Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO

This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve the problem using the sparse Bayesian learning framework which leads to a non-convex optimization problem. We offer two solution techniques to the problem based on alternating minimization and singular value decomposition. Our simulation results illustrate the superior performance of our methods in terms of accuracy and run time compared with the existing works

Bayesian Algorithms for Kronecker-structured Sparse Vector Recovery With Application to IRS-MIMO Channel Estimation

We study the sparse recovery problem with an underdetermined linear system characterized by a Kronecker-structured dictionary and a Kronecker-supported sparse vector. We cast this problem into the sparse Bayesian learning (SBL) framework and rely on the expectation-maximization method for a solution. To this end, we model the Kronecker-structured support with a hierarchical Gaussian prior distribution parameterized by a Kronecker-structured hyperparameter, leading to a non-convex optimization problem. The optimization problem is solved using the alternating minimization (AM) method and a singular value decomposition (SVD)-based method, resulting in two algorithms. Further, we analytically guarantee that the AM-based method converges to the stationary point of the SBL cost function. The SVD-based method, though it adopts approximations, is empirically shown to be more efficient and accurate. We then apply our algorithm to estimate the uplink wireless channel in an intelligent reflecting surface-aided MIMO system and extend the AM-based algorithm to address block sparsity in the channel. We also study the SBL cost to show that the minima of the cost function are achieved at sparse solutions and that incorporating the Kronecker structure reduces the number of local minima of the SBL cost function. Our numerical results demonstrate the effectiveness of our algorithms compared to the state-of-the-art

Octreotide ameliorates hypoxia/reoxygenation-induced cerebral infarction by inhibiting oxidative stress, inflammation and apoptosis, and via inhibition of TLR4/MyD88/NF-κB signaling pathway

Purpose: To explore the effects of octreotide (OCT) on oxidative stress, inflammation and apoptosis in hypoxia/reoxygenation (H/R)-induced cerebral infarction.Methods: The in vitro model of cerebral infarction was established by treating N2A cells with hypoxia for 4 h and reoxygenation for 24 h. The viability of N2A cells was determined by CCK-8 assay. The cells were divided into 3 groups: control group, H/R group, and H/R+OCT group. The cells in H/R+OCT group were pretreated with OCT (60 ng/mL) before H/R treatment. The oxidative stress of N2A cells were assessed by determining the levels of superoxide dismutase (SOD), glutathione peroxidase (GSHPx), catalase (CAT), reactive oxygen species (ROS) and malondialdehyde (MDA). Inflammation of N2A cells was evaluated by evaluating the levels of TNF-α, IL-1β, IL-6, and IL-8, while the apoptosis of N2A cells was assessed by flow cytometry. Western blot analysis was used to determine the expression of Bcl-2, Bax, TLR4, MyD88, and NF-κB.Results: Octreotide treatment significantly reduced the level of oxidative stress. The inflammation of N2A cells caused by hypoxia/reoxygenation was inhibited by treatment with octreotide. Apoptosis of N2A cells was also inhibited by octreotide treatment. Hypoxia/reoxygenation activated TLR4/MyD88/NF-κB signaling pathway, while octreotide inhibits the activation of this pathway.Conclusion: The results reveal that octreotide inhibits hypoxia/reoxygenation-induced oxidative stress,as well as the inflammation, and apoptosis of N2A cells by inhibiting TLR4/MyD88/NF-κB signaling pathway. Thus, these findings may provide new insights into the treatment of cerebral infarction

Venlafaxine inhibits neuronal apoptosis in a depression rat model via ERK1/ERK2 pathway

Purpose: To investigate the effects and mechanism of action of venlafaxine on neuronal apoptosis of depressed rats. Methods: Rats were randomly divided into normal control (NC) group, depressed rats (depression) group or venlafaxine-treatment group. Changes in body weight and sucrose preference ratio were recorded and behaviors in open field test (OFT) were observed in each group. Pathological changes in and the apoptosis rate of the cerebral neurons, and the activity of extracellular signal-regulated kinase 1 (ERK1)/ERK2 pathway were observed under a microscope. Results: At weeks 2 and 4, the body weight and water consumption of rats in depression group dropped below those of rats in NC group. On the other hand, at week 2, the body weight and water consumption of rats in venlafaxine-treatment group were significantly higher than those of rats in depression group (p < 0.05). Besides, depression group had randomly arranged neuron cells and a thinner cell layer, while venlafaxine-treatment group had a relatively regular hippocampal neural cell arrangement and a thicker cell layer. Moreover, cell apoptosis rate was higher in depression group than in that NC group, and lower in venlafaxine-treatment group than that in depression group (p < 0.05). Finally, the protein expressions of phosphorylated (p)-ERK1 and p-ERK2 were significantly higher in depression group than those in NC group (p<0.05), and distinctly lower in venlafaxine-treatment group than those in depression group (p <0.05). Conclusion: By suppressing the activity of ERK1/ERK2 pathway, venlafaxine relieves the symptoms of depression and repairs neuronal injuries in rats, thereby suppressing neuronal apoptosis. Thus, these findings provide a novel approach for the development of new antidepressants

