Neighborhood Effects on Economic Outcomes of Youths

This paper analyzes the long-term neighborhood effects on incomes of youths in the United States. The working dataset comes from the National Longitudinal Survey of the Youths and consists of the youths between the age of 12 and 14 in 1997. Their neighborhood characteristics between 1998 and 2001 are analyzed to predict their household incomes in 2015. Using two-stage probit models and OLS regressions, this paper suggests two main findings. First, the youths in safe neighborhoods between 1998 and 2001 are 81.5 percentage- point more likely than the youths from risky neighborhoods to earn above the median household income in 2015. Also, living in safe neighborhoods increases the youths’ incomes in 2015 by 221.2 percentage- point. These results show that the neighborhood environment in which the individuals grow up as youths has a significant impact on their adulthood incomes. As a result, this paper supports policy initiatives that actively seek to assist low-income families to move into affluent neighborhoods to break the poverty trap

Transient Simulation of Secondary Loop Mobile Air Conditioning Systems

Since the conventional refrigerant R-134a is being phased down due to its high GWP, finding a suitable replacement refrigerant with low GWP and a system design is of great importance. However, most of the alternatives are either flammable or more expensive. Therefore, to ensure the safety of passenger and reduce the refrigerant charge, a secondary loop system with coolant loop on both condenser- and evaporator-side was proposed. In the secondary loop system, the evaporator and condenser exchange heat with air through cabin cooler and radiator, respectively. The secondary loop system has more advantages than the direct expansion system such as easy applicability of heat pump operation and battery thermal management. In this study, transient models were developed for both direct expansion system and secondary loop system in Dymola. The simulation results show that the coefficient of performance of the secondary loop system is lower than that of direct expansion system due to high pressure ratio and high compressor revolution speed when two types of systems provide similar cooling capacity. Moreover, the performances of the system using R-134a, R-152a, and R-1234yf were evaluated and compared to that of conventional direct expansion system using R-134a under the US06 driving cycle condition. Though large fluctuation is observed on the condenser capacity in the direct expansion system, the evaporator capacity is very stable. In overall, R-152a has better performance than R-1234yf and is a good candidate as an alternative refrigerant but the secondary system needs more efficiency enhancement options to compete with current R-134a direct expansion system

EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning

We propose EnCLAP, a novel framework for automated audio captioning. EnCLAP employs two acoustic representation models, EnCodec and CLAP, along with a pretrained language model, BART. We also introduce a new training objective called masked codec modeling that improves acoustic awareness of the pretrained language model. Experimental results on AudioCaps and Clotho demonstrate that our model surpasses the performance of baseline models. Source code will be available at https://github.com/jaeyeonkim99/EnCLAP . An online demo is available at https://huggingface.co/spaces/enclap-team/enclap .Comment: Accepted to ICASSP 202

Nonverbal Social Behavior Generation for Social Robots Using End-to-End Learning

To provide effective and enjoyable human-robot interaction, it is important for social robots to exhibit nonverbal behaviors, such as a handshake or a hug. However, the traditional approach of reproducing pre-coded motions allows users to easily predict the reaction of the robot, giving the impression that the robot is a machine rather than a real agent. Therefore, we propose a neural network architecture based on the Seq2Seq model that learns social behaviors from human-human interactions in an end-to-end manner. We adopted a generative adversarial network to prevent invalid pose sequences from occurring when generating long-term behavior. To verify the proposed method, experiments were performed using the humanoid robot Pepper in a simulated environment. Because it is difficult to determine success or failure in social behavior generation, we propose new metrics to calculate the difference between the generated behavior and the ground-truth behavior. We used these metrics to show how different network architectural choices affect the performance of behavior generation, and we compared the performance of learning multiple behaviors and that of learning a single behavior. We expect that our proposed method can be used not only with home service robots, but also for guide robots, delivery robots, educational robots, and virtual robots, enabling the users to enjoy and effectively interact with the robots.Comment: 10 pages, 7 figures, 3 tables, submitted to the International Journal of Robotics Research (IJRR