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

A New Species of the Genus Trimeresurus from Southwest China (Squamata: Viperidae)

Species from the Trimeresurus popeiorum complex (Subgenus: Popeia) is a very complex group. T. popeiorum is the only Popeia species known from China. During the past two years, five adult Popeia specimens (4 males, 1 female) were collected from Yingjiang County, Southern Yunnan, China. Molecular, morphological and ecological data show distinct differences from known species, herein we describe these specimens as a new species Trimeresurus yingjiangensis sp. nov Chen, Ding, Shi and Zhang, 2018. Morphologically, the new species distinct from other Popeia species by a combination of following characters: (1) dorsal body olive drab,without cross bands on the scales; (2) a conspicuous bicolor ventrolateral stripe present on each side of males, first row of dorsal scales firebrick with a white ellipse dot on posterior upper part in male, these strips absent in females; (3) eyes firebrick in both gender; (4) suboculars separated from 3rd upper labial by one scale on each side; (5) ventrals 164–168 (n = 5); (6) MSR 21

Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

This paper presents a comprehensive evaluation of the Optical Character Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large Multimodal Model (LMM). We assess the model's performance across a range of OCR tasks, including scene text recognition, handwritten text recognition, handwritten mathematical expression recognition, table structure recognition, and information extraction from visually-rich document. The evaluation reveals that GPT-4V performs well in recognizing and understanding Latin contents, but struggles with multilingual scenarios and complex tasks. Specifically, it showed limitations when dealing with non-Latin languages and complex tasks such as handwriting mathematical expression recognition, table structure recognition, and end-to-end semantic entity recognition and pair extraction from document image. Based on these observations, we affirm the necessity and continued research value of specialized OCR models. In general, despite its versatility in handling diverse OCR tasks, GPT-4V does not outperform existing state-of-the-art OCR models. How to fully utilize pre-trained general-purpose LMMs such as GPT-4V for OCR downstream tasks remains an open problem. The study offers a critical reference for future research in OCR with LMMs. Evaluation pipeline and results are available at https://github.com/SCUT-DLVCLab/GPT-4V_OCR

Voxel-based, brain-wide association study of aberrant functional connectivity in schizophrenia implicates thalamocortical circuitry

Background: Wernicke\u27s concept of \u27sejunction\u27 or aberrant associations among specialized brain regions is one of the earliest hypotheses attempting to explain the myriad of symptoms in psychotic disorders. Unbiased data mining of all possible brain-wide connections in large data sets is an essential first step in localizing these aberrant circuits. Methods: We analyzed functional connectivity using the largest resting-state neuroimaging data set reported to date in the schizophrenia literature (415 patients vs. 405 controls from UK, USA, Taiwan, and China). An exhaustive brain-wide association study at both regional and voxel-based levels enabled a continuous data-driven discovery of the key aberrant circuits in schizophrenia. Results: Results identify the thalamus as the key hub for altered functional networks in patients. Increased thalamus-primary somatosensory cortex connectivity was the most significant aberration in schizophrenia (P=10-18). Overall, a number of thalamic links with motor and sensory cortical regions showed increased connectivity in schizophrenia, whereas thalamo-frontal connectivity was weakened. Network changes were correlated with symptom severity and illness duration, and support vector machine analysis revealed discrimination accuracies of 73.53-80.92%. Conclusions: Widespread alterations in resting-state thalamocortical functional connectivity is likely to be a core feature of schizophrenia that contributes to the extensive sensory, motor, cognitive, and emotional impairments in this disorder. Changes in this schizophrenia-associated network could be a reliable mechanistic index to discriminate patients from healthy controls

Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation

Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration

Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation

Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration

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

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

Water use pattern of Pinus tabulaeformis in the semiarid region of Loess Plateau, China

Aim of the study: We analyzed the water-use strategy of P. tabulaeformis and determine the relationships between environmental factors and transpiration rates in the P. tabulaeformis woodlands.Area of study: Loess Plateau region of Northwest China.Material and Methods: Sap flow density of the P. tabulaeformis trees was measured with Granier-type sensors. Stand transpiration was extrapolated from the sap flow measurements of individual trees using the following Granier equation.Main results: The mean sap flow rates of individual P. tabulaeformis trees ranged from 9 L day−1 to 54 L day−1. Photosynthetically active radiation and vapor pressure deficit were the dominant driving factors of transpiration when soil water content was sufficient (soil water content>16%), considering that soil water content is the primary factor of influencing transpiration at the driest month of the year. During the entire growing season, the maximum and minimum daily stand transpiration rates were 2.93 and 0.78 mm day−1, respectively. The mean stand transpiration rate was 1.9 mm day−1, and the total stand transpiration from May to September was 294.1 mm.Research highlights: This study can serve as a basis for detailed analyses of the water physiology and growth of P. tabulaeformis plantation trees for the later application of a climate-driven process model.Keywords: Sap flow; stand transpiration; environmental factor; Pinus tabulaeformis; Loess Plateau

Sap flow in response to rainfall pulses for two shrub species in the semiarid Chinese Loess Plateau

Rainfall pulses can significantly drive the evolution of the structure and function of semiarid ecosystems, and understanding the mechanisms that underlie the response of semiarid plants to rainfall is the key to understanding the responses of semi–arid ecosystems to global climatic change. We measured sap flow in the branches and stems of shrubs (Caragana korshinskii Kom. and Hippophae rhamnoides Linn.) using sap flow gauges, and studied the response of sap flow density to rainfall pulses using the “threshold–delay” model in the Chinese Loess Plateau. The results showed that the sap flow began about 1 h earlier, and increased twofold after rainfall, compared to its pre-rainfall value. The sap flow increased significantly with increasing rainfall classes, then gradually decreased. The response of sap flow was different among rainfall, species, position (branch and stem) during the pulse period, and the interactive effects also differed significantly (P < 0.0001). The response pattern followed the threshold–delay model, with lower rainfall thresholds of 5.2, 5.5 mm and 0.7, 0.8 mm of stem and branch for C. korshinskii and H. rhamnoides, demonstrating the importance of small rainfall events for plant growth and survival in semi–arid regions