Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, invasion, and other cancer hallmarks. The goal of precision medicine is to identify therapeutically-actionable mutations from large-scale omic datasets. However, the multiplicity of oncogenes required for transform...

Data-driven evolutionary optimization has witnessed great success in solving complex real-world problems. However, existing data-driven algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage security threats if the must collected from different devices. To address above issue, this paper proposes a federated framework able p...

Surrogate risk minimization is a popular framework for supervised learning; property elicitation is a widely studied area in probability forecasting, machine learning, statistics and economics. In this paper, we connect these two themes by showing that calibrated surrogate losses in supervised learning can essentially be viewed as eliciting or estimating certain properties of the underlying con...

This paper presents a political economy model of public standards in an open economy model. We use the model to derive the political optimum and to analyze different factors that have an influence on this political equilibrium. The paper discusses how the level of development influences the political equilibrium. We also analyze the relation between trade and the political equilibrium and compa...

We present a framework for designing fast and monotonic algorithms for transmission tomography penalized-likelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the log-likelihood. Due to the form of the log-likelihood function, it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the propose...

We present an approach to uncertainty propagation in dynamic systems, exploiting information provided by related experimental results along with their models. The approach relies on a solution mapping technique to approximate mathematical models by polynomial surrogate models. We use these surrogate models to formulate prediction bounds in terms of polynomial optimizations. Recent results on po...

We present a framework for designing fast and monotonic algorithms for transmission tomography penalizedlikelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the loglikelihood. Due to the form of the log-likelihood function, it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the proposed ...

We present a framework for designing fast and monotonic algorithms for transmission tomography penalizedlikelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the loglikelihood. Due to the form of the log-likelihood function, it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the proposed ...