A novel approach to metabolic network analysis using a Nash Equilibrium formulation is proposed. Enzymes are considered to be players in a multi-player game in which each player attempts to minimize the dimensionless Gibbs free energy associated with the biochemical reaction(s) it catalyzes subject to elemental mass balances. Mathematical formulation of the metabolic network as a set of nonline...

Synthesis of heat exchanger networks (HENs) is inherently a mixed integer and nonlinear programming (MINLP) problem. Solving such problems leads to difficulties <span style="font-size: 10pt; color: #00...

In this paper we propose a multiperiod nonlinear programming (NLP) formulation that incorporates empirical process models for the optimal planning of a multi-plant production site. Using as a basis a real world application of a polymer plant that produces 27 products, a model is developed for predicting the detailed production using actual plant data. The empirical process models account for ra...

This paper discusses an implementation of the Outer-Approximation/Equality-Relaxation algorithm for solving MINLP problems that arise in process systems engineering. The computer code DICOPT has been developed utilizing state-of-the-art optimization tools and a powerful modelling language. Computational experience in solving fifteen MINLP problems with DICOPT is reported. Applications include d...

We introduce DRAIL, a new declarative framework for specifying Deep Relational Models. Our framework separates structural considerations, which express domain knowledge, from the learning architecture to simplify the process of building complex structural models. We show the DRAIL formulation of two NLP tasks, Twitter Part-of-Speech tagging and Entity-Relation extraction. We compare the perform...

Stackelberg games form the core of a number of tools deployed for computing optimal patrolling strategies in adversarial domains, such as the US Federal Air Marshall Service and the US Coast Guard. In traditional Stackelberg security game models the attacker knows only the probability that each target is covered by the defender, but is oblivious to the detailed timing of the coverage schedule. ...

This paper describes a machine-learning based approach to recognizing diagnosed disease names and corresponding temporal expressions. Using CRFs (conditional random fields) to learn and predict tags, the systems described in this paper are characterized by a character-level formulation and heuristic features extracted from medical terminologies. Experimental results on the NTCIR-11 MedNLP-2 dat...

We propose a new formulation of the PP attachment problem as a 4-way classification which takes into account the argument or adjunct status of the PP. Based on linguistic diagnostics, we train a 4-way classifier that reaches an average accuracy of 73.9% (baseline 66.2%). Compared to a sequence of binary classifiers, the 4-way classifier reaches better performance and individuates a verb's argum...