Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach

Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising technology for future wireless communications. The deployment of XL-MIMO, especially at high-frequency bands, leads to users being located in the near-field region instead of the conventional far-field. This letter proposes efficient model-based deep learning algorithms for estimating the near-field wireless channel of XL-MIMO communications. In particular, we first formulate the XL-MIMO near-field channel estimation task as a compressed sensing problem using the spatial gridding-based sparsifying dictionary, and then solve the resulting problem by applying the Learning Iterative Shrinkage and Thresholding Algorithm (LISTA). Due to the near-field characteristic, the spatial gridding-based sparsifying dictionary may result in low channel estimation accuracy and a heavy computational burden. To address this issue, we further propose a new sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that formulates the sparsifying dictionary as a neural network layer and embeds it into LISTA neural network. The numerical results show that our proposed algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves better performance than LISTA with ten times atoms reduction.Comment: 4 pages, 5 figure

Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study

BackgroundThe novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models’ generalization ability.MethodsWe retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.ResultsThe AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).ConclusionThe nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively

The Stochastic Resonance Phenomenon of Different Noises in Underdamped Bistable System

In this paper, the stochastic resonance (SR) phenomenon of four kinds of noises (the white noise, the harmonic noise, the asymmetric dichotomous noise, and the Lévy noise) in underdamped bistable systems is studied. By applying theory of stochastic differential equations to the numerical simulation of stochastic resonance problem, we simulate and analyze the system responses and pay close attention to stochastic control in the proposed systems. Then, the factors of influence to the SR are investigated by the Euler-Maruyama algorithm, Milstein algorithm, and fourth-order Runge-Kutta algorithm, respectively. The results show that the SR phenomenon can be generated in the proposed system under certain conditions by adjusting the parameters of the control effect with different noises. We also found that the type of the noise has little effect on the resonance peak of the output power spectrum density, which is not observed in conventional harmonic systems driven by multiplicative noise with only an overdamped term. Therefore, the conclusion of this paper can provide experimental basis for the further study of stochastic resonance

A new species of Psyllaephagus (Hymenoptera: Encyrtidae) from China, parasitoid of Macrohomotoma sinica (Hemiptera: Homotomidae) on Ficus concinna

During the investigation of forest insects in Guilin, Guangxi, some encyrtids belonging to Psyllaephagus were reared from Macrohomotoma sinica (Hemiptera: Homotomidae) on Ficus concinna.A new species of Psyllaephagus Howard (Hymenoptera: Encyrtidae), P. guangxiensis Zu sp. nov., is described from Guangxi, China as a parasitoid of Macrohomotoma sinica Yang & Li (Hemiptera: Homotomidae) on Ficus concinna (Miq.) Miq. (Urticales: Moraceae)

Flexible Actuator Based on Conductive PAM Hydrogel Electrodes with Enhanced Water Retention Capacity and Conductivity

Conductive polyacrylamide (PAM) hydrogels with salts that act as electrolytes have been used as transparent electrodes with high elasticity in flexible electronic devices. Different types and contents of raw materials will affect their performance in all aspects. We tried to introduce highly hydratable salts into PAM hydrogels to improve their water retention capacity. Different salts can improve the water retention capacity of PAM hydrogels to a certain extent. In particular, PAM hydrogels containing higher concentrations of lithium chloride (LiCl) and calcium chloride (CaCl2) showed an extremely strong water retention capacity and could retain about 90% and more than 98% of the initial water in the experimental environment at a temperature of 25 °C and a relative humidity of 60% RH, respectively. In addition, we conducted electrical conductivity tests on these PAM hydrogels with different salts. The PAM hydrogels containing LiCl also show outstanding conductivity, and the highest conductivity value can reach up to about 8 S/m. However, the PAM hydrogels containing CaCl2, which also performed well in terms of their water retention capacity, were relatively common in terms of their electrical conductivity. On this basis, we attempted to introduce single-walled carbon nanotubes (SWCNTs), multi-walled carbon nanotubes (MWCNTs), and graphene (GO) electronic conductors to enhance the electrical conductivity of the PAM hydrogels containing LiCl. The conductivity of the PAM hydrogels containing LiCl was improved to a certain extent after the addition of these electronic conductors. The highest electrical conductivity was about 10 S/m after we added the SWCNTs. This experimental result indicates that these electronic conductors can indeed enhance the electrical conductivity of PAM hydrogels to a certain extent. After a maximum of 5000 repeated tensile tests, the conductive hydrogel samples could still maintain their original morphological characteristics and conductivity. This means that these conductive hydrogel samples have a certain degree of system reliability. We made the PAM conductive hydrogels with high water retention and good conductivity properties into thin electrodes and applied them to an electric response flexible actuator with dielectric elastomer as the functional material. This flexible actuator can achieve a maximum area strain of 18% under an external voltage of 10 kV. This new composite hydrogels with high water retention and excellent conductivity properties will enable more possibilities for the application of hydrogels