Abstract: This article formulates algorithms to upper-bound the maximum value-at-risk (VaR) of a state function along trajectories of stochastic processes. The VaR is upper bounded by two methods: ...
This important study uses single-neuron Patch-seq RNA sequencing to investigate the process by which RNA editing can produce protein diversity and regulate function in various cellular contexts. The ...
This study develops a unified framework for optimal portfolio selection in jump–uncertain stochastic markets, contributing both theoretical foundations and computational insights. We establish the ...
Abstract: Deterministic Turing machines and their associated complexity measures, by construction, cannot capture the complexity of the output of stochastic processes - like those in the real world.
As global financial markets become increasingly interconnected, accurately modelling correlations between assets is essential. Traditional models often assume static correlations, which fail to ...
In response to pressure from rivals including Chinese AI company DeepSeek, OpenAI is changing the way its newest AI model, o3-mini, communicates its step-by-step “thought” process. On Thursday, OpenAI ...
Understanding the physical dynamics of probe diffusion is critical for uncovering subtle interactions between particles and their environments, offering insights into evolving transport mechanisms at ...
We introduce a pure deep neural network-based methodology for the estimation of long memory parameters associated with time series models, emphasizing on long-range dependence. Such parameters, ...
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