Narrowband Searches for Continuous and Long-duration Transient Gravitational Waves from Known Pulsars in the LIGO-Virgo Third Observing Run

Isolated neutron stars that are asymmetric with respect to their spin axis are possible sources of detectable continuous gravitational waves. This paper presents a fully coherent search for such signals from eighteen pulsars in data from LIGO and Virgo's third observing run (O3). For known pulsars, efficient and sensitive matched-filter searches can be carried out if one assumes the gravitational radiation is phase-locked to the electromagnetic emission. In the search presented here, we relax this assumption and allow both the frequency and the time derivative of the frequency of the gravitational waves to vary in a small range around those inferred from electromagnetic observations. We find no evidence for continuous gravitational waves, and set upper limits on the strain amplitude for each target. These limits are more constraining for seven of the targets than the spin-down limit defined by ascribing all rotational energy loss to gravitational radiation. In an additional search, we look in O3 data for long-duration (hours-months) transient gravitational waves in the aftermath of pulsar glitches for six targets with a total of nine glitches. We report two marginal outliers from this search, but find no clear evidence for such emission either. The resulting duration-dependent strain upper limits do not surpass indirect energy constraints for any of these targets. © 2022. The Author(s). Published by the American Astronomical Society

Multi-trait genome-wide association study identifies new loci associated with optic disc parameters

A new avenue of mining published genome-wide association studies includes the joint analysis of related traits. The power of this approach depends on the genetic correlation of traits, which reflects the number of pleiotropic loci, i.e. genetic loci influencing multiple traits. Here, we applied new meta-analyses of optic nerve head (ONH) related traits implicated in primary open-angle glaucoma (POAG); intraocular pressure and central corneal thickness using Haplotype reference consortium imputations. We performed a multi-trait analysis of ONH parameters cup area, disc area and vertical cup-disc ratio. We uncover new variants; rs11158547 in PPP1R36-PLEKHG3 and rs1028727 near SERPINE3 at genome-wide significance that replicate in independent Asian cohorts imputed to 1000 Genomes. At this point, validation of these variants in POAG cohorts is hampered by the high degree of heterogeneity. Our results show that multi-trait analysis is a valid approach to identify novel pleiotropic variants for ONH

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

[[alternative]]The study of integration of rough set theory and association rules for ordinal data analysis

