Unemployment Insurance and Labor Market Transitions: Evidence from the Covid-19 Pandemic

To help the unemployed, the Federal Government expanded the weekly amount (Federal Pandemic Unemployment Compensation) and potential benefit duration (Pandemic Emergency Unemployment Compensation) of Unemployment Insurance (UI) benefits during the pandemic, along with the eligibility for benefits (Pandemic Unemployment Assistance). While these programs were set to expire in September 2021, around half of the states terminated some or all programs in advance. This thesis investigates whether the termination of each temporary UI program incentivized re-employment using difference-in-difference estimations. I document four new facts. Firstly, the early termination of all programs in June resulted in a 6-7 percentage point increase in the Unemployment-toEmployment (U-E) transition rates. The early termination of Federal Pandemic Unemployment Compensation (FPUC2) alone had negligible effects. Secondly, under strong assumptions, terminating Pandemic Emergency Unemployment Compensation and Pandemic Unemployment Assistance in advance (in addition to FPUC2) caused a roughly 5 percentage point increase in U-E transition rates. Thirdly, the employment effects of the early termination were short-termed and diminished by the end of 2021. Finally, the scheduled termination of these programs did not increase the U-E flow. Overall, the results infer that UI’s expanded eligibility and longer potential duration, not the high benefit amount, reduced re-employment in 2021.Bachelor of Scienc

明代版本挿絵芸術の研究 -『西廂記』を中心として

早稲田大学博士(文学)早大学位記番号:新9359doctoral thesi

Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. However, PS model still suffers from long acquisition time and even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term, and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on public dataset and prospective experiments on in-vivo data. The proposed method was compared with four competing algorithms based on PS model, and two non-PS model methods. Main results. The proposed method has robust performance against shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+Sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. Accelerated PS model has the potential to save much time for clinical dynamic MRI examination, and is promising for real-time MRI applications.Comment: 23 pages, 11 figures. Accepted as manuscript on Physics in Medicine & Biolog

Real-Time Data Transmission Scheduling Algorithm for Wireless Sensor Networks Based on Deep Q-Learning

In the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the network performance of WSNs is to schedule the transmission process. An effective scheduling algorithm can meet the requirements of a strict industrial environment for network performance, which is of great research significance. Aiming at the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed. The algorithm comprehensively considers the influence of the remaining deadline, remaining hops, and unassigned time-slot nodes in the data transmission process, defines the reward function and action selection strategy of Q-learning, and guides the system state information transfer process. At the same time, deep learning and Q-learning are combined to solve the problem of disaster maintenance caused by the large scale of the system state. A multi-layer Stacked Auto Encoder (SAE) network model establishes the state-action mapping relationship, and the Q-learning algorithm updates it. Finally, according to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained. The network performance of the proposed data transmission scheduling algorithm is analyzed and evaluated by simulation experiments. The simulation results show that compared with the commonly used heuristic algorithms, the proposed algorithm improves real-time performance and can better meet the data transmission requirements of high reliability and real-time WSNs

Real-time phase-contrast flow cardiovascular magnetic resonance with low-rank modeling and parallel imaging

Background: Conventional phase-contrast cardiovascular magnetic resonance (PC-CMR) employs cine-based acquisitions to assess blood flow condition, in which electro-cardiogram (ECG) gating and respiration control are generally required. This often results in lower acquisition efficiency, and limited utility in the presence of cardiovascular pathology (e.g., cardiac arrhythmia). Real-time PC-CMR, without ECG gating and respiration control, is a promising alternative that could overcome limitations of the conventional approach. But real-time PC-CMR involves image reconstruction from highly undersampled (k, t)-space data, which is very challenging. In this study, we present a novel model-based imaging method to enable high-resolution real-time PC-CMR with sparse sampling. Methods: The proposed method captures spatiotemporal correlation among flow-compensated and flow-encoded image sequences with a novel low-rank model. The image reconstruction problem is then formulated as a low-rank matrix recovery problem. With proper temporal subspace modeling, it results in a convex optimization formulation. We further integrate this formulation with the SENSE-based parallel imaging model to handle multichannel acquisitions. The performance of the proposed method was systematically evaluated in 2D real-time PC-CMR with flow phantom experiments and in vivo experiments (with healthy subjects). Additionally, we performed a feasibility study of the proposed method on patients with cardiac arrhythmia. Results: The proposed method achieves a spatial resolution of 1.8 mm and a temporal resolution of 18 ms for 2D real-time PC-CMR with one directional flow encoding. For the flow phantom experiments, both regular and irregular flow patterns were accurately captured. For the in vivo experiments with healthy subjects, flow dynamics obtained from the proposed method correlated well with those from the cine-based acquisitions. For the experiments with the arrhythmic patients, the proposed method demonstrated excellent capability of resolving the beat-by-beat flow variations, which cannot be obtained from the conventional cine-based method. Conclusion: The proposed method enables high-resolution real-time PC-CMR at 2D without ECG gating and respiration control. It accurately resolves beat-by-beat flow variations, which holds great promise for studying patients with irregular heartbeats. Electronic supplementary material The online version of this article (doi:10.1186/s12968-017-0330-1) contains supplementary material, which is available to authorized users

Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction

Personnel performance is important for the high-technology industry to ensure its core competitive advantages are present. Therefore, predicting personnel performance is an important research area in human resource management (HRM). In this paper, to improve prediction performance, we propose a novel framework for personnel performance prediction to help decision-makers to forecast future personnel performance and recruit the best suitable talents. Firstly, a hybrid convolutional recurrent neural network (CRNN) model based on self-attention mechanism is presented, which can automatically learn discriminative features and capture global contextual information from personnel performance data. Moreover, we treat the prediction problem as a classification task. Then, the k-nearest neighbor (KNN) classifier was used to predict personnel performance. The proposed framework is applied to a real case of personnel performance prediction. The experimental results demonstrate that the presented approach achieves significant performance improvement for personnel performance compared to existing methods

Solvent-Type Passivation Strategy Controls Solid-State Self-Quenching-Resistant Behavior in Sulfur Dots

Treatment of sulfur dots with polyethylene glycol (PEG) has been an efficient way to achieve a high luminescence quantum yield, and such a PEG-related quantum dot (QD)-synthesis strategy has been well documented. However, the polymeric insulating capping layer acting as the “thick shell” will significantly slow down the electron-transfer efficiency and severely hamper its practical application in an optoelectric field. Especially, the employment of synthetic polymers with long alkyl chains or large molecular weights may lead to structural complexity or even unexpected changes of physical characteristics for QDs. Therefore, in sulfur dot preparation, it is a breakthrough to use short-chain molecular species to replace PEG for better control and reproducibility. In this article, a solvent-type passivation (STP) strategy has been reported, and no PEG or any other capping agent is required. The main role of the solvent, ethanol, is to directly react with NaOH, and the generated sodium ethoxide passivates the surface defects. The afforded STP-enhanced emission sulfur dots (STPEE-SDs) possess not only the self-quenching-resistant feature in the solid state but also the extension of fluorescence band toward the wavelength as long as 645 nm. The realization of sulfur dot emission in the deep-red region with a decent yield (8.7%) has never been reported. Moreover, a super large Stokes shift (300 nm, λex = 345 nm, λem = 645 nm) and a much longer decay lifetime (109 μs) have been found, and such values can facilitate to suppress the negative influence from background signals. Density functional theory demonstrates that the surface passivation via sodium ethoxide is dynamically favorable, and the spectroscopic insights into emission behavior could be derived from the passivation effect of the sulfur vacancy as well as the charge-transfer process dominated by the highly electronegative ethoxide layer