Probabilistic Independence Networks for Hidden Markov Probability Models

Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (PINs). The paper contains a self-contained review of the basic principles of PINs.It is shown that the well-known forward-backward (F-B) and Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach.

A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

Workforce Issues in the Greater Boston Health Care Industry: Implications for Work and Family

Interviews with more than 40 leaders in the Boston area health care industry have identified a range of broadly-felt critical problems. This document synthesizes these problems and places them in the context of work and family issues implicit in the organization of health care workplaces. It concludes with questions about possible ways to address such issues. The defining circumstance for the health care industry nationally as well as regionally at present is an extraordinary reorganization, not yet fully negotiated, in the provision and financing of health care. Hoped-for controls on increased costs of medical care – specifically the widespread replacement of indemnity insurance by market-based managed care and business models of operation--have fallen far short of their promise. Pressures to limit expenditures have produced dispiriting conditions for the entire healthcare workforce, from technicians and aides to nurses and physicians. Under such strains, relations between managers and workers providing care are uneasy, ranging from determined efforts to maintain respectful cooperation to adversarial negotiation. Taken together, the interviews identify five key issues affecting a broad cross-section of occupational groups, albeit in different ways: Staffing shortages of various kinds throughout the health care workforce create problems for managers and workers and also for the quality of patient care. Long work hours and inflexible schedules place pressure on virtually every part of the healthcare workforce, including physicians. Degraded and unsupportive working conditions, often the result of workplace "deskilling" and "speed up," undercut previous modes of clinical practice. Lack of opportunities for training and advancement exacerbate workforce problems in an industry where occupational categories and terms of work are in a constant state of flux. Professional and employee voices are insufficiently heard in conditions of rapid institutional reorganization and consolidation. Interviewees describe multiple impacts of these issues--on the operation of health care workplaces, on the well being of the health care workforce, and on the quality of patient care. Also apparent in the interviews, but not clearly named and defined, is the impact of these issues on the ability of workers to attend well to the needs of their families--and the reciprocal impact of workers' family tensions on workplace performance. In other words, the same things that affect patient care also affect families, and vice versa. Some workers describe feeling both guilty about raising their own family issues when their patients' needs are at stake, and resentful about the exploitation of these feelings by administrators making workplace policy. The different institutions making up the health care system have responded to their most pressing issues with a variety of specific stratagems but few that address the complexities connecting relations between work and family. The MIT Workplace Center proposes a collaborative exploration of next steps to probe these complications and to identify possible locations within the health care system for workplace experimentation with outcomes benefiting all parties.

Inverse Metabolic Engineering of Synechocystis PCC 6803 for Improved Growth Rate and Poly-3-hydroxybutyrate Production

Synechocystis PCC 6803 is a photosynthetic bacterium that has the potential to make bioproducts from carbon dioxide and light. Biochemical production from photosynthetic organisms is attractive because it replaces the typical bioprocessing steps of crop growth, milling, and fermentation, with a one-step photosynthetic process. However, low yields and slow growth rates limit the economic potential of such endeavors. Rational metabolic engineering methods are hindered by limited cellular knowledge and inadequate models of Synechocystis. Instead, inverse metabolic engineering, a scheme based on combinatorial gene searches which does not require detailed cellular models, but can exploit sequence data and existing molecular biological techniques, was used to find genes that (1) improve the production of the biopolymer poly-3-hydroxybutyrate (PHB) and (2) increase the growth rate. A fluorescence activated cell sorting assay was developed to screen for high PHB producing clones. Separately, serial sub-culturing was used to select clones that improve growth rate. Novel gene knock-outs were identified that increase PHB production and others that increase the specific growth rate. These improvements make this system more attractive for industrial use and demonstrate the power of inverse metabolic engineering to identify novel phenotype-associated genes in poorly understood systems.