Effects of gaze on vection from jittering, oscillating, and purely radial optic flow

In this study, we examined the effects of different gaze types (stationary fixation, directed looking, or gaze shifting) and gaze eccentricities (central or peripheral) on the vection induced by jittering, oscillating, and purely radial optic flow. Contrary to proposals of eccentricity independence for vection (e.g., Post, 1988), we found that peripheral directed looking improved vection and peripheral stationary fixation impaired vection induced by purely radial flow (relative to central gaze). Adding simulated horizontal or vertical viewpoint oscillation to radial flow always improved vection, irrespective of whether instructions were to fixate, or look at, the center or periphery of the self-motion display. However, adding simulated high-frequency horizontal or vertical viewpoint jitter was found to increase vection only when central gaze was maintained. In a second experiment, we showed that alternating gaze between the center and periphery of the display also improved vection (relative to stable central gaze), with greater benefits observed for purely radial flow than for horizontally or vertically oscillating radial flow. These results suggest that retinal slip plays an important role in determining the time course and strength of vection. We conclude that how and where one looks in a self-motion display can significantly alter vection by changing the degree of retinal slip

Relative visual oscillation can facilitate visually induced self-motion perception

Adding simulated viewpoint jitter or oscillation to displays enhances visually induced illusions of self-motion (vection). The cause of this enhancement is yet to be fully understood. Here, we conducted psychophysical experiments to investigate the effects of different types of simulated oscillation on vertical vection. Observers viewed horizontally oscillating and nonoscillating optic flow fields simulating downward self-motion through an aperture. The aperture was visually simulated to be nearer to the observer and was stationary or oscillating in-phase or counter-phase to the direction of background horizontal oscillations of optic flow. Results showed that vection strength was modulated by the oscillation of the aperture relative to the background optic flow. Vertical vection strength increased as the relative oscillatory horizontal motion between the flow and the aperture increased. However, such increases in vection were only generated when the added oscillations were orthogonal to the principal direction of the optic flow pattern, and not when they occurred in the same direction. The oscillation effects observed in this investigation could not be explained by motion adaptation or different (motion parallax based) effects on depth perception. Instead, these results suggest that the oscillation advantage for vection depends on relative visual motion

Vection in depth during consistent and inconsistent multisensory stimulation

We examined vection induced during physical or simulated head oscillation along either the horizontal or depth axis. In the first two experiments, during active conditions, subjects viewed radial-flow displays which simulated viewpoint oscillation that was either in-phase or out-of-phase with their own tracked head movements. In passive conditions, stationary subjects viewed playbacks of displays generated in earlier active conditions. A third control, experiment was also conducted where physical and simulated fore ^ aft oscillation was added to a lamellar flow display. Consistent with ecology, when active in-phase horizontal oscillation was added to a radial-flow display it modestly improved vection compared to active out-of-phase and passive conditions. However, when active fore ^ aft head movements were added to either a radial-flow or a lamellar-flow display, both in-phase and out-of-phase conditions produced very similar vection. Our research shows that consistent multisensory input can enhance the visual perception of self- motion in some situations. However, it is clear that multisensory stimulation does not have to be consistent (ie ecological) to generate compelling vection in depth

A credit risk model for agricultural loan portfolios under the new Basel Capital Accord

The New Basel Capital Accord (Basel II) provides added emphasis to the development of portfolio credit risk models. An important regulatory change in Basel II is the differentiated treatment in measuring capital requirements for the corporate exposures and retail exposures. Basel II allows agricultural loans to be categorized and treated as the retail exposures. However, portfolio credit risk model for agricultural loans is still in their infancy. Most portfolio credit risk models being used have been developed for corporate exposures, and are not generally applicable to agricultural loan portfolio. The objective of this study is to develop a credit risk model for agricultural loan portfolios. The model developed in this study reflects characteristics of the agricultural sector, loans and borrowers and designed to be consistent with Basel II, including consideration given to forecasting accuracy and model applicability. This study conceptualizes a theory of loan default for farm borrowers. A theoretical model is developed based on the default theory with several assumptions to simplify the model. An annual default model is specified using FDIC state level data over the 1985 to 2003. Five state models covering Iowa, Illinois, Indiana, Kansas, and Nebraska areestimated as a logistic function. Explanatory variables for the model are a three-year moving average of net cash income per acre from crops, net cash income per cwt from livestock, government payments per acre, the unemployment rate, and a trend. Net cash income generated by state reflects the five major commodities: corn, soybeans, wheat, fed cattle, and hogs. A simulation model is developed to generate the stochastic default rates by state over the 2004 to 2007 period, providing the probability of default and the loan loss distribution in a pro forma context that facilitates proactive decision making. The model also generates expected loan loss, VaR, and capital requirements. This study suggests two key conclusions helpful to future credit risk modeling efforts for agricultural loan portfolios: (1) net cash income is a significant leading indicator to default, and (2) the credit risk model should be segmented by commodity and geographical location

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems

In this paper, we extend mean-field Langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. We propose mean-field Langevin averaged gradient (MFL-AG), a single-loop algorithm that implements gradient descent ascent in the distribution spaces with a novel weighted averaging, and establish average-iterate convergence to the mixed Nash equilibrium. We also study both time and particle discretization regimes and prove a new uniform-in-time propagation of chaos result which accounts for the dependency of the particle interactions on all previous distributions. Furthermore, we propose mean-field Langevin anchored best response (MFL-ABR), a symmetric double-loop algorithm based on best response dynamics with linear last-iterate convergence. Finally, we study applications to zero-sum Markov games and conduct simulations demonstrating long-term optimality.Comment: ICLR 2024 spotligh

t3t^3-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence

The variational autoencoder (VAE) typically employs a standard normal prior as a regularizer for the probabilistic latent encoder. However, the Gaussian tail often decays too quickly to effectively accommodate the encoded points, failing to preserve crucial structures hidden in the data. In this paper, we explore the use of heavy-tailed models to combat over-regularization. Drawing upon insights from information geometry, we propose $t^3$VAE, a modified VAE framework that incorporates Student's t-distributions for the prior, encoder, and decoder. This results in a joint model distribution of a power form which we argue can better fit real-world datasets. We derive a new objective by reformulating the evidence lower bound as joint optimization of KL divergence between two statistical manifolds and replacing with $\gamma$-power divergence, a natural alternative for power families. $t^3$VAE demonstrates superior generation of low-density regions when trained on heavy-tailed synthetic data. Furthermore, we show that $t^3$VAE significantly outperforms other models on CelebA and imbalanced CIFAR-100 datasets.Comment: ICLR 2024; 27 pages, 7 figures, 8 table