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Environmentalists are more concerned with the environment in this age of industrialization, and they are continually interested in researching factors that can facilitate the transition towards sustainability. This study applies an econometric technique called the panel Generalized Method of Moments generalized moments to analyze green finance and renewable energy’s impact on CO2 emissions from 2010 to 2019. According to the findings, green finance has a significant negative and positive impact on carbon emissions and green economic recovery. In addition, the results showed that logistics operations use energy and fossil fuel, and the findings also showed that the amount of fossil fuel and non-green energy sources creates a significant harmful effect on the environmental sustainability, in addition to having a negative impact on economic growth. Inadequate transportation-related infrastructure and logistics services are other significant contributors to CO2 and overall emissions of greenhouse gases. According to the findings, sustainable energy development can be advanced by fostering the growth of green finance. This can be accomplished by employing a variety of metrics that pertain to the three dimensions of economic development, financial development, and environmental development.

In many data analysis tasks, it is beneficial to learn representations where each dimension is statistically independent and thus disentangled from the others. If data generating factors are also statistically independent, disentangled representations can be formed by Bayesian inference of latent variables. We examine a generalization of the Variational Autoencoder (VAE), $��$-VAE, for learning such representations using variational inference. $��$-VAE enforces conditional independence of its bottleneck neurons controlled by its hyperparameter $��$. This condition is in general not compatible with the statistical independence of latents. By providing analytical and numerical arguments, we show that this incompatibility leads to a non-monotonic inference performance in $��$-VAE with a finite optimal $��$., Presented at the 53rd Asilomar Conference on Signals, Systems, and Computers

Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which the emergent bump of neural activity in such networks can be manipulated by space and time-dependent external sensory or motor signals are not understood. Here, we find fundamental limits on how rapidly internal representations encoded along continuous attractors can be updated by an external signal. We apply these results to place cell networks to derive a velocity-dependent non-equilibrium memory capacity in neural networks., Comment: 17 pages