Speaker Recognition Using Isomorphic Graph Attention Network Based Pooling on Self-Supervised Representation

The emergence of self-supervised representation (i.e., wav2vec 2.0) allows speaker-recognition approaches to process spoken signals through foundation models built on speech data. Nevertheless, effective fusion on the representation requires further investigating, due to the inclusion of fixed or sub-optimal temporal pooling strategies. Despite of improved strategies considering graph learning and graph attention factors, non-injective aggregation still exists in the approaches, which may influence the performance for speaker recognition. In this regard, we propose a speaker recognition approach using Isomorphic Graph ATtention network (IsoGAT) on self-supervised representation. The proposed approach contains three modules of representation learning, graph attention, and aggregation, jointly considering learning on the self-supervised representation and the IsoGAT. Then, we perform experiments for speaker recognition tasks on VoxCeleb1\&2 datasets, with the corresponding experimental results demonstrating the recognition performance for the proposed approach, compared with existing pooling approaches on the self-supervised representation.Comment: 9 pages, 4 figure

Subgroup Analysis: Risk Quantification and Debiased Inference

Subgroup analysis is frequently used to account for the treatment effect heterogeneity in clinical trials. When a promising subgroup is selected from existing trial data, a decision on whether an additional confirmatory trial for the selected subgroup is worth pursuing needs to be made. Unfortunately, the usual statistical analysis applied to the selected subgroup as if the subgroup is identified independent of the data often leads to overly optimistic evaluations. Any statistical analysis that ignores how the subgroup is selected tends to suffer from subgroup selection bias. In this dissertation, we propose two new statistical tools to evaluate the selected subgroup. The first is a risk index which can be used as a simple screening tool to reduce the risk of over-optimism in naive subgroup analysis and the second is debiased inference to answer the question of how good the selected subgroup really is. The proposed tools are model-free, easy-to-implement and adjust for the subgroup selection bias appropriately. We demonstrate the merit of the proposed tools by re-analyzing the MONET1 trial. An extension of the debiased inference method is also discussed for observational studies with potentially many confounders.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/151409/1/xinzhoug_1.pd

Simulation of Refrigerant Phase Equilibria

Vapor-liquid equilibria for refrigerant mixtures modeled by an equation of state are studied. Phase behavior calculated by the Soave-Redlich-Kwong (SRK) equation with a single adjustable binary interaction parameter is compared with experimental data for binary refrigerant mixtures, two with a supercritical component and one that exhibits azeotropic behavior. It is shown that the SRK equation gives an adequate description of the phase envelope for binary refrigerant systems. The complex domain trust region methods of Lucia and co-workers (Lucia and Xu, 1992; Lucia et al., 1993) are applied to fixed vapor, isothermal flash model equations, with particular attention to root finding and root assignment at the equation of state (EOS) level of the calculations, and convergence in the retrograde and azeotropic regions of the phase diagram. Rules for assigning roots to the vapor and liquid phases in the case where all roots to the EOS are complex-valued are proposed and shown to yield correct results, even in retrograde regions. Convergence of the flash model equations is also studied. It is shown that the complex domain trust region algorithms outperform Newton\u27s method in singular regions of the phase diagram (i.e., at near azeotropic conditions and in the retrograde loop), primarily due to the eigenvalue-eigenvector decomposition strategy given in Sridhar and Lucia (1995). A variety of geometric figures are used to illustrate salient points

Assessing Heterogeneous Risk of Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data

There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset type II diabetes (T2D). However, because existing clinical studies with limited sample sizes often suffer from selection bias issues, there is no robust evidence supporting as to whether and what kind of populations are indeed vulnerable for developing T2D after taking statins. In this case study, building on the biobank and electronic health record data in the Partner Health System, we introduce a new data analysis pipeline from a biological perspective and a novel statistical methodology that address the limitations in existing studies to: (i) systematically examine heterogeneous treatment effects of stain use on T2D risk, (ii) uncover which patient subgroup is most vulnerable to T2D after taking statins, and (iii) assess the replicability and statistical significance of the most vulnerable subgroup via bootstrap calibration. Our proposed bootstrap calibration approach delivers asymptotically sharp confidence intervals and debiased estimates for the treatment effect of the most vulnerable subgroup in the presence of possibly high-dimensional covariates. By implementing our proposed approach, we find that females with high T2D genetic risk at baseline are indeed at high risk of developing T2D due to statin use, which provides evidences to support future clinical decisions with respect to statin use.Comment: 31 pages, 2 figures, 6 table

Experimental study on self-healing and mechanical properties of sisal fiber-loaded microbial concrete

Microbial concrete can make cracks self-healing, but the high alkalinity in concrete is not conducive to the survival and reproduction of microorganisms. In this study, using the porosity of the sisal fiber surface, microorganisms were immobilized on the sisal fiber, and the effects of several other microbial incorporation methods on the performance of self-healing concrete were compared. The fiber-loaded microbial concrete had the best self-healing effect, with a maximum self-healing width of 1.32 mm at 28 days. Splitting tensile strength is 28.7% greater than normal concrete, while compressive strength is 21.8% greater. The water absorption of sisal fiber enhanced the chloride permeability by 25.7%. Via microscopic examination, it was revealed that sisal fibers loaded a significant number of microorganisms and formed a large amount of calcium carbonate precipitation on the surface. Fiber-loaded microbial concrete’s elastic modulus and vickers hardness were 13% and 6% higher than normal concrete, respectively