Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, engineering task designing vaccines remains a challenge. In particular, genetic variability human makes it difficult design peptide that provide widespread immunity vaccinated populations. We introduce framework for evaluating uses probabilistic models, demonstr...

A probabilistic framework is proposed for the optimization of efficient switched control strategies for physical systems dominated by stochastic excitation. In this framework, the equation for the state trajectory is replaced with an equivalent equation for its probability distribution function in the constrained optimization setting. This allows for a large class of control rules to be conside...

By exploring the geometry of the underlying constrained optimization, a finitely parameterized solution to the discrete time receding horizon linear quadratic control problem with a finite input constraint set is obtained. The resulting controller gives rise to a closed loop system which is piece-wise affine in the plant state. The switching regions are polytopes and are related to those obtain...

Equations having form (1) arise as first-order conditions in microeconomic constrained optimization models, as defining characterizations for general equilibrium and macroeconomic models, and as steady-state solution characterizations for descriptive and optimal growth models. (See Silberberg [12], Hansen [7], Cass and Shell [3], and Brock [l].) If Y :R"' -+ R" is continuously differentiable ov...

In this paper, we consider the problem of constrained maximization of the minimum of a set of submodular functions, in which the goal is to find solutions that are robust to worst-case values of the objective functions. Unfortunately, this problem is both non-submodular and inapproximable. In the case where the submodular functions are monotone, an approximate solution can be found by relaxing ...

This paper introduces a new method for identifying named-entity (NE) transliterations in bilingual corpora. Recent works have shown the advantage of discriminative approaches to transliteration: given two strings (ws, wt) in the source and target language, a classifier is trained to determine if wt is the transliteration of ws. This paper shows that the transliteration problem can be formulated...

where X is taken to be a known or given feasible set. In particular, we are interested in identifying x∗ ∈ X such that f (x∗) realizes the above minimum, if such an x∗ exists. Typical constraints include bounds on x, such as X = {x ∈ Rn : ||x||6 R} for some R > 0, or linear inequality constraints such as X = {x ∈ Rn : Ax> b} for a matrix A and a column vector b. More examples will be discussed....