Production planning with due-date constraints

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1992.Includes bibliographical references (leaves 161-166).by Suguna Pappu.Ph.D

Articulated Matching with Point Features

Articulated matching arises naturally in object recognition and tracking. When a part-based description of an object is adopted, the problem of matching the model features to the data can be decomposed into matching the parts while paying attention to overall model coherence. Here we focus on the first part of the problem, namely, the articulated matching of a part based description of an object to unlabelled data point features. Each model part is assumed to undergo an independent affine transformation. The novel aspect of our approach is the integrated search for both the point-to-point feature correspondences and the different affine transformations of the parts. The result is an alternating algorithm which uses a least-squares solution for the affine parameters and the softassign technique for the correspondences. A significant property of the algorithm is its ability to reject missing and extra features as outliers. The algorithm is embedded in a deterministic annealing scheme. Re..

Gradient boosting for Parkinson's disease diagnosis from voice recordings

Background Parkinson's Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings. Method We used Parkinson Dataset with Replicated Acoustic Features Data Set from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. Results The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson's Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946-0.955 in 4-fold cross validation using only seven acoustic features. Conclusions Machine learning can accurately detect Parkinson's disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson's disease.WOS:0005732847000042-s2.0-85091053141PubMed: 3293349

Cephalohematomas, an occult nidus for infection and inflammation: A case report and review of the literature

Background: Cephalohematomas (CH) are benign neonatal fluid collections that arise between the periosteum and skull due to birth trauma, and usually resolve spontaneously without intervention. CH may rarely become infected. Case description: The authors report a case of sterile CH requiring surgical evacuation in a persistently febrile neonate treated with intravenous (IV) antibiotics for Escherichia coli urosepsis. Diagnostic tap of the CH yielded no pathogens, but given the persistence of fevers, surgical evacuation was performed. The patient demonstrated clinical improvement postoperatively. Conclusion: A systematic review of literature was conducted through a MEDLINE search using the keyword cephalohematoma. Articles were screened for cases of infected CH and their subsequent management. Clinicopathological characteristics and outcomes of the present case were reviewed and compared to those in the literature. Infected CH were reported in 25 articles describing 58 patients. Common pathogens included E. coli and Staphylococcal species. Treatment included a course of IV antibiotics (10 days-6 weeks) and often included percutaneous aspiration (n = 47) for diagnostic and therapeutic purposes. Surgical evacuation was performed in 23 cases. To the authors\u27 knowledge, the present case is the first documented report in which evacuation of a culture-negative CH resulted in resolution of the patient\u27s clinical symptoms of sepsis that persisted despite appropriate antibiotic treatment. This suggests that patients with CH should be evaluated through diagnostic tap of the collection if there are signs of local or persistent systemic infection. Surgical evacuation may be indicated if percutaneous aspiration does not result in clinical improvement

A robust point matching algorithm for autoradiograph alignment

We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local natural and artifactual differences between the autoradio-raph slices. We have executed the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated it using synthetically generated spatial mappings and provided a visual comparison against the well known iterated closest point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided