Per-link Parallel and Distributed Hybrid Beamforming for Multi-Cell Massive MIMO Millimeter Wave Full Duplex

This paper presents two novel hybrid beamforming (HYBF) designs for a multi-cell massive multiple-input-multiple-output (mMIMO) millimeter wave (mmWave) full duplex (FD) system under limited dynamic range (LDR). Firstly, we present a novel centralized HYBF (C-HYBF) scheme based on alternating optimization. In general, the complexity of C-HYBF schemes scales quadratically as a function of the number of users and cells, which may limit their scalability. Moreover, they require significant communication overhead to transfer complete channel state information (CSI) to the central node every channel coherence time for optimization. The central node also requires very high computational power to jointly optimize many variables for the uplink (UL) and downlink (DL) users in FD systems. To overcome these drawbacks, we propose a very low-complexity and scalable cooperative per-link parallel and distributed (P$\&$D)-HYBF scheme. It allows each mmWave FD base station (BS) to update the beamformers for its users in a distributed fashion and independently in parallel on different computational processors. The complexity of P$\&$D-HYBF scales only linearly as the network size grows, making it desirable for the next generation of large and dense mmWave FD networks. Simulation results show that both designs significantly outperform the fully digital half duplex (HD) system with only a few radio-frequency (RF) chains and achieve similar performance. <br>

Updated IQ and Well-being Scores for the 50 U.S. States

At the level of the 50 U.S. states, an interconnected nexus of well-being variables exists. These variables have been shown to strongly correlate with estimates of state IQ in interesting ways. But the state IQ estimates (McDaniel 2006) are now more than 16 years old, and the state well-being estimates (Pesta et al., 2010) are over 12 years old. Updated state IQ and well-being estimates are therefore needed. I thus first created new state IQ estimates by analyzing scores from both the Program for the International Assessment of Adult Competency (for adults), and the National Assessment of Educational Progress (for fourth and eighth grade children) exams. I also created new global well-being scores by analyzing state variables from the following four well-being subdomains: crime, income, health, and education. When validating the nexus, several interesting correlations existed among the variables. For example, state IQ most strongly predicted FICO credit scores, alcohol consumption (directly), income inequality, and state temperature. Interestingly, state IQ derived here also correlated .58 with state IQ estimates from over 100 years ago. Global well-being likewise correlated with many old and new variables in the nexus, including a correlation of .80 with IQ. In sum, at the level of the U.S. state, a nexus of important, strongly correlated variables exists. These variables comprise well-being, and state IQ is a central node in this network.

Attention-Based Graph Convolutional Network for Zero-Shot Learning with Pre-Training

Zero-shot learning (ZSL) is a powerful and promising learning paradigm for classifying instances that have not been seen in training. Although graph convolutional networks (GCNs) have recently shown great potential for the ZSL tasks, these models cannot adjust the constant connection weights between the nodes in knowledge graph and the neighbor nodes contribute equally to classify the central node. In this study, we apply an attention mechanism to adjust the connection weights adaptively to learn more important information for classifying unseen target nodes. First, we propose an attention graph convolutional network for zero-shot learning (AGCNZ) by integrating the attention mechanism and GCN directly. Then, in order to prevent the dilution of knowledge from distant nodes, we apply the dense graph propagation (DGP) model for the ZSL tasks and propose an attention dense graph propagation model for zero-shot learning (ADGPZ). Finally, we propose a modified loss function with a relaxation factor to further improve the performance of the learned classifier. Experimental results under different pre-training settings verified the effectiveness of the proposed attention-based models for ZSL.

SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation, A Dynamical Mathematical Model

In the present work we propose a dynamical mathematical model of the lung cells inflammation process in response to SARS-CoV-2 infection. In this scenario the main protease Nsp5 enhances the inflammatory process, increasing the levels of NF kB, IL-6, Cox2, and PGE2 with respect to a reference state without the virus. In presence of the virus the translation rates of NF kB and IkB arise to a high constant value, and when the translation rate of IL-6 also increases above the threshold value of 7 pg mL−1 s−1 the model predicts a persistent over stimulated immune state with high levels of the cytokine IL-6. Our model shows how such over stimulated immune state becomes autonomous of the signals from other immune cells such as macrophages and lymphocytes, and does not shut down by itself. We also show that in the context of the dynamical model presented here, Dexamethasone or Nimesulide have little effect on such inflammation state of the infected lung cell, and the only form to suppress it is with the inhibition of the activity of the viral protein Nsp5. To that end, our model suggest that drugs like Saquinavir may be useful. In this form, our model suggests that Nsp5 is effectively a central node underlying the severe acute lung inflammation during SARS-CoV-2 infection. The persistent production of IL-6 by lung cells can be one of the causes of the cytokine storm observed in critical patients with COVID19. Nsp5 seems to be the switch to start inflammation, the consequent overproduction of the ACE2 receptor, and an important underlying cause of the most severe cases of COVID19.

DNA Methylation Changes in Fibromyalgia Suggest the Role of the Immune-Inflammatory Response and Central Sensitization

Fibromyalgia (FM) has been explained as a result of gene-environment interactions. The present study aims to verify DNA methylation differences in eleven candidate genome regions previously associated to FM, evaluating DNA methylation patterns as potential disease biomarkers. DNA methylation was analyzed through bisulfite sequencing, comparing 42 FM women and their 42 healthy sisters. The associations between the level of methylation in these regions were further explored through a network analysis. Lastly, a logistic regression model investigated the regions potentially associated with FM, when controlling for sociodemographic variables and depressive symptoms. The analysis highlighted significant differences in the GCSAML region methylation between patients and controls. Moreover, seventeen single CpGs, belonging to other genes, were significantly different, however, only one cytosine related to GCSAML survived the correction for multiple comparisons. The network structure of methylation sites was different for each group; GRM2 methylation represented a central node only for FM patients. Logistic regression revealed that depressive symptoms and DNA methylation in the GRM2 region were significantly associated with FM risk. Our study encourages better exploration of GCSAML and GRM2 functions and their possible role in FM affecting immune, inflammatory response, and central sensitization of pain.

Syntactic and Semantic Features of Three-Act Verbs in Russian and Uzbek Languages

Research of syntactic-semantic analysis of three-act verbs consists in theoretical comprehension of transformational grammar in its enormous explanatory power. The core of transformational grammar is the idea of the core of the language, consisting of the simplest linguistic structures, from which all other linguistic structures of greater or lesser complexity can be derived. The problem of invariance, which is the central problem of modern structural linguistics, finds its most profound solution precisely in transformational grammar. The core of the language includes simple, declarative, active sentences, the so-called core sentences. In European languages, verb sentences are most common. They are followed by substantive, adjective and adverbial sentences in decreasing order of usage. In a simple sentence, the verb does not have to be the central node, but if there is a verb in the sentence, it is always the center of that sentence.

Analyzing Levels of Concern About Joint Punishment for Dishonesty Using the Visibility Graph Network

Joint punishment for dishonesty is an important means of administrative regulation. This research analyzed the dynamic characteristics of time series data from the Baidu search index using the keywords “joint punishment for dishonesty” based on a visibility graph network. Applying a visibility graph algorithm, time series data from the Baidu Index was transformed into complex networks, with parameters calculated to analyze the topological structure. Results showed differences in the use of joint punishment for dishonesty in certain provinces by calculating the parameters of the time series network from January 1, 2020 to May 27, 2021; it was also shown that most of the networks were scale-free. Finally, the results of K-means clustering showed that the 31 provinces (excluding Hong Kong, Macao and Taiwan) can be divided into four types. Meanwhile, by analyzing the national Baidu Index data from 2020 to May 2021, the period of the time series data and the influence range of the central node were found.

Mining tacit knowledge of pilots in flight missions based on visual cognitive graph

<div>As a highly dynamic operating process, flight activity requires a lot of attention from pilots. Thus, it’s quite imperative to give research to their visual attention. Traditional research methods mostly based on qualitative analysis, or hypothetical model, and seldom put context information into their model. However, the underlying knowledge (tacit knowledge) hidden in the different performances of pilot’s attention allocation is context related, and is hard to express by experts, thus it is difficult to use those traditional methods to construct an interaction system. In this paper, we mined attention pattern with scene context to achieve the quantitative analysis of tacit knowledge of pilots during flight tasks, and use the method of data mining as well as attribute graph model to construct visual cognitive graph(s). The attribute graph model was adopted to construct visual cognitive graphs which associate the obtained visual information within the flight context. Based on the model, the attention pattern with scene context was mined to achieve the quantitative analysis of tacit knowledge of pilots during flight tasks. Besides, three physical quantities derived from graph theory was introduced to describe the tacit knowledge, which can be used directly to construct an interaction system: first, key information, which shown as central node in the graph we built, reveals the most important information during flight mission within context; second, relevant information, which contains several nodes that was closely connected and strongly impact the central node, reveals the factors affecting the key information; third, bridge information based on betweenness centrality, which can be regard as the important information bridge(s), reveals the process of decision making. Our work can be directly used to train novice pilots, to guide the interface design, and to construct the adaptive interaction system.</div>

Mining tacit knowledge of pilots in flight missions based on visual cognitive graph

<div>As a highly dynamic operating process, flight activity requires a lot of attention from pilots. Thus, it’s quite imperative to give research to their visual attention. Traditional research methods mostly based on qualitative analysis, or hypothetical model, and seldom put context information into their model. However, the underlying knowledge (tacit knowledge) hidden in the different performances of pilot’s attention allocation is context related, and is hard to express by experts, thus it is difficult to use those traditional methods to construct an interaction system. In this paper, we mined attention pattern with scene context to achieve the quantitative analysis of tacit knowledge of pilots during flight tasks, and use the method of data mining as well as attribute graph model to construct visual cognitive graph(s). The attribute graph model was adopted to construct visual cognitive graphs which associate the obtained visual information within the flight context. Based on the model, the attention pattern with scene context was mined to achieve the quantitative analysis of tacit knowledge of pilots during flight tasks. Besides, three physical quantities derived from graph theory was introduced to describe the tacit knowledge, which can be used directly to construct an interaction system: first, key information, which shown as central node in the graph we built, reveals the most important information during flight mission within context; second, relevant information, which contains several nodes that was closely connected and strongly impact the central node, reveals the factors affecting the key information; third, bridge information based on betweenness centrality, which can be regard as the important information bridge(s), reveals the process of decision making. Our work can be directly used to train novice pilots, to guide the interface design, and to construct the adaptive interaction system.</div>

Dorsal premammillary projection to periaqueductal gray controls escape vigor from innate and conditioned threats

Escape from threats has paramount importance for survival. However, it is unknown if a single circuit controls escape vigor from innate and conditioned threats. Cholecystokinin (cck)-expressing cells in the hypothalamic dorsal premammillary nucleus (PMd) are necessary for initiating escape from innate threats via a projection to the dorsolateral periaqueductal gray (dlPAG). We now show that in mice PMd-cck cells are activated during escape, but not other defensive behaviors. PMd-cck ensemble activity can also predict future escape. Furthermore, PMd inhibition decreases escape speed from both innate and conditioned threats. Inhibition of the PMd-cck projection to the dlPAG also decreased escape speed. Intriguingly, PMd-cck and dlPAG activity in mice showed higher mutual information during exposure to innate and conditioned threats. In parallel, human functional magnetic resonance imaging data show that a posterior hypothalamic-to-dlPAG pathway increased activity during exposure to aversive images, indicating that a similar pathway may possibly have a related role in humans. Our data identify the PMd-dlPAG circuit as a central node, controlling escape vigor elicited by both innate and conditioned threats.