Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods

Thompson Sampling-Based Channel Selection through Density Estimation aided by Stochastic Geometry

We propose a sophisticated channel selection scheme based on multi-armed bandits and stochastic geometry analysis. In the proposed scheme, a typical user attempts to estimate the density of active interferers for every channel via the repeated observations of signal-to-interference power ratio (SIR), which demonstrates the randomness induced by randomized interference sources and fading effects. The purpose of this study involves enabling a typical user to identify the channel with the lowest density of active interferers while considering the communication quality during exploration. To resolve the trade-off between obtaining more observations on uncertain channels and using a channel that appears better, we employ a bandit algorithm called Thompson sampling (TS), which is known for its empirical effectiveness. We consider two ideas to enhance TS. First, noticing that the SIR distribution derived through stochastic geometry is useful for updating the posterior distribution of the density, we propose incorporating the SIR distribution into TS to estimate the density of active interferers. Second, TS requires sampling from the posterior distribution of the density for each channel, while it is significantly more complicated for the posterior distribution of the density to generate samples than well-known distribution. The results indicate that this type of sampling process is achieved via the Markov chain Monte Carlo method (MCMC). The simulation results indicate that the proposed method enables a typical user to determine the channel with the lowest density more efficiently than the TS without density estimation aided by stochastic geometry, and ε-greedy strategies

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

無線リソース割当における相互作用のモデル化及び解析

京都大学0048新制・課程博士博士(情報学)甲第22589号情博第726号新制||情||124(附属図書館)京都大学大学院情報学研究科通信情報システム専攻(主査)教授 守倉 正博, 教授 原田 博司, 教授 大木 英司学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Elastic Bending Flexibility of a Fluorescent Organic Single Crystal: New Aspects of the Commonly Used Building Block 4,7-Dibromo-2,1,3-benzothiadiazole

Commercially available 4,7-dibromo-2,1,3-benzothiadiazole can be used as a building block for the organic synthesis of high performance optoelectronics devices. Herein, we report that a single crystal of 4,7-dibromo-2,1,3-benzothiadiazole displays elastic bending flexibility and crystalline-state fluorescence. A large needle-shaped single crystal (over 1 cm long) exhibited a fibril lamella type structure based on slip-stacked (<i>J</i>-aggregated) molecular wires. The crystal was capable of bending under applied stress and quickly reverts to its original shape upon relaxation. The crystal shows a reversible fluorescence change via mechanical bending–relaxation that can be performed many times

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs With Graph Convolutional Networks

For densely deployed wireless local area networks (WLANs), this paper proposes a deep reinforcement learning-based channel allocation scheme that enables the efficient use of experience. The central idea is that an objective function is modeled relative to communication quality as a parametric function of a pair of observed topologies and channels. This is because communication quality in WLANs is significantly influenced by the carrier sensing relationship between access points. The features of the proposed scheme can be summarized by two points. First, we adopt graph convolutional layers in the model to extract the features of the channel vectors with topology information, which is the adjacency matrix of the graph dependent on the carrier sensing relationships. Second, we filter experiences to reduce the duplication of data for learning, which can often adversely influence the generalization performance. Because fixed experiences tend to be repeatedly observed in WLAN channel allocation problems, the duplication of experiences must be avoided. The simulation results demonstrate that the proposed method enables the allocation of channels in densely deployed WLANs such that the system throughput increases. Moreover, improved channel allocation, compared to other existing methods, is achieved in terms of the system throughput. Furthermore, compared to the immediate reward maximization method, the proposed method successfully achieves greater reward channel allocation or realizes the optimal channel allocation while reducing the number of changes