Loss of selective constraint on a gene may be expected following changes in the environment or life history that render its function unnecessary. The long-term persistence of protein-coding genes after the loss of known functional necessity can occur by chance or because of selective maintenance of an unknown gene function. The selective maintenance of an alternative gene function is not demons...

In his ‘Objective Bayesian calibration and the problem of non-convex evidence’, Gregory Wheeler criticises the principle I invoke in Williamson (2010) for calibrating degrees of belief with chances. Bayesian epistemologists commonly appeal to some sort of Calibration norm that says that degrees of belief should be calibrated to known chances. Typically they invoke a principle variously known as...

How did I get to become a cell biologist? Or, more generally, why do things happen the way they do? The answer provided by the philosopher Democritus and later adopted by Jacques Monod is "everything existing in the universe is the fruit of chance and necessity." While I read Monod's book Chance and Necessity as an undergraduate student, little did I appreciate the accuracy of this citation and...

To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios. Using this semantics, we can compile stochastic const...

Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only solution approach to date compiles down SCSPs into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to...

Ambiguous Chance Constrained Programs: Algorithms and Applications Emre Erdoğan Chance constrained problems are optimization problems where one or more constraints ensure that the probability of one or more events occurring is less than a prescribed threshold. Although it is typically assumed that the distribution defining the chance constraints are known perfectly; in practice this assumption ...

Grouping datasets plays an important role in many scientific researches. Depending on data features and applications, different constrains are imposed on groups, while having groups with similar members is always a main criterion. In this paper, we propose an algorithm for grouping the objects with random labels, nominal features having too many nominal attributes. In addition, the size constra...

The controller state and reference governor (CSRG) is an add-on scheme for nominal closed-loop systems with dynamic controllers which supervises the internal input to system enforce pointwise-in-time constraints. By admitting both modifications, CSRG can achieve enlarged constrained domain of attraction compared conventional schemes where only modification permitted. This paper studies subject ...

Lean manufacturing is a strategic concern for companies which conduct mass production and it has become even more significant for those producing in a project-oriented way by modularization.  In this paper, a bi-objective optimization model is proposed to design and plan a supply chain up to the final assembly centre. The delivery time and the quality in the procurement and low fluctuation of t...

A new approach to solve Chance constrained Portfolio Optimization Problems (CPOPs) without using the Monte Carlo simulation is proposed. Specifically, according to Chebyshev inequality, the prediction interval of a stochastic function value included in CPOP is estimated from a set of samples. By using the prediction interval, CPOP is transformed into Lower-bound Portfolio Optimization Problem (...