Truncated Total Least Squares Method with a Practical Truncation Parameter Choice Scheme for Bioluminescence Tomography Inverse Problem

In bioluminescence tomography (BLT), reconstruction of internal bioluminescent source distribution from the surface optical signals is an ill-posed inverse problem. In real BLT experiment, apart from the measurement noise, the system errors caused by geometry mismatch, numerical discretization, and optical modeling approximations are also inevitable, which may lead to large errors in the reconstruction results. Most regularization techniques such as Tikhonov method only consider measurement noise, whereas the influences of system errors have not been investigated. In this paper, the truncated total least squares method (TTLS) is introduced into BLT reconstruction, in which both system errors and measurement noise are taken into account. Based on the modified generalized cross validation (MGCV) criterion and residual error minimization, a practical parameter-choice scheme referred to as improved GCV (IGCV) is proposed for TTLS. Numerical simulations with different noise levels and physical experiments demonstrate the effectiveness and potential of TTLS combined with IGCV for solving the BLT inverse problem

Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation

This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to emphasize semantic regions in weakly supervised semantic segmentation. MCC adroitly incorporates concepts from masked image modeling and contrastive learning to devise Transformer blocks that induce keys to contract towards semantically pertinent regions. Unlike prevalent techniques that directly eradicate patch regions in the input image when generating masks, we scrutinize the neighborhood relations of patch tokens by exploring masks considering keys on the affinity matrix. Moreover, we generate positive and negative samples in contrastive learning by utilizing the masked local output and contrasting it with the global output. Elaborate experiments on commonly employed datasets evidences that the proposed MCC mechanism effectively aligns global and local perspectives within the image, attaining impressive performance. The source code is available at \url{https://github.com/fwu11/MCC}

Influence of Temperature on Hydrolysis Acidification of Food Waste

AbstractFor two-phase anaerobic digestion process of food waste, degree of hydrolysis and products by acidification during hydrolysis and acidification phase directly affect the performance of methanogenesis phase. Temperature has great impact on hydrolysis and acidification of food waste. This paper monitored the dynamic change of biogas production, biogas composition, pH, soluble chemical oxygen demand (SCOD) and volatile fatty acids (VFAs) during hydrolysis and acidification stage so as to investigate specific influence of temperature on food waste. With the same inoculum and 9 days’ fermentation, three different temperatures (35, 55 and 70°C) were taken into consideration. The results showed that cumulative gas production was 4860mL at 70°C, which was 129.79% and 37.87% higher than that at 35 and 55°C. Besides, hydrogen content at 70°C was 45.34%, which was the highest among the three temperatures. Hydrolysis rate was proportional to the increase of temperature. Meanwhile, total VFAs yield and composition widely differed at three different temperatures. The hydrolysis and acidification products at 35°C were mainly ethanol and acetic acids and the highest concentrations of ethanol at 35°C were 3.28 and 3.65 times of that at 55 and 70°C, but more acetic, isobutyric and butyric acids were generated at 55 and 70°C. Among three temperatures, 70°C had the highest acetic acids concentration while 55°C had the highest isobutyric and butyric acids concentration

Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system

Background: For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. Methods: In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Results: Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and the two patients that emerged from a minimally conscious state achieved accuracies significantly greater than the chance level. Furthermore, P300 potentials and steady state visual evoked potentials were observed in the electroencephalography signals from the five patients. Conclusions: Number processing and arithmetic abilities as well as command following were demonstrated in the five patients. Furthermore, our results suggested that through brain-computer interface systems, many cognitive experiments may be conducted in patients with disorders of consciousness, although they cannot provide sufficient behavioral responses. © 2015 Li et al