博士[[abstract]]首先，傳統的關聯法則，使用者必須不斷的試誤（包含：屬性的挑選、門檻值的設定等…法則產生前的相關程序與步驟），俾便找出具解釋能力的關聯法則。再者，與近期相關研究相比，資料採礦資料都是以資料是精確且乾淨為前提的，在這樣的條件下所產生的關聯法則，可能會發生在某些特定情況下（例如：有人為輸入的錯誤、記錄錯誤等…不完整資料），符合條件的規則被淘汰亦或產生過多的規則。最後，透過相關研究的文獻探討，發現約略集理論已成功的被運用在選擇屬性及改變效率之決策問題上。因此，本研究選擇以約略集理論為研究的理論基礎，從縮短決策者探勘關聯法則的試誤時間為解決問題的方向，在規則產生前，利用集合的產生，針對資料型態涉及順序尺度或含區間資料的順序尺度，提供新的演算概念。希冀，在不失去原本的排序關係的前提下，提供更多的排序資訊予決策者使用。 研究中，針對順序尺度與含區間資料的順序尺度，分別提出約略關聯法則的探勘步驟、演算流程說明、應用於酒精飲料產品與非酒精飲料的案例，以及提供相關個案的管理意涵。最後，將本研究所未考量到的部分以及可以持續研究的方向分段論述，讓後續的相關研究學者可以參考。[[abstract]]First, as per the traditional association rules, in order to identify meaningful association rules, the user must use trial and error method （including attribute choice, threshold value hypothesis, etc., considering the procedure and step taken before the association rules were formulated）. Furthermore, unlike algorithm-related research, data mining algorithms assumed that input data were accurate; however, the assumption would not be made in case one best rule exists for each particular situation such as input mistake or record mistake and similar incomplete data. Finally, through literature review, rough set theory has been successfully applied in deriving decision trees/rules and specifying problems, with proven effectiveness in selecting attributes. Therefore, we select rough set theory on the basis of our research, and this reduces the time that policymakers take to determine meaningful association rules. Before the rule is formulated, through the set process, we provide a new algorithm for the data type that involves ordinal data and ordinal data with internal data. Under a condition that does not affect the sorting relations between the values of the ordinal data, we provide more sorting information that the policymakers can use. In the research, we provide two new algorithms that are suitable for ordinal data and ordinal data with internal data. Further, we provide illustrative examples using alcoholic and non-alcoholic beverage products individually. Finally, we give some suggestions for future research.[[tableofcontents]]目次 謝辭 I 中文摘要 II 英文摘要 III 目次 V 表目錄 VIII 圖目錄 X 第一章 緒論 1 1.1研究背景與動機 1 1.2研究問題與目的 2 1.3研究方法與流程 3 第二章 文獻探討 4 2.1約略集理論 4 2.1.1 一般的約略集（A general view of rough sets） 5 2.1.2 變精度約略集（variable precision rough set/VPRS） 6 2.1.3 約略集理論的好處及應用的領域 7 2.1.4 約略集與各領域的結合 10 2.1.5 約略集理論與本研究的關係 11 2.2關聯法則 12 2.2.1 關聯法則的定義 12 2.2.2 從分群或分類討論關聯法則 13 2.2.3 從產生規則集討論關聯法則 16 2.2.4 從資料維度討論關聯法則 17 2.2.5 關聯法則的改良 18 2.2.6 關聯法則與本研究的關係 19 2.3相關研究綜合討論 20 2.3.1 資料採礦與約略集理論 20 2.3.2 相關研究使用的資料型態與衡量尺度 21 2.3.3 以約略集理論為基礎的相關研究 23 2.3.4 約略集理論與模糊理論及之比較 23 第三章 探勘順序尺度約略關聯法則 25 3.1研究問題 25 3.2順序尺度的約略關聯法則探勘步驟 26 3.3順序尺度的約略關聯法則演算流程 33 3.4順序尺度的約略關聯法則應用在非酒精飲料 36 3.5順序尺度的約略關聯法則應用在非酒精飲料管理意涵 42 3.5.1 從法則產生效率與效能的角度與傳統關聯法則比較 42 3.5.2 從法則資訊提供的角度與過去的研究比較 44 3.5.3 順序尺度的約略關聯法則在行銷策略上的運用 46 第四章 探勘含區間資料的順序尺度約略關聯法則 48 4.1研究問題 48 4.2含區間資料的順序尺度約略關聯法則探勘步驟 49 4.3含區間資料的順序尺度約略關聯法則演算流程 57 4.4含區間資料的順序尺度約略關聯法則應用在酒精飲料 61 4.5含區間資料的順序尺度約略關聯法則應用在酒精飲料管理意涵 69 4.5.1 與傳統關聯法則比較 69 4.5.2 含區間資料的順序尺度約略關聯法則建立產業的競爭力 70 4.5.3 含區間資料的順序尺度約略關聯法則在行銷策略上的應用 70 第五章 結論與後續研究 72 5.1研究結論 73 5.2後續研究 74 5.2.1 從相對的概念討論順序尺度 74 5.2.2 發展階層概念的順序尺度約略關聯規則 75 5.2.3 發展決策支援系統 76 5.2.4 從關聯規則門檻值設定改善 77 5.2.5 發展推薦機制探勘改變行為 77 參考文獻 78 附錄-問卷 87 表目錄 表2-1約略集理論的好處 7 表2-2約略集理論運用的領域 9 表2-3約略集理論與各理論結合之應用 10 表2-4從分類觀點討論或改良關聯法則 13 表2-5從分群觀點討論或改良關聯法則 14 表2-6從產生規則集討論關聯法則 16 表2-7從資料維度討論關聯法則 18 表2-8關聯法則的改良 18 表2-9相關研究使用的資料型態與衡量尺度 22 表3-1習慣飲用「非酒精類飲料」的排序資料表 25 表3-2資訊系統表 27 表3-3順序性資料的核心屬性值 28 表3-4決策資料表 29 表3-5不可辨識關係下的屬性值 31 表3-6非酒精飲料資訊表 36 表3-7基本統計表 38 表3-8非酒精飲料偏好排序核心屬性集合 39 表3-9非酒精飲料偏好的約略關聯法則集合 40 表3-10非酒精飲料偏好的傳統關聯法則集合 41 表3-11APRIORI產生的關聯法則 43 表3-12非酒精飲料偏好的消費者行為規則集合 47 表4-1資訊系統表 50 表4-2啤酒「品牌回想」的排序資料表 50 表4-3「年齡及收入」與品牌回想間的關係 53 表4-4以台灣啤酒為主的決策資料表 54 表4-5不可辨識關係下的資料集合 56 表4-6酒精飲料資訊表 61 表4-7基本統計表 63 表4-8屬值屬性與決策屬性間的潛在關係 64 表4-9酒精飲料的品牌回想排序總值 65 表4-10酒精飲料偏好的約略關聯法則集合 66 表4-11酒精飲料偏好的傳統關聯法則集合 68 表4-12傳統關聯法則產生的酒精飲料偏好的規則集合 69 表4-13酒精飲料偏好的消費者行為規則集合 70 圖目錄 圖1-1 研究流程圖 3 圖2-1 文獻探討架構圖 4 圖3-1約略集理論的上界與下界概念 32 圖3-2 資料節點串流圖 44 圖3-3分群後的非酒精飲料產品光譜圖 44 圖3-4探勘核心屬性後的非酒精飲料產品光譜圖 45 圖4-1品牌權益的概念模式 48 圖4-2考量品牌回想排序總值的酒精飲料品牌光譜圖 65 圖5-1順序尺度資料的階層約略關聯展開樹 75[[note]]學號: 895620028, 學年度: 10

Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition

Indoor action recognition plays an important role in modern society, such as intelligent healthcare in large mobile cabin hospitals. With the wide usage of depth sensors like Kinect, multimodal information including skeleton and RGB modalities brings a promising way to improve the performance. However, existing methods are either focusing on a single data modality or failed to take the advantage of multiple data modalities. In this paper, we propose a Teacher-Student Multimodal Fusion (TSMF) model that fuses the skeleton and RGB modalities at the model level for indoor action recognition. In our TSMF, we utilize a teacher network to transfer the structural knowledge of the skeleton modality to a student network for the RGB modality. With extensive experiments on two benchmarking datasets: NTU RGB+D and PKU-MMD, results show that the proposed TSMF consistently performs better than state-of-the-art single modal and multimodal methods. It also indicates that our TSMF could not only improve the accuracy of the student network but also significantly improve the ensemble accuracy

Hybrid Manifold Embedding

In this brief, we present a novel supervised manifold learning framework dubbed hybrid manifold embedding (HyME). Unlike most of the existing supervised manifold learning algorithms that give linear explicit mapping functions, the HyME aims to provide a more general nonlinear explicit mapping function by performing a two-layer learning procedure. In the first layer, a new clustering strategy called geodesic clustering is proposed to divide the original data set into several subsets with minimum nonlinearity. In the second layer, a supervised dimensionality reduction scheme called locally conjugate discriminant projection is performed on each subset for maximizing the discriminant information and minimizing the dimension redundancy simultaneously in the reduced low-dimensional space. By integrating these two layers in a unified mapping function, a supervised manifold embedding framework is established to describe both global and local manifold structure as well as to preserve the discriminative ability in the learned subspace. Experiments on various data sets validate the effectiveness of the proposed method