1,038 research outputs found

    Improving efficiency of the path optimization method for a gauge theory

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    We investigate efficiency of a gauge-covariant neural network and an approximation of the Jacobian in optimizing the complexified integration path toward evading the sign problem in lattice field theories. For the construction of the complexified integration path, we employ the path optimization method. The 22-dimensional U(1)\text{U}(1) gauge theory with the complex gauge coupling constant is used as a laboratory to evaluate the efficiency. It is found that the gauge-covariant neural network, which is composed of the Stout-like smearing, can enhance the average phase factor, as the gauge-invariant input does. For the approximation of the Jacobian, we test the most drastic case in which we perfectly drop the Jacobian during the learning process. It reduces the numerical cost of the Jacobian calculation from O(N3){\cal O}(N^3) to O(1){\cal O}(1), where NN means the number of degrees of freedom of the theory. The path optimization using this Jacobian approximation still enhances the average phase factor at expense of a slight increase of the statistical error.Comment: 8 pages, 5 figures; accepted versio

    Dataset: An empirical study on self-admitted technical debt in modern code review

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    This data was used in the IST paper "An Empirical Study on Self-Admitted Technical Debt in Modern Code Review". The program to use this data is published in GitHub (https://github.com/Yutaro-Kashiwa/ReviewSATD_RP) When you use this data in your research, please cite the following papers: ``` @article{Kashiwa:IST:2022:SATD_Review, author = {Yutaro Kashiwa and Ryoma Nishikawa and Yasutaka Kamei and Masanari Kondo and Emad Shihab and Ryosuke Sato and Naoyasu Ubayashi}, title = {An empirical study on self-admitted technical debt in modern code review}, journal = {Information and Software Technology}, volume = {146}, pages = {106855}, year = {2022}, url = {https://doi.org/10.1016/j.infsof.2022.106855}, doi = {10.1016/j.infsof.2022.106855} } ``

    Non-missense variants of KCNH2 show better outcomes in type 2 long QT syndrome

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    AIMS: More than one-third of type 2 long QT syndrome (LQT2) patients carry KCNH2 non-missense variants that can result in haploinsufficiency (HI), leading to mechanistic loss-of-function. However, their clinical phenotypes have not been fully investigated. The remaining two-thirds of patients harbour missense variants, and past studies uncovered that most of these variants cause trafficking deficiency, resulting in different functional changes: either HI or dominant-negative (DN) effects. In this study, we examined the impact of altered molecular mechanisms on clinical outcomes in LQT2 patients. METHODS AND RESULTS: We included 429 LQT2 patients (234 probands) carrying a rare KCNH2 variant from our patient cohort undergoing genetic testing. Non-missense variants showed shorter corrected QT (QTc) and less arrhythmic events (AEs) than missense variants. We found that 40% of missense variants in this study were previously reported as HI or DN. Non-missense and HI-groups had similar phenotypes, while both exhibited shorter QTc and less AEs than the DN-group. Based on previous work, we predicted the functional change of the unreported variants-whether they cause HI or DN via altered functional domains-and stratified them as predicted HI (pHI)- or pDN-group. The pHI-group including non-missense variants exhibited milder phenotypes compared to the pDN-group. Multivariable Cox model showed that the functional change was an independent risk of AEs (P = 0.005). CONCLUSION: Stratification based on molecular biological studies enables us to better predict clinical outcomes in the patients with LQT2

    Some Aspects of Persistent Homology Analysis on Phase Transition: Examples in an Effective QCD Model with Heavy Quarks

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    Recently, persistent homology analysis has been used to investigate phase structure. In this study, we apply persistent homology analysis to the QCD effective model with heavy quarks at finite imaginary chemical potential; i.e., the Potts model with the suitably tuned external field. Since we try to obtain a deeper understanding of the relationship between persistent homology and phase transition in QCD, we consider the imaginary chemical potential because the clear phase transition, which is closely related to the confinement-deconfinement transition, exists. In the actual analysis, we employ the point-cloud approach to consider persistent homology. In addition, we investigate the fluctuation of persistent diagrams to obtain additional information on the relationship between the spatial topology and the phase transition

    CACNA1C-E1115K変異ヒトiPS細胞モデルにおけるCav 1.2イオン選択性障害がQT延長症候群・ブルガダ症候群のオーバーラップを引き起こすメカニズムの検討

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    京都大学新制・課程博士博士(医学)甲第24485号医博第4927号新制||医||1063(附属図書館)京都大学大学院医学研究科医学専攻(主査)教授 江藤 浩之, 教授 湊谷 謙司, 教授 大鶴 繁学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDGA

    3D Environment Modeling for Falsification and Beyond with Scenic 3.0

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    We present a major new version of Scenic, a probabilistic programming language for writing formal models of the environments of cyber-physical systems. Scenic has been successfully used for the design and analysis of CPS in a variety of domains, but earlier versions are limited to environments which are essentially two-dimensional. In this paper, we extend Scenic with native support for 3D geometry, introducing new syntax which provides expressive ways to describe 3D configurations while preserving the simplicity and readability of the language. We replace Scenic's simplistic representation of objects as boxes with precise modeling of complex shapes, including a ray tracing-based visibility system that accounts for object occlusion. We also extend the language to support arbitrary temporal requirements expressed in LTL, and build an extensible Scenic parser generated from a formal grammar of the language. Finally, we illustrate the new application domains these features enable with case studies that would have been impossible to accurately model in Scenic 2.Comment: 13 pages, 6 figures. Full version of a CAV 2023 tool paper, to appear in the Springer Lecture Notes in Computer Science serie
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