Q-matrix Misspecication Detection using Spectral Clustering on TIMSS 2011 Assessment Data

HonorsStatisticsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/169385/1/zhihguo.pd

TaylorAECNet: A Taylor Style Neural Network for Full-Band Echo Cancellation

This paper describes aecX team's entry to the ICASSP 2023 acoustic echo cancellation (AEC) challenge. Our system consists of an adaptive filter and a proposed full-band Taylor-style acoustic echo cancellation neural network (TaylorAECNet) as a post-filter. Specifically, we leverage the recent advances in Taylor expansion based decoupling-style interpretable speech enhancement and explore its feasibility in the AEC task. Our TaylorAECNet based approach achieves an overall mean opinion score (MOS) of 4.241, a word accuracy (WAcc) ratio of 0.767, and ranks 5th in the non-personalized track (track 1)

INFLUENCE OF WALL SCATTERING EFFECT ON ELECTRONS GAS DYNAMICS PARAMETERS IN ELECTRIC PROPULSION THRUSTERS WITH CLOSED ELECTRON DRIFT

The analysis is represented of some works devoted to the mathematical modeling of processes in plasma-ion thrusters and Hall effect thrusters. It is shown that the common in these works is the use of approximate forms of the equations of gas dynamics, which are applicable to the description of relatively dense gases, but not to analyze the processes in the rarefied plasma of electric propulsion thrusters. As a result, the above mathematical models do not represent the processes that are significantly responsible for the values of the thruster operating parameters.Authors try to partially correct this drawback by insertion into the initial approximate forms of the equations written for a point in the plasma volume, the parameters that actually represent the boundary effects and should be written not in the equations of gas dynamics themselves, but in the boundary conditions for these equations.The most complete forms of the necessary equations are given in this paper. It is shown that it is necessary to take into account electrons thermal conductivity as well as at least one (radial-azimuth) component of viscosity tensor to describe the "wall scattering" effect.It is concluded that the most productive approach in mathematical modeling is to write the most complete forms of equations with their subsequent simplification – removing the terms responsible for the processes recognized on the basis of primary numerical estimates as such, which can be neglected.The analysis is represented of some works devoted to the mathematical modeling of processes in plasma-ion thrusters and Hall effect thrusters. It is shown that the common in these works is the use of approximate forms of the equations of gas dynamics, which are applicable to the description of relatively dense gases, but not to analyze the processes in the rarefied plasma of electric propulsion thrusters. As a result, the above mathematical models do not represent the processes that are significantly responsible for the values of the thruster operating parameters.Authors try to partially correct this drawback by insertion into the initial approximate forms of the equations written for a point in the plasma volume, the parameters that actually represent the boundary effects and should be written not in the equations of gas dynamics themselves, but in the boundary conditions for these equations.The most complete forms of the necessary equations are given in this paper. It is shown that it is necessary to take into account electrons thermal conductivity as well as at least one (radial-azimuth) component of viscosity tensor to describe the "wall scattering" effect.It is concluded that the most productive approach in mathematical modeling is to write the most complete forms of equations with their subsequent simplification – removing the terms responsible for the processes recognized on the basis of primary numerical estimates as such, which can be neglected

Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.Comment: 7 pages, Accepted by CIKM23 Applied Research Trac

Role of Securin, Separase and Cohesins in female meiosis and polar body formation in Drosophila

Chromosome segregation in meiosis is controlled by a conserved pathway that culminates in Separase-mediated cleavage of the α-kleisin Rec8, leading to dissolution of cohesin rings. Drosophila has no gene encoding Rec8, and the absence of a known Separase target raises the question of whether Separase and its regulator Securin (Pim in Drosophila) are important in Drosophila meiosis. Here, we investigate the role of Securin, Separase and the cohesin complex in female meiosis using fluorescence in situ hybridization against centromeric and arm-specific sequences to monitor cohesion. We show that Securin destruction and Separase activity are required for timely release of arm cohesion in anaphase I and centromere-proximal cohesion in anaphase II. They are also required for release of arm cohesion on polar body chromosomes. Cohesion on polar body chromosomes depends on the cohesin components SMC3 and the mitotic α-kleisin Rad21 (also called Vtd in Drosophila). We provide cytological evidence that SMC3 is required for arm cohesion in female meiosis, whereas Rad21, in agreement with recent findings, is not. We conclude that in Drosophila meiosis, cohesion is regulated by a conserved Securin–Separase pathway that targets a diverged Separase target, possibly within the cohesin complex

Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings.Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices. The proposed model first builds different orders of adjacency matrices from manually designed node connections. After that, an intact multi-order adjacency matrix is attached from the automatic fusion of various orders of adjacency matrices. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we utilize a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors