Posterior-Aided Regularization for Likelihood-Free Inference

The recent development of likelihood-free inference aims training a flexible density estimator for the target posterior with a set of input-output pairs from simulation. Given the diversity of simulation structures, it is difficult to find a single unified inference method for each simulation model. This paper proposes a universally applicable regularization technique, called Posterior-Aided Regularization (PAR), which is applicable to learning the density estimator, regardless of the model structure. Particularly, PAR solves the mode collapse problem that arises as the output dimension of the simulation increases. PAR resolves this posterior mode degeneracy through a mixture of 1) the reverse KL divergence with the mode seeking property; and 2) the mutual information for the high quality representation on likelihood. Because of the estimation intractability of PAR, we provide a unified estimation method of PAR to estimate both reverse KL term and mutual information term with a single neural network. Afterwards, we theoretically prove the asymptotic convergence of the regularized optimal solution to the unregularized optimal solution as the regularization magnitude converges to zero. Additionally, we empirically show that past sequential neural likelihood inferences in conjunction with PAR present the statistically significant gains on diverse simulation tasks

Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder

The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contrast the what-if case of the opposite sensitive attribute. Along with this separation with the causality, a frequent assumption in the deep latent causal model defines a single latent variable to absorb the entire exogenous uncertainty of the causal graph. However, we claim that such structure cannot distinguish the 1) information caused by the intervention (i.e., sensitive variable) and 2) information correlated with the intervention from the data. Therefore, this paper proposes Disentangled Causal Effect Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling the exogenous uncertainty into two latent variables: either 1) independent to interventions or 2) correlated to interventions without causality. Particularly, our disentangling approach preserves the latent variable correlated to interventions in generating counterfactual examples. We show that our method estimates the total effect and the counterfactual effect without a complete causal graph. By adding a fairness regularization, DCEVAE generates a counterfactual fair dataset while losing less original information. Also, DCEVAE generates natural counterfactual images by only flipping sensitive information. Additionally, we theoretically show the differences in the covariance structures of DCEVAE and prior works from the perspective of the latent disentanglement

Advanced Technologies for Large-Sized OLED Display

Five years have passed, since the first 55″ full high-definition (FHD) OLED TV fabricated on Gen 8.5 glass was successfully launched into the TV market. For the time being, the size of OLED TV became diverse from 55″ to 77″, and the resolution was doubled into ultrahigh definition (UHD). The brightness and color gamut were enhanced, while the lower power consumption was realized. Utmost picture quality and slim form factor of OLED TV as well as the improved performance have made OLED TV recognized as the best premium TV. In this chapter, we describe the recent progress in three key technologies, which enable such an enhancement of performance in OLED TV, i.e., oxide thin-film transistor (TFT) and white organic light-emitting diode (WOLED), compensation circuit, and method to compensate the nonuniformity of oxide TFTs, OLED devices, and luminance

Examining the links between burnout and suicidal ideation in diverse occupations

IntroductionIt is uncertain whether burnout is associated with suicidal ideation among workers not in health care services. The aim of this study was to identify how burnout and suicidal ideation are linked among employees in various occupations and whether depression affects this link.MethodsThis cross-sectional study collected data from 12,083 participants aged 19–65 years from 25 companies and public institutions who underwent workplace mental health screening. Burnout and depression were assessed using both the Oldenburg Burnout Inventory and the Center for Epidemiologic Studies Depression Scale. Suicidal ideation was assessed by a self-rated questionnaire from the Korea National Health and Nutrition Examination Survey.ResultsExhaustion but not the cynicism dimension of burnout was associated with increased odds of suicidal ideation after adjustment for depression and other covariates (odds ratio [OR] = 1.47, 95% CI = 1.26–1.72). The association of exhaustion with suicidal ideation was significant in both depressed (OR = 1.36, 95% CI = 1.14–1.61) and not depressed (OR = 1.77, 95% CI = 1.13–2.76) participants. In exhausted participants, insufficient job control, an unfavorable occupational climate, low educational level, and depression were associated with increased odds of suicidal ideation.ConclusionExhaustion is linked with risk of suicidal ideation in employees not in health care service, regardless of depression status. Exhausted employees, particularly those having poor job resources, should be recognized as an at-risk group

Crystal structure of Hop2-Mnd1 and mechanistic insights into its role in meiotic recombination

In meiotic DNA recombination, the Hop2−Mnd1 complex promotes Dmc1-mediated single-stranded DNA (ssDNA) invasion into homologous chromosomes to form a synaptic complex by a yet-unclear mechanism. Here, the crystal structure of Hop2−Mnd1 reveals that it forms a curved rod-like structure consisting of three leucine zippers and two kinked junctions. One end of the rod is linked to two juxtaposed winged-helix domains, and the other end is capped by extra α-helices to form a helical bundle-like structure. Deletion analysis shows that the helical bundle-like structure is sufficient for interacting with the Dmc1-ssDNA nucleofilament, and molecular modeling suggests that the curved rod could be accommodated into the helical groove of the nucleofilament. Remarkably, the winged-helix domains are juxtaposed at fixed relative orientation, and their binding to DNA is likely to perturb the base pairing according to molecular simulations. These findings allow us to propose a model explaining how Hop2−Mnd1 juxtaposes Dmc1-bound ssDNA with distorted recipient double-stranded DNA and thus facilitates strand invasio