Hyung Don Ryoo: A healthy career in cellular death

Ryoo engages the power of the fly to study apoptosis during development and disease

Learning Latent Super-Events to Detect Multiple Activities in Videos

In this paper, we introduce the concept of learning latent super-events from activity videos, and present how it benefits activity detection in continuous videos. We define a super-event as a set of multiple events occurring together in videos with a particular temporal organization; it is the opposite concept of sub-events. Real-world videos contain multiple activities and are rarely segmented (e.g., surveillance videos), and learning latent super-events allows the model to capture how the events are temporally related in videos. We design temporal structure filters that enable the model to focus on particular sub-intervals of the videos, and use them together with a soft attention mechanism to learn representations of latent super-events. Super-event representations are combined with per-frame or per-segment CNNs to provide frame-level annotations. Our approach is designed to be fully differentiable, enabling end-to-end learning of latent super-event representations jointly with the activity detector using them. Our experiments with multiple public video datasets confirm that the proposed concept of latent super-event learning significantly benefits activity detection, advancing the state-of-the-arts.Comment: CVPR 201

Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression

We design a new approach that allows robot learning of new activities from unlabeled human example videos. Given videos of humans executing the same activity from a human's viewpoint (i.e., first-person videos), our objective is to make the robot learn the temporal structure of the activity as its future regression network, and learn to transfer such model for its own motor execution. We present a new deep learning model: We extend the state-of-the-art convolutional object detection network for the representation/estimation of human hands in training videos, and newly introduce the concept of using a fully convolutional network to regress (i.e., predict) the intermediate scene representation corresponding to the future frame (e.g., 1-2 seconds later). Combining these allows direct prediction of future locations of human hands and objects, which enables the robot to infer the motor control plan using our manipulation network. We experimentally confirm that our approach makes learning of robot activities from unlabeled human interaction videos possible, and demonstrate that our robot is able to execute the learned collaborative activities in real-time directly based on its camera input

Exchange Rate Fluctuations, Financing Constraints, Hedging, and Exports: Evidence from Firm Level Data

An important puzzle in international macroeconomics is the exchange rate disconnect puzzle. Nominal exchange rates seem to be unrelated to other macroeconomic variables, for example, export quantities. This paper uses Japanese firm level data to examine whether exchange rate fluctuations are strongly related to the export quantities of firms. We build a simultaneous nonlinear structural model with external financing costs, and estimate the model on 14 separate Japanese 4 digit level industries. We find that export volumes at the firm level are significantly affected by exchange rate fluctuations. We find higher elasticities of exports with respect to exchange rates than in previous work. Our results cast some doubt on the prevailing wisdom that exchange rates have no effect on trade. Finally, we find in our data that financing constraints play an important role in affecting the sensitivity of exports to exchange rate fluctuations. Firms that are less financially constrained -for example, keiretsu firms- tend to have lower exchange rate elasticities, which is consistent with our model.

Public debt in an OLG model with imperfect competition

Fiscal policy is needed to avoid dynamic inefficiency and maintain full employment in a modified Diamond OLG model with imperfect competition. A distributionally neutral tax scheme can maintain full employment in the face of variations in .household confidence.. No variations in taxes will be needed if households correctly anticipate future taxes: the tax policy functions as an insurance scheme. JEL Categories: E62, E22Public debt, Keynesian OLG model, dynamic effeciency, confidence.

Macroeconomic implications of financialization

A growing literature suggests that 'financialization' may weaken the performance of non-financial corporations and constrain the growth of ag- gregate demand. This paper evaluates (some of) the claims that have been made using two alternative approaches (one derived from Skott (1981, 1988, 1989) and one from Lavoie and Godley (2001-2002)) and two differ- ent settings (a labor-constrained setting and a dual-economy setting). All models pay explicit attention to financial stock-flow relations. The results are insensitive to the precise specification of household saving behavior but depend critically on the labor market assumptions (labor-constrained vs dual) and the specification of the investment function (Harrodian vs stagnationist). JEL Categories: E12, E21, E44financialization, stock-flow consistency, retention rate, ex- ternal finance, new issues.