CSAIL Digital Archive
http://hdl.handle.net/1721.1/29806
Thu, 22 Jan 2015 07:30:15 GMT2015-01-22T07:30:15ZEfficiently Solving Repeated Integer Linear Programming Problems by Learning Solutions of Similar Linear Programming Problems using Boosting Trees
http://hdl.handle.net/1721.1/93099
Efficiently Solving Repeated Integer Linear Programming Problems by Learning Solutions of Similar Linear Programming Problems using Boosting Trees
Banerjee, Ashis Gopal; Roy, Nicholas
It is challenging to obtain online solutions of large-scale integer linear programming (ILP) problems that occur frequently in slightly different forms during planning for autonomous systems. We refer to such ILP problems as repeated ILP problems. The branch-and-bound (BAB) algorithm is commonly used to solve ILP problems, and a significant amount of computation time is expended in solving numerous relaxed linear programming (LP) problems at the nodes of the BAB trees. We observe that the relaxed LP problems, both within a particular BAB tree and across multiple trees for repeated ILP problems, are similar to each other in the sense that they contain almost the same number of constraints, similar objective function and constraint coefficients, and an identical number of decision variables. We present a boosting tree-based regression technique for learning a set of functions that map the objective function and the constraints to the decision variables of such a system of similar LP problems; this enables us to efficiently infer approximately optimal solutions of the repeated ILP problems. We provide theoretical performance guarantees on the predicted values and demonstrate the effectiveness of the algorithm in four representative domains involving a library of benchmark ILP problems, aircraft carrier deck scheduling, vehicle routing, and vehicle control.
Wed, 21 Jan 2015 00:00:00 GMThttp://hdl.handle.net/1721.1/930992015-01-21T00:00:00ZSupplementary Materials for "A Survey of Corpora in Computational and Cognitive Narrative Science"
http://hdl.handle.net/1721.1/92563
Supplementary Materials for "A Survey of Corpora in Computational and Cognitive Narrative Science"
Finlayson, Mark Alan
This archive contains supplementary materials for the article titled "A Survey of Corpora in Computational and Cognitive Narrative Science" by Mark A. Finlayson, published in the journal *Sprache und Datenverarbeitung*. The archive contains two files. The first file is the raw bibliographic data of the survey, containing 2600+ citations. The second file is a spreadsheet with the coded features of each corpus, plus the analyses that underlie sections 3 & 4 of the paper.
Tue, 30 Dec 2014 00:00:00 GMThttp://hdl.handle.net/1721.1/925632014-12-30T00:00:00ZQueueing Theory Analysis of Labor & Delivery at a Tertiary Care Center
http://hdl.handle.net/1721.1/92354
Queueing Theory Analysis of Labor & Delivery at a Tertiary Care Center
Gombolay, Matthew; Golen, Toni; Shah, Neel; Shah, Julie
Labor and Delivery is a complex clinical service requiring the support of highly trained healthcare professionals from Obstetrics, Anesthesiology, and Neonatology and the access to a finite set of valuable resources. In the United States, the rate of cesarean sections on labor floors is approximately twice as high as considered appropriate for patient care. We analyze one month of data from a Boston-area hospital to assess how well the labor and delivery process can be modelled with tools from queueing theory. We find that the labor and delivery process is highly amenable to analysis under queueing theory models. We also investigate the problem of high cesarean section rates and the potential effects of resource utilization of lowering the rate of cesarean section.
Tue, 16 Dec 2014 00:00:00 GMThttp://hdl.handle.net/1721.1/923542014-12-16T00:00:00ZNetwork Infusion to Infer Information Sources in Networks
http://hdl.handle.net/1721.1/92031
Network Infusion to Infer Information Sources in Networks
Feizi, Soheil; Duffy, Ken; Kellis, Manolis; Medard, Muriel
Several models exist for diffusion of signals across biological, social, or engineered networks. However, the inverse problem of identifying the source of such propagated information appears more difficult even in the presence of multiple network snapshots, and especially for the single-snapshot case, given the many alternative, often similar, progression of diffusion that may lead to the same observed snapshots. Mathematically, this problem can be undertaken using a diffusion kernel that represents diffusion processes in a given network, but computing this kernel is computationally challenging in general. Here, we propose a path-based network diffusion kernel which considers edge-disjoint shortest paths among pairs of nodes in the network and can be computed efficiently for both homogeneous and heterogeneous continuous-time diffusion models. We use this network diffusion kernel to solve the inverse diffusion problem, which we term Network Infusion (NI), using both likelihood maximization and error minimization. The minimum error NI algorithm is based on an asymmetric Hamming premetric function and can balance between false positive and false negative error types. We apply this framework for both single-source and multi-source diffusion, for both single-snapshot and multi-snapshot observations, and using both uninformative and informative prior probabilities for candidate source nodes. We also provide proofs that under a standard susceptible-infected diffusion model, (1) the maximum-likelihood NI is mean-field optimal for tree structures or sufficiently sparse Erdos-Renyi graphs, (2) the minimum-error algorithm is mean-field optimal for regular tree structures, and (3) for sufficiently-distant sources, the multi-source solution is mean-field optimal in the regular tree structure. Moreover, we provide techniques to learn diffusion model parameters such as observation times. We apply NI to several synthetic networks and compare its performance to centrality-based and distance-based methods for Erdos-Renyi graphs, power-law networks, symmetric and asymmetric grids. Moreover, we use NI in two real-world applications. First, we identify the news sources for 3,553 stories in the Digg social news network, and validate our results based on annotated information, that was not provided to our algorithm. Second, we use NI to identify infusion hubs of human diseases, defined as gene candidates that can explain the connectivity pattern of disease-related genes in the human regulatory network. NI identifies infusion hubs of several human diseases including T1D, Parkinson, MS, SLE, Psoriasis and Schizophrenia. We show that, the inferred infusion hubs are biologically relevant and often not identifiable using the raw p-values.
Tue, 02 Dec 2014 00:00:00 GMThttp://hdl.handle.net/1721.1/920312014-12-02T00:00:00Z