CSAIL Digital Archive
http://hdl.handle.net/1721.1/29806
2014-12-18T03:56:35ZQueueing 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.
2014-12-16T00:00:00ZNetwork 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.
2014-12-02T00:00:00ZtBurton: A Divide and Conquer Temporal Planner
http://hdl.handle.net/1721.1/91170
tBurton: A Divide and Conquer Temporal Planner
Wang, David; Williams, Brian C.
Planning for and controlling a network of interacting devices requires a planner that accounts for the automatic timed transitions of devices while meeting deadlines and achieving durative goals. For example, a planner for an imaging satellite with a camera intolerant of exhaust would need to determine that opening a valve causes a chain reaction that ignites the engine, and thus needs to shield its camera. While planners exist that support deadlines and durative goals, currently, no planners can handle automatic timed transitions. We present tBurton, a temporal planner that supports these features while additionally producing a temporally least-commitment plan. tBurton uses a divide and conquer approach: dividing the problem using causal-graph decomposition and conquering each factor with heuristic forward search. The `sub-plans' from each factor are unified in a conflict directed search, guided by the causal graph structure. We describe why tBurton is fast and efficient and present its efficacy on benchmarks from the International Planning Competition.
2014-10-24T00:00:00ZAutomatic Error Elimination by Multi-Application Code Transfer
http://hdl.handle.net/1721.1/91150
Automatic Error Elimination by Multi-Application Code Transfer
Sidiroglou-Douskos, Stelios; Lahtinen, Eric; Rinard, Martin
We present pDNA, a system for automatically transfer- ring correct code from donor applications into recipient applications to successfully eliminate errors in the recipient. Experimental results using six donor applications to eliminate nine errors in six recipient applications highlight the ability of pDNA to transfer code across applications to eliminate otherwise fatal integer and buffer overflow errors. Because pDNA works with binary donors with no need for source code or symbolic information, it supports a wide range of use cases. To the best of our knowledge, pDNA is the first system to eliminate software errors via the successful transfer of correct code across applications.
2014-10-02T00:00:00Z