Beamforming and Power Splitting Designs for AN-aided Secure Multi-user MIMO SWIPT Systems

In this paper, an energy harvesting scheme for a multi-user multiple-input-multiple-output (MIMO) secrecy channel with artificial noise (AN) transmission is investigated. Joint optimization of the transmit beamforming matrix, the AN covariance matrix, and the power splitting ratio is conducted to minimize the transmit power under the target secrecy rate, the total transmit power, and the harvested energy constraints. The original problem is shown to be non-convex, which is tackled by a two-layer decomposition approach. The inner layer problem is solved through semi-definite relaxation, and the outer problem, on the other hand, is shown to be a single- variable optimization that can be solved by one-dimensional (1- D) line search. To reduce computational complexity, a sequential parametric convex approximation (SPCA) method is proposed to find a near-optimal solution. The work is then extended to the imperfect channel state information case with norm-bounded channel errors. Furthermore, tightness of the relaxation for the proposed schemes are validated by showing that the optimal solution of the relaxed problem is rank-one. Simulation results demonstrate that the proposed SPCA method achieves the same performance as the scheme based on 1-D but with much lower complexity.Comment: 12 pages, 6 figures, submitted for possible publicatio

Inhibiting toll-like receptor 4 signaling ameliorates pulmonary fibrosis during acute lung injury induced by lipopolysaccharide: an experimental study

<p>Abstract</p> <p>Background</p> <p>Toll-like receptor 4 (TLR4) is essential in lipopolysaccharide (LPS)-induced fibroblast activation and collagen secretion in vitro. However, its effects on the process of lung fibroblast activation and fibrosis initiation during LPS induced acute lung injury (ALI) remain unknown. The goal of the present study was to determine the effect of inhibiting TLR4 on LPS-induced ALI and fibrosis in vivo.</p> <p>Methods</p> <p>The ALI model was established by intraperitoneal injection of LPS in mice. TLR4-small hairpin RNA (shRNA) lentivirus was injected intravenously into the mice to inhibit TLR4 expression. mRNA and protein levels were detected by real-time PCR and Western-blot analysis, respectively. The contents of the C-terminal propeptide of type I procollagen (PICP) in bronchoalveolar lavage fluid (BALF) were detected by ELISA, and the degree of fibrosis was detected by van Gieson collagen staining, the hydroxyproline assay, and alpha smooth muscle actin (α-SMA) immunohistochemical staining.</p> <p>Results</p> <p>Overexpression of TLR4, type I procollagen, alpha-SMA, and p-AKT in murine pulmonary tissue after intraperitoneal injection of LPS at 72 hours and 28 days were detected. Moreover, the degree of fibrosis was shown to increase by ELISA analysis of PICP in BALF, van Gieson collagen staining, the hydroxyproline assay, and α-SMA immunohistochemical staining. All of these changes were alleviated by intravenous infection with TLR4-shRNA lentivirus.</p> <p>Conclusions</p> <p>Inhibiting TLR4 signaling could ameliorate fibrosis at the early stage of ALI induced by LPS.</p

Blooms of the woloszynskioid dinoflagellate Tovellia diexiensis sp nov (Dinophyceae) in Baishihai Lake at the eastern edge of Tibetan Plateau

Freshwater red tides due to dinoflagellates have caused spectacular and regular &quot;summer reddening&quot; in recent years in Baishihai Lake, a temperate, meromictic, meso- or oligotrophic, high-altitude, landslide-dammed, deep lake located at the eastern edge of Tibetan Plateau in China. Based on morphological and molecular analyses, the causative organism has been identified as a new woloszynskioid dinoflagellate, Tovellia diexiensis Q. Zhang et G.X. Liu sp. nov. The vegetative cells are 20-32 mu m long and 16-24 mu m wide. They have a hemispherical episome and a broadly rounded hyposome with a short characteristic antapical spine. Usually cells are bright red due to the presence of numerous red-pigmented bodies, which often masked the yellowish green discoid chloroplasts. The amphiesma of motile cells comprise mainly quadrilateral, pentagonal or hexagonal thin plates, arranged in 4-5 latitudinal series on the episome, 1 in the cingulum and 4 on, the hyposome. Molecular phylogenies based on small subunit ribosomal DNA and large subunit ribosomal DNA (LSU) indicate T diexiensis from Baishihai Lake to belong to the family Tovelliaceae, which was monophyletic in our LSU phylogenies. During the bloom-forming period in 2005, cell density of T diexiensis reached 9.15 x 10(5) cells L-1. Astaxanthin and its diester were found to be the major pigments in T diexiensis, resulting in a characteristic blood-red color of the water in Baishihai Lake.</p

Simple and Asymmetric Graph Contrastive Learning without Augmentations

Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefabricated graph augmentation and homophily assumptions. Thus, they fail to generalize well to heterophilic graphs where connected nodes may have different class labels and dissimilar features. In this paper, we study the problem of conducting contrastive learning on homophilic and heterophilic graphs. We find that we can achieve promising performance simply by considering an asymmetric view of the neighboring nodes. The resulting simple algorithm, Asymmetric Contrastive Learning for Graphs (GraphACL), is easy to implement and does not rely on graph augmentations and homophily assumptions. We provide theoretical and empirical evidence that GraphACL can capture one-hop local neighborhood information and two-hop monophily similarity, which are both important for modeling heterophilic graphs. Experimental results show that the simple GraphACL significantly outperforms state-of-the-art graph contrastive learning and self-supervised learning methods on homophilic and heterophilic graphs. The code of GraphACL is available at https://github.com/tengxiao1/GraphACL.Comment: Accepted to NeurIPS 202

Embedding Power Line Communication in Photovoltaic Optimizer by Modulating Data in Power Control Loop

In Photovoltaic (PV) system, dc-dc power optimizer (DCPO) is an option to maximize output power. At the same time, data links among DCPOs are often required for system monitoring and controlling. This paper proposes a novel power line communication (PLC) method for the DCPOs, in which the data of a DCPO is modulated into the control loop of power converter, and then transmitted through the series-connected dc power line to other DCPOs. In the process of communication, differential phase shift keying (DPSK) modulation and discrete Fourier transformation (DFT) demodulation are employed. To analyze the quality of communication, the communication model of the system is built, based on small-signal model. Furthermore, the noises of the system, including switching, maximum power point tracking (MPPT) and additive white Gaussian noise (AWGN), are discussed and measured to evaluate the signal-to-noise ratio (SNR). At last, an experimental system including 6 DCPOs is established and tested, which verifies the feasibility and effectiveness of the proposed method

Diffusion on the Probability Simplex

Diffusion models learn to reverse the progressive noising of a data distribution to create a generative model. However, the desired continuous nature of the noising process can be at odds with discrete data. To deal with this tension between continuous and discrete objects, we propose a method of performing diffusion on the probability simplex. Using the probability simplex naturally creates an interpretation where points correspond to categorical probability distributions. Our method uses the softmax function applied to an Ornstein-Unlenbeck Process, a well-known stochastic differential equation. We find that our methodology also naturally extends to include diffusion on the unit cube which has applications for bounded image generation

Wireless Powered Sensor Networks for Internet of Things: Maximum Throughput and Optimal Power Allocation

This paper investigates a wireless powered sensor network (WPSN), where multiple sensor nodes are deployed to monitor a certain external environment. A multi-antenna power station (PS) provides the power to these sensor nodes during wireless energy transfer (WET) phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center (FC) during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, the paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading based Stackelberg game, linear energy trading based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes