The ProfessionAl Go annotation datasEt (PAGE)

The game of Go has been highly under-researched due to the lack of game records and analysis tools. In recent years, the increasing number of professional competitions and the advent of AlphaZero-based algorithms provide an excellent opportunity for analyzing human Go games on a large scale. In this paper, we present the ProfessionAl Go annotation datasEt (PAGE), containing 98,525 games played by 2,007 professional players and spans over 70 years. The dataset includes rich AI analysis results for each move. Moreover, PAGE provides detailed metadata for every player and game after manual cleaning and labeling. Beyond the preliminary analysis of the dataset, we provide sample tasks that benefit from our dataset to demonstrate the potential application of PAGE in multiple research directions. To the best of our knowledge, PAGE is the first dataset with extensive annotation in the game of Go. This work is an extended version of [1] where we perform a more detailed description, analysis, and application.Comment: Journal version of arXiv:2205.00254, under revie

INVESTIGATION OF SMOOTH MUSCLE CELL DEATH AND GENOME INSTABILITY IN HUTCHINSON-GILFORD PROGERIA SYNDROME

Hutchinson–Gilford progeria syndrome (HGPS) is a severe human premature aging disorder caused by a lamin A mutant named progerin. Death occurs at a mean age of 13 y from cardiovascular problems. Previous studies revealed loss of vascular smooth muscle cells (SMCs) from large arteries in HGPS patient and mouse models, suggesting a causal connection between SMC loss and cardiovascular malfunction. The primary aim of this dissertation is to elucidate the molecular mechanisms underlying the massive SMC loss in HGPS. To study this, I develop an in vitro differentiation protocol to generate HGPS SMCs from induced pluripotent stem cells (iPSCs). My results indicate that HGPS SMCs exhibit a profound cell death phenotype, potentially recapitulating the in vivo SMC loss. Mechanistically, I find that HGPS SMCs bear deficient homologous recombination (HR). In addition, progerin accumulation strongly suppresses PARP1 and consequently triggers an activation of the error-prone non-homologous end joining (NHEJ) response during S/G2 phase. As a result, HGPS SMCs exhibit prolonged mitosis and mitotic catastrophe. Mis-regulated DNA damage response (DDR) is proposed to induce genome instability and various cellular phenotypes in HGPS, including HGPS SMC cell death. To better understand HGPS DDR misregulation, I examine HR and NHEJ in HGPS fibroblasts at different cell cycle phases. My analysis indicates that HR is deficient in S/G2 phase, whereas NHEJ, the dominant G0/G1 phase DDR pathway, is impaired in G0/G1 phase but active in S/G2 phase HGPS fibroblasts. The mis-regulation of HR and NHEJ may jeopardize genome integrity in both G0/G1 and S/G2 phase HGPS cells. Mechanistic study reveals that H2AX, a crucial upstream DDR signal, is reduced in G0/G1 but normal in S/G2 phase HGPS cells, implicating a potential cause of the cell cycle-dependent NHEJ mis-regulation. Furthermore, this reduction is correlated with impaired ATM activation and loss of H3K9me3 in HGPS. Restoration of H3K9me3 by methylene blue treatment can stimulate ATM activity, improve H2AX signaling and rescue NHEJ in G0/G1 phase HGPS cells. This dissertation not only is the first mechanistic study on HGPS SMC loss but also provides a molecular basis and therapeutic approach for the HGPS DDR deficiencies

Times of Uncertainty: The Psychological and Behavioral Impact of Employment Uncertainty on Furloughed Workers and the Moderating Effect of Work Orientation

Although furloughs have been used by organizations for some time, their use increased sharply during the COVID-19 pandemic. They differ from layoffs in the uncertainty they involve around the employment relationship. However, the phenomenon has received little attention from research on involuntary job loss, and the impact of the employment uncertainty it involves is largely unknown. Furthermore, the moderating factors that differentiate the impacts across employee populations are also unclear. In this dissertation I report a mixed-method field study examining the impact of employment uncertainty on furloughed workers and the moderating role by their work orientation. To guide the development of hypotheses, I conduct a qualitative analysis of semi-structured interviews with 28 furloughed employees. I then test my predictions with furloughed workers from various industries. Results suggest that employment uncertainty increases furloughed workers’ negative emotions while decreasing their occupational commitment. The behavioral impacts of uncertainty include hedging and “live like working,” mediated by occupational commitment. Furthermore, one’s work orientation moderates the adverse impacts of uncertainty such that the effects are alleviated for someone with a stronger sense of calling orientation but worsened for someone with a stronger sense of job orientation. The theoretical and practical implications of the findings are discussed

Exploring Timbre Disentanglement in Non-Autoregressive Cross-Lingual Text-to-Speech

