Markov-Switching Models with Evolving Regime-Specific Parameters: Are Post-War Booms or Recessions All Alike?

In this paper, we relax the assumption of constant regime-specific mean growth rates in Hamilton’s (1989) two-state Markov-switching model of the business cycle. We first present a benchmark model, in which each regime-specific mean growth rate evolves according to a random walk process over different episodes of booms or recessions. We then present a model with vector error correction dynamics for the regime-specific mean growth rates, by deriving and imposing a condition for the existence of a long-run equilibrium growth rate for real output. In the Bayesian Markov Chain Monte Carlo (MCMC) approach developed in this paper, the counterfactual priors, as well as the hierarchical priors for the regime-specific parameters, play critical roles. By applying the proposed approach to postwar U.S. real GDP growth (1947:Q4-2011:Q3), we uncover the evolving nature of the regime-specific mean growth rates of real output in the U.S. business cycle. An additional feature of the postwar U.S. business cycle that we uncover is a steady decline in the long-run equilibrium output growth. The decline started in the 1950s and ended in the 2000s. Our empirical results also provide partial, if not decisive, evidence that the central bank may have been more successful in restoring the economy back to its long-run equilibrium growth path after unusually severe recessions than after unusually good booms

Sources of Infestation, Biology, Damage by and control of Megaselia Rufipes Meigen (Diptera: Phoridae) on oil Palm Seeds

A study was carried out to identify sources of infestation by the Megaselia rufipes Meigen (Diptera: Phoridae) on some germinated oil palm seeds in the Seed Production facilities of the CSIR-Oil Palm Research Institute. The study also covered the identification of the pest, some aspects of the biology, cost of damage and control. It was observed that the sources of infestation was from poor sorting of seeds including broken and dead seeds mixed up with healthy seeds which were sent to the Germinator house from the Seed-store for initiation of germination process. Such unhealthy, poor quality seeds undergo fermentation during post-heating soaking process, emitting attractant(s). Adult flies which had hatched out in the Germinator room responded to the attractant(s) when such poor quality seeds were exposed in the open for air-drying. The flies’ activities caused damage to seeds including rotten kennels and seeds, empty shells and dead developing embryos in transparent polyethylene storage bags. The highest infestation was on 2052 seeds out of a total production of 582,503 germinated seeds in batch number 5 and the lowest was 223 seeds out of 241,089 seeds in batch number 6. Fermenting seeds among healthy germinating seeds in improper sealed/ broken storage polyethylene bags attracted adult flies which gained access to the seeds through holes. The fly was identified as Megaselia rufipes Meigen (Diptera: Phoridae). Its pupal duration was found to be 9-10 days. Bioassay results showed that the fly could be controlled effectively by dipping the seeds in Fenitrothion insecticide.Keywords: fly, insect pest, Megaselia rufipes, oil palm seed, infestatio

Meta-Learning with a Geometry-Adaptive Preconditioner

Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared initialization and the inner-loop process optimizes task-specific weights. Although MAML relies on the standard gradient descent in the inner-loop, recent studies have shown that controlling the inner-loop's gradient descent with a meta-learned preconditioner can be beneficial. Existing preconditioners, however, cannot simultaneously adapt in a task-specific and path-dependent way. Additionally, they do not satisfy the Riemannian metric condition, which can enable the steepest descent learning with preconditioned gradient. In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a preconditioner that is dependent on task-specific parameters, and its preconditioner can be shown to be a Riemannian metric. Thanks to the two properties, the geometry-adaptive preconditioner is effective for improving the inner-loop optimization. Experiment results show that GAP outperforms the state-of-the-art MAML family and preconditioned gradient descent-MAML (PGD-MAML) family in a variety of few-shot learning tasks. Code is available at: https://github.com/Suhyun777/CVPR23-GAP.Comment: Accepted at CVPR 2023. Code is available at: https://github.com/Suhyun777/CVPR23-GAP; This is an extended version of our previous CVPR23 wor

Improving disclosure of medical error through educational program as a first step toward patient safety

Participant’s Response to medical errors. Description of data: Raw data of participant’s response to medical errors (3 clinical cases with different severity of error outcome), satisfaction and change after the education program. (XLSX 18 kb