Welfare Reform and Labor Participation: Are There Urban and Rural Differences?

Although welfare reform began in 1996 at the national level, Iowa was one of the earliest states to obtain a waiver to initiate the Iowa Family Investment Program (FIP) in 1993. To gain a better understanding of welfare recidivism, we use Iowa administrative quarterly data between October1993 and September 1995, impute the education attainment for the caseheads with missing education attainment using fractional imputation and study the factors that affect the probability of working, the potential wage for the caseheads and the possibility of leaving FIP based on the potential wage. We find higher education (i.e. higher skills) leads to higher labor force participation, especially for single-mothers with children. Metro or urban location is associated with the probability of working and potential wage earnings, but has no effect on FIP participation. The local unemployment rate does not affect labor participation of low-income individuals, but does affect the potential wage and FIP status. Those with lower education, and nonwhites are more affected by the local labor market environment than others. If an individual moves once in a year, he or she will earn more money than in the original job; no gains are achieved through moving more than once. The possibility of leaving FIP is relatively high if there is only one move.Labor and Human Capital,

Cardiac-derived CTRP9 protects against myocardial ischemia/reperfusion injury via calreticulin-dependent inhibition of apoptosis.

Cardiokines play an essential role in maintaining normal cardiac functions and responding to acute myocardial injury. Studies have demonstrated the heart itself is a significant source of C1q/TNF-related protein 9 (CTRP9). However, the biological role of cardiac-derived CTRP9 remains unclear. We hypothesize cardiac-derived CTRP9 responds to acute myocardial ischemia/reperfusion (MI/R) injury as a cardiokine. We explored the role of cardiac-derived CTRP9 in MI/R injury via genetic manipulation and a CTRP9-knockout (CTRP9-KO) animal model. Inhibition of cardiac CTRP9 exacerbated, whereas its overexpression ameliorated, left ventricular dysfunction and myocardial apoptosis. Endothelial CTRP9 expression was unchanged while cardiomyocyte CTRP9 levels decreased after simulated ischemia/`reperfusion (SI/R) in vitro. Cardiomyocyte CTRP9 overexpression inhibited SI/R-induced apoptosis, an effect abrogated by CTRP9 antibody. Mechanistically, cardiac-derived CTRP9 activated anti-apoptotic signaling pathways and inhibited endoplasmic reticulum (ER) stress-related apoptosis in MI/R injury. Notably, CTRP9 interacted with the ER molecular chaperone calreticulin (CRT) located on the cell surface and in the cytoplasm of cardiomyocytes. The CTRP9-CRT interaction activated the protein kinase A-cAMP response element binding protein (PKA-CREB) signaling pathway, blocked by functional neutralization of the autocrine CTRP9. Inhibition of either CRT or PKA blunted cardiac-derived CTRP9\u27s anti-apoptotic actions against MI/R injury. We further confirmed these findings in CTRP9-KO rats. Together, these results demonstrate that autocrine CTRP9 of cardiomyocyte origin protects against MI/R injury via CRT association, activation of the PKA-CREB pathway, ultimately inhibiting cardiomyocyte apoptosis

TIPS AND TRAPS: METHODOLOGICAL ISSUES RELATED TO CONDUCTING AN ONLINE TRIP PLANNING STUD

This study examines methodological issues related to studying American college students’ online travel planning behavior. A mixed method approach, which integrated think-aloud protocol combined with the process tracing method, log analysis and survey techniques, was evaluated in this paper. In previous studies, this methodological approach has been subject to issues of reliability and validity that were addressed in the design of the current study. Specifically, questions were related to whether the experiment used in this study would yield results comparable to the decision-making and search processes respondents would engage upon on their real-life trip planning. The purpose of this paper was to discuss how this study has evolved from previous work in this area in order to increase reliability and validity. Issues regarding the artificiality of the environment are addressed in this paper. Overall it was found that the qualitative- based, mixed-method approach used in this study was deemed appropriate and has resulted in delivering insights into the phenomena of online travel planning

HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks

Portrait stylization is a long-standing task enabling extensive applications. Although 2D-based methods have made great progress in recent years, real-world applications such as metaverse and games often demand 3D content. On the other hand, the requirement of 3D data, which is costly to acquire, significantly impedes the development of 3D portrait stylization methods. In this paper, inspired by the success of 3D-aware GANs that bridge 2D and 3D domains with 3D fields as the intermediate representation for rendering 2D images, we propose a novel method, dubbed HyperStyle3D, based on 3D-aware GANs for 3D portrait stylization. At the core of our method is a hyper-network learned to manipulate the parameters of the generator in a single forward pass. It not only offers a strong capacity to handle multiple styles with a single model, but also enables flexible fine-grained stylization that affects only texture, shape, or local part of the portrait. While the use of 3D-aware GANs bypasses the requirement of 3D data, we further alleviate the necessity of style images with the CLIP model being the stylization guidance. We conduct an extensive set of experiments across the style, attribute, and shape, and meanwhile, measure the 3D consistency. These experiments demonstrate the superior capability of our HyperStyle3D model in rendering 3D-consistent images in diverse styles, deforming the face shape, and editing various attributes

Alleviating the Long-Tail Problem in Conversational Recommender Systems

Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, \ie a large proportion of items are rarely (or even never) mentioned in the conversations, which are called long-tail items. As a result, the CRSs trained on these datasets tend to recommend frequent items, and the diversity of the recommended items would be largely reduced, making users easier to get bored. To address this issue, this paper presents \textbf{LOT-CRS}, a novel framework that focuses on simulating and utilizing a balanced CRS dataset (\ie covering all the items evenly) for improving \textbf{LO}ng-\textbf{T}ail recommendation performance of CRSs. In our approach, we design two pre-training tasks to enhance the understanding of simulated conversation for long-tail items, and adopt retrieval-augmented fine-tuning with label smoothness strategy to further improve the recommendation of long-tail items. Extensive experiments on two public CRS datasets have demonstrated the effectiveness and extensibility of our approach, especially on long-tail recommendation.Comment: work in progres