In this paper, we present a FastPitch-based non-autoregressive cross-lingual Text-to-Speech (TTS) model built with language independent input representation and monolingual force aligners. We propose a phoneme length regulator that solves the length mismatch problem between language-independent phonemes and monolingual alignment results. Our experiments show that (1) an increasing number of training speakers encourages non-autoregressive cross-lingual TTS model to disentangle speaker and language representations, and (2) variance adaptors of FastPitch model can help disentangle speaker identity from learned representations in cross-lingual TTS. The subjective evaluation shows that our proposed model is able to achieve decent speaker consistency and similarity. We further improve the naturalness of Mandarin-dominated mixed-lingual utterances by utilizing the controllability of our proposed model.Comment: Submitted to ICASSP 202

Multi-Dimensional Modeling of Charring Ablators

Re-entry of a spacecraft occurs at the hypersonic regime where the flow field is extremely complex: high temperature gradients occurring in the shock-layer region ionize and dissociate the air. Even if a large portion of heat generated during this process is convected away in the surrounding air, a fraction of it is still transferred to the vehicle. Therefore, it is important to protect the vehicle with a suitable kind of shielding. Of the many techniques available today, use of ablative material is gaining popularity. The basic idea behind an ablating heat shield is that the energy incident on the spacecraft is used to vaporized the material, thus preventing a significant part of the heat to be transferred into the structure. The available literature indicates that most of the past investigations either do not consider the actual physical processes taking place during ablation, or are limited to a one-dimensional model. The present investigation shows the development of a numerical model for simulating the multi-dimensional heat transfer phenomena that occurred in a typical ablative TPS. The newly developed model is verified using closed form analytical solutions and validated with available data. This effort consists of the first steps of an ongoing project to develop a comprehensive multi-scale, multi-physics and multi-dimensional material response code aimed at modeling charring and surface ablators

MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients

Overall survival (OS) time is one of the most important evaluation indices for gliomas situations. Multimodal Magnetic Resonance Imaging (MRI) scans play an important role in the study of glioma prognosis OS time. Several deep learning-based methods are proposed for the OS time prediction on multi-modal MRI problems. However, these methods usually fuse multi-modal information at the beginning or at the end of the deep learning networks and lack the fusion of features from different scales. In addition, the fusion at the end of networks always adapts global with global (eg. fully connected after concatenation of global average pooling output) or local with local (eg. bilinear pooling), which loses the information of local with global. In this paper, we propose a novel method for multi-modal OS time prediction of brain tumor patients, which contains an improved nonlocal features fusion module introduced on different scales. Our method obtains a relative 8.76% improvement over the current state-of-art method (0.6989 vs. 0.6426 on accuracy). Extensive testing demonstrates that our method could adapt to situations with missing modalities. The code is available at https://github.com/TangWen920812/mmmna-net.Comment: Accepted EMBC 202

A Dynamic Credit Evaluation Approach Using Sensitivity-Optimized Weights for Supply Chain Finance

Supply chain financing provides important funding channels for micro and small enterprises (MSEs), but effectively evaluating their creditworthiness remains challenging. Past methods overly rely on static financial indicators and subjective judgment in determining credit evaluation weights. This study proposes a dynamic credit evaluation approach that uses sensitivity analysis to optimize the weighting scheme. An indicator system is constructed based on the unique characteristics of e-commerce MSEs. The weight optimization integrates subjective, objective, and sensitivity-based methods to reflect specific financing scenarios. A system dynamics model simulates the credit evaluation mechanism and identifies the sensitivity of each influencing factor. The resultant comprehensive weights are applied in a TOPSIS-GRA dynamic evaluation model to assess MSE credit levels over time. An empirical analysis of 20 online stores demonstrates the proposed model\u27s advantages in accurately revealing credit rankings relative to conventional static models. This research provides an effective data-driven weighting technique and dynamic evaluation framework for supply chain finance credit assessment

MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices

We present MovePose, an optimized lightweight convolutional neural network designed specifically for real-time body pose estimation on CPU-based mobile devices. The current solutions do not provide satisfactory accuracy and speed for human posture estimation, and MovePose addresses this gap. It aims to maintain real-time performance while improving the accuracy of human posture estimation for mobile devices. The network produces 17 keypoints for each individual at a rate exceeding 11 frames per second, making it suitable for real-time applications such as fitness tracking, sign language interpretation, and advanced mobile human posture estimation. Our MovePose algorithm has attained an Mean Average Precision (mAP) score of 67.7 on the COCO \cite{cocodata} validation dataset. The MovePose algorithm displayed efficiency with a performance of 69+ frames per second (fps) when run on an Intel i9-10920x CPU. Additionally, it showcased an increased performance of 452+ fps on an NVIDIA RTX3090 GPU. On an Android phone equipped with a Snapdragon 8 + 4G processor, the fps reached above 11. To enhance accuracy, we incorporated three techniques: deconvolution, large kernel convolution, and coordinate classification methods. Compared to basic upsampling, deconvolution is trainable, improves model capacity, and enhances the receptive field. Large kernel convolution strengthens these properties at a decreased computational cost. In summary, MovePose provides high accuracy and real-time performance, marking it a potential tool for a variety of applications, including those focused on mobile-side human posture estimation. The code and models for this algorithm will be made publicly accessible