We consider a multiperiod system operation problem with two conflicting objectives, minimizing cost and risk. Risk stems from uncertain disruptions to the system during operation. While a general model would hedge against disruptions in each time period, we study special cases in which only a modest number of disruptions occur. To optimize for risk, we employ a convex approximation based on con...

This paper studies a Maritime Inventory Routing Problem with Time Windows (MIRPTW) for deliveries with uncertain disruptions. We consider disruptions that increase travel times between ports and ultimately affect the deliveries in one or more time windows. The objective is to find flexible solutions that can withstand unplanned disruptions. We propose a Lagrangian heuristic algorithm of obtaini...

there are a variety of electrocardiogram (ecg) based methods to detect myocardial infarction (mi) patients. this study used the signal averaged electrocardiogram (saecg) and its wavelet coefficient as an index to detect mi. orthogonal leads signals from 50 acute myocardial infarction (ami) and 50 healthy subjects were selected from the national metrology institute of germany (ptb diagnostic dat...

The Rocky Mountain Athletic Conference is a Division II National Collegiate Athletic Association conference that offers, inter alia, women’s softball. Within the conference, four-game series are played against every other conference team according to a temporally constrained schedule. Manually generated schedules result in imbalances, such as breaks of multiple home or away series and away-seri...

Probabilistic databases extend statistical inference from limited, hand-crafted statistical models to an entire database. Data analysts can discover trends, test hypothesis, and run what-if scenarios by simply running SQL queries. The technical challenge in a probabilistic database is the query processor, which needs to perform a probabilistic inference for every row output by a SQL query: the ...

We propose a novel approach which combines the use of Bayesian network and probabilistic association rules to discover and explain anomalies in data. The Bayesian network allows us to organize information in order to capture both correlation and causality in the feature space, while the probabilistic association rules have a structure similar to association mining rules. In particular, we focus...

In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks --- small modifications of the input that change predictions. Besides rigorously studied $\ell_p$-bounded additive perturbations, semantic perturbations (e.g. rotation, translation) raise a serious concern on deploying ML systems in real-world. Therefore, important provide provable guara...

Supply chains, as vital systems to the well-being of countries and economies, require systematic approaches reduce their vulnerability. In this paper, we propose a non linear optimisation model determine an effective distribution protective resources among facilities in service supply so probability failure which are exposed case external disruptions. The protected assets depends on level prote...