Winner's Curse Free Robust Mendelian Randomization with Summary Data

In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses utilizing summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropic issues. To address these two issues and establish valid causal conclusions, we propose a unified robust Mendelian Randomization framework with summary data, which systematically removes the winner's curse and screens out invalid genetic instruments with pleiotropic effects. Different from existing robust MR literature, our framework delivers valid statistical inference on the causal effect neither requiring the genetic pleiotropy effects to follow any parametric distribution nor relying on perfect instrument screening property. Under appropriate conditions, we show that our proposed estimator converges to a normal distribution and its variance can be well estimated. We demonstrate the performance of our proposed estimator through Monte Carlo simulations and two case studies. The codes implementing the procedures are available at https://github.com/ChongWuLab/CARE/

Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory

Graph neural networks (GNNs), which have emerged as an effective method for handling machine learning tasks on graphs, bring a new approach to building recommender systems, where the task of recommendation can be formulated as the link prediction problem on user-item bipartite graphs. Training GNN-based recommender systems (GNNRecSys) on large graphs incurs a large memory footprint, easily exceeding the DRAM capacity on a typical server. Existing solutions resort to distributed subgraph training, which is inefficient due to the high cost of dynamically constructing subgraphs and significant redundancy across subgraphs. The emerging persistent memory technologies provide a significantly larger memory capacity than DRAMs at an affordable cost, making single-machine GNNRecSys training feasible, which eliminates the inefficiencies in distributed training. One major concern of using persistent memory devices for GNNRecSys is their relatively low bandwidth compared with DRAMs. This limitation can be particularly detrimental to achieving high performance for GNNRecSys workloads since their dominant compute kernels are sparse and memory access intensive. To understand whether persistent memory is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive analysis of their performance on a persistent memory device, namely, Intel Optane. Based on the analysis, we provide guidance on how to configure Optane for GNNRecSys workloads. Furthermore, we present techniques for large-batch training to fully realize the advantages of single-machine GNNRecSys training. Our experiment results show that with the tuned batch size and optimal system configuration, Optane-based single-machine GNNRecSys training outperforms distributed training by a large margin, especially when handling deep GNN models

Bending vibration prediction of orthotropic plate with wave-based method

A novel numerical predictive approach for steady-state response of thin orthotropic plate is presented based on wave-based method (WBM) that is applied in bending vibration prediction of thin and thick plate in mid-frequency range. The wavenumber parameters for orthotropic material and the particular solution of an infinite orthotropic plate with Fourier transform are derived. The proposed method is validated by numerical examples with simply supported boundary and clamped boundary. The compared result shows that the computational accuracy and efficiency of WBM is significantly higher than element based method, which is the ability of WBM for mid-frequency problems. The predictive ability of WBM is extended to process the dynamic response of orthotropic plate

SU(2) tetrahedron flux distribution few body effect in lattice QCD

We study the four-quark interaction as a first step in understanding the QCD origin of the nuclear force in nature. We simulate QCD on a 20 x 20 x 20 x 32 space-time lattice with the simplifying quenched and static approximations, and with the SU(2) gauge group. Recent four-quark simulations reveal interesting tetrahedral geometry and planar four-quark flux distributions that cannot be explained by existing models. We simulate the flux distribution for the still-unexplored next higher level of geometrical complexity, namely four quarks on the corners of a tetrahedron. In order to complete the simulation within the allotted computing time, we have improved the approach used to simulate the flux distribution. Compared to previous approaches, the new approach nearly eliminates the bottleneck of the computing time, provides more than a 100-time speedup in our project, and also provides a better strategy for improving signal-noise ratio and suppressing signal distortion from the lattice structure. As the result of this improved approach, we have observed the long diagonal flux tube structure, repeated the Helsinki group's 1998 results for the flux distribution of a square geometry, and, for the first time, simulated the flux distribution of a tetrahedron geometry. In this thesis, we also explore some fundamental questions of lattice QCD related to computability theory and complexity theory

Comparison of Neutron Detection Performance of Four Thin-Film Semiconductor Neutron Detectors Based on Geant4

Third-generation semiconductor materials have a wide band gap, high thermal conductivity, high chemical stability and strong radiation resistance. These materials have broad application prospects in optoelectronics, high-temperature and high-power equipment and radiation detectors. In this work, thin-film solid state neutron detectors made of four third-generation semiconductor materials are studied. Geant4 10.7 was used to analyze and optimize detectors. The optimal thicknesses required to achieve the highest detection efficiency for the four materials are studied. The optimized materials include diamond, silicon carbide (SiC), gallium oxide (Ga2O3) and gallium nitride (GaN), and the converter layer materials are boron carbide (B4C) and lithium fluoride (LiF) with a natural enrichment of boron and lithium. With optimal thickness, the primary knock-on atom (PKA) energy spectrum and displacements per atom (DPA) are studied to provide an indication of the radiation hardness of the four materials. The gamma rejection capabilities and electron collection efficiency (ECE) of these materials have also been studied. This work will contribute to manufacturing radiation-resistant, high-temperature-resistant and fast response neutron detectors. It will facilitate reactor monitoring, high-energy physics experiments and nuclear fusion research

Simulations of charge collection of a gallium nitride based pin thin-film neutron detector

The development of new fast neutron reactors and nuclear fusion reactors requires new neutron detectors in extreme environments. Due to its wide bandgap (3.4 eV) and radiation resistance capability, gallium nitride (GaN) is a candidate for neutron detection in extreme environments. This study introduces a novel simulation method of charge collection efficiency (CCE) for GaN pin thin-film neutron detector based on the Hecht equation and Monte Carlo simulation. A modified 2-carrier Hecht equation is used to simulate the CCE of the detector with a different depth depletion region. After obtaining the neutron energy deposition distribution in the sensitive volume of the detector, the Hecht equation is used to calculate the charge collection efficiency at different positions of the detector under a uniform electric field. The maximum relative error between the simulated CCE and the experimental CCE value is about 6.3%

The source localization technique based on improved functional beamforming using a virtual array

Beamforming have become a popular technique to identify sound source. The most common application is conventional beamforming, but it has low resolution and requires a large number of microphones at low frequency. To overcome this problem, an improved functional beamforming method based on “virtual array” and the relative spectral matrix is introduced. Firstly, the relative complex pressures of the sound field can be acquired by “virtual array” with one scanning microphone and a fixed reference microphone. Thereby, a relative spectral matrix of the relative complex pressures measured can be obtained. Then the improved functional beamforming method with order v is developed based on the relative spectral matrix. And the resolution of the improved method can be modified by increased the number of order v. but it also can be improved by changing virtual microphones. This property allows widening the scope of this interesting beamforming method