN using unlimited synthetic data with
automatically generated labels and transfers the learned model to real
scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly
employ a multi-stage pipeline and focus on feature-level alignment. They
require prior knowledge of real-world statistics and ignore the pixel-level
dropout noise gap and the spatial feature gap between different domains. In
this paper, we propose a novel end-to-end framework, named ePointDA, to address
the above issues. Specifically, ePointDA consists of three modules:
self-supervised dropout noise rendering, statistics-invariant and
spatially-adaptive feature alignment, and transferable segmentation learning.
The joint optimization enables ePointDA to bridge the domain shift at the
pixel-level by explicitly rendering dropout noise for synthetic LiDAR and at
the feature-level by spatially aligning the features between different domains,
without requiring the real-world statistics. Extensive experiments adapting
from synthetic GTA-LiDAR to real KITTI and SemanticKITTI demonstrate the
superiority of ePointDA for LiDAR point cloud segmentation.Comment: Accepted by AAAI 202
Reciprocity Among Different Groups in Society
Reciprocity is a behavior which makes human society more harmonious. It is also a common concept in behavioral economics. There are many factors can influence trust and reciprocity between people. In this study, we utilized some previous experiments’ results done by predecessors to expect the relationship between gender and reciprocity in certain age group-university students (Teenagers). Combining with game theory, particularly the investment game, our research will exhibit the likelihood of being trusted and trustworthiness level between men and women when they make decision. The overview of this essay comprises four sections: The introduction of reciprocity, the literature review of two articles about age and gender respectively, the experiment design and the conclusion. Our methodology mainly based on the improvement of double blind trials and the hypothesis is: The trust between the same gender is easier to achieve compared to different gender. Furthermore, the final part of this essay will analyze the improvement and suggest some recommendations
A prognostic model of idiopathic pulmonary fibrosis constructed based on macrophage and mitochondria-related genes
Abstract Background Studies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the understanding of the interactions and specific mechanisms between mitochondrial function and macrophages in pulmonary fibrosis is still very limited. Methods To construct a prognostic model for IPF based on Macrophage- related genes (MaRGs) and Mitochondria-related genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the signature genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve key genes. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of key genes.Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups. Results There were 4791 DEGs between IPF and Control groups. Furthermore, 26 signature genes were achieved by the intersection processing. Three key genes including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the key genes was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that key genes were associated with classical pathways including ‘Apoptosis Signaling’, ‘PI3K/AKT Signaling’, and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2. Conclusion We achieved 3 key genes including ALDH2, MCL1,, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF
Dexamethasone distribution characteristic following controllable continuous sub-tenon drug delivery in rabbit
Drug delivery systems are required to be safe, minimally invasive and effectively delivery drug to the target tissues. But delivery drugs to the eye has not yet satisfied this need. Here, we focused on examining the distribution of dexamethasone (DEX) in ocular and plasmic samples following controllable continuous sub-Tenon drug delivery (CCSDD) of dexamethasone disodium phosphate (DEXP) in rabbit, and to compare that with two traditional routes: subconjunctival injection and intravenous injection. The DEX concentration was analyzed by Shimadzu LC–MS 2010 system. In CCSDD group, during observed 24 h, the mean DEX level in collected samples from highest to lowest following in order: sclera, cornea, retina/choroid, iris, plasma, aqueous humor, lens and vitreous body. In ocular solid tissue, the DEX level in posterior segment is higher than in anatomic corresponding anterior segment, but it is opposite in ocular fluid tissue. High levels of DEX were maintained at 12 h in the ocular tissue immediately after the administration. Even at 24 h, the mean DEX concentration was 31.72 ng/ml and 22.40 ng/ml in aqueous and vitreous, respectively. In CCSDD group, the ocular DEX exposure (AUC0-24) is much higher and plasma exposure is much less than IV group, and it is also similar in SC group except iris. The amount of DEX levels are markedly increased in ocular tissues but it yield lower plasma levels indicating reduction of systemic absorption by CCSDD. Thus, CCSDD is an effective method of delivering DEX into anterior and posterior segment of the eye