On scheduling parallel machines with a partially shared resource / 1993:160

Includes bibliographical references (p. 13)

Minimum tardiness scheduling in flow shops : construction and evaluation of alternative solution approaches / 1993:153

Includes bibliographical references (p. 23-24)

A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence

We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence. To solve this problem, we have developed Red-Blue MPNN, a novel graph neural network that processes an augmented graph to estimate the correspondence. We have designed a novel conditional refinement scheme to find the exact correspondence when certain conditions are satisfied. We further develop a graph neural network that takes the aligned meshes and the time value as input and fuses this information to process further and generate the desired result. Using motion capture datasets and human mesh designing software, we create a large-scale synthetic dataset consisting of temporal sequences of human meshes in motion. Our results demonstrate that our approach generates realistic deformation of body parts given complex inputs

See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging

In recent years, deep learning models have revolutionized medical image interpretation, offering substantial improvements in diagnostic accuracy. However, these models often struggle with challenging images where critical features are partially or fully occluded, which is a common scenario in clinical practice. In this paper, we propose a novel curriculum learning-based approach to train deep learning models to handle occluded medical images effectively. Our method progressively introduces occlusion, starting from clear, unobstructed images and gradually moving to images with increasing occlusion levels. This ordered learning process, akin to human learning, allows the model to first grasp simple, discernable patterns and subsequently build upon this knowledge to understand more complicated, occluded scenarios. Furthermore, we present three novel occlusion synthesis methods, namely Wasserstein Curriculum Learning (WCL), Information Adaptive Learning (IAL), and Geodesic Curriculum Learning (GCL). Our extensive experiments on diverse medical image datasets demonstrate substantial improvements in model robustness and diagnostic accuracy over conventional training methodologies.Comment: 20 pages, 3 figures, 1 tabl

The job shop tardiness problem: A decomposition approach

An important criterion for evaluating the effectiveness of many manufacturing firms is their ability to meet due dates. In low to medium volume discrete manufacturing, typified by traditional job shops and more recently by flexible manufacturing systems, this criterion is usually operationalized on the shop floor through the use of prioritizing dispatching rules. The widespread use of dispatching rules has led to a number of investigations where the due date performance of various rules is compared. In contrast to previous research on dispatching rules, this paper proposes a new approach that decomposes the dynamic problem into a series of static problems. These static problems are solved in their entirely, and then implemented dynamically on a rolling basis. To illustrate this approach, a specific heuristic is developed that constructs the schedule for the entire system by focusing on the bottleneck machine. Computational results indicate that significant due date performance improvement over traditional dispatching rules can be obtained by using this new approach.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30576/1/0000211.pd

Product assignment in flexible multilines : Part 2 - Single-state systems with no demand splitting / 1993:107

Includes bibliographical references (p. 34)

Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing

In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance. Recent medical research has been focused on tweaking the architectures to attain better performance without giving due consideration to the representation of data. In this paper, we present a novel approach for transforming grid based data into its higher dimensional representations, leveraging unstructured point cloud data structures. We first generate a sparse point cloud from an image by integrating pixel color information as spatial coordinates. Next, we construct a hypersurface composed of points based on the image dimensions, with each smooth section within this hypersurface symbolizing a specific pixel location. Polygonal face construction is achieved using an adjacency tensor. Finally, a dense point cloud is generated by densely sampling the constructed hypersurface, with a focus on regions of higher detail. The effectiveness of our approach is demonstrated on a publicly accessible brain tumor dataset, achieving significant improvements over existing classification techniques. This methodology allows the extraction of intricate details from the original image, opening up new possibilities for advanced image analysis and processing tasks

Real-time scheduling of an automated manufacturing center

This paper investigates the dynamic scheduling of an automated manufacturing workcenter at which jobs are processed in batches, and there is a constant changeover time between batches of different part types. The primary measures of schedule performance are mean flow time and mean tardiness.The dynamic scheduling problem is treated as a series of static problems which are solved on a rolling-horizon basis. Characteristics of the optimal solutions to the mean flow time and mean tardiness problems are developed, and an implicit enumeration approach to the mean tardiness problem is proposed. These results are used for constructing efficient scheduling procedures for the dynamic problem. We also derive the steady state relationship between workcenter utilization level, batch size and mean flow time for one and two part types. A simulation study extends this relationship to a larger number of part types.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27935/1/0000361.pd

Radial Velocity Detectability of Low Mass Extrasolar Planets in Close Orbits

Detection of Jupiter mass companions to nearby solar type stars with precise radial velocity measurements is now routine, and Doppler surveys are moving towards lower velocity amplitudes. The detection of several Neptune-mass planets with orbital periods less than a week has been reported. The drive toward the search for close-in Earth-mass planets is on the agenda. Successful detection or meaningful upper limits will place important constraints on the process of planet formation. In this paper, we quantify the statistics of detection of low-mass planets in-close orbits, showing how the detection threshold depends on the number and timing of the observations. In particular, we consider the case of a low-mass planet close to but not on the 2:1 mean motion resonance with a hot jupiter. This scenario is a likely product of the core-accretion hypothesis for planet formation coupled with migration of jupiters in the protoplanetary disk. It is also advantageous for detection because the orbital period is well-constrained. Detection of few Earth mass rocky cores will require ~ 1 m/s velocity precision, and most importantly, a much better understanding of stellar radial velocity jitter.Comment: to appear in ApJ (8 pages, 7 figures

Clinico-Pathological Factors Determining Recurrence of Phyllodes Tumors of the Breast: The 25-Year Experience at a Tertiary Cancer Centre

Background: Phyllodes tumors (PTs) of the breast are rare fibroepithelial tumors that are generally more prone to recurrence.Aims and objectivesThis study aimed to assess the clinicopathological features, diagnostic modalities, and therapeutic interventions, along with their respective outcomes, to identify the factors associated with a recurrence of PTs of the breast.MethodologyA retrospective cohort and observational study was conducted, which entailed analyzing the clinicopathological data of patients who were previously diagnosed or presented with PTs of the breast between 1996 and 2021. Data included the total number of patients diagnosed with PTs of the breast and their ages, tumor grade on initial biopsy, tumor location (left or right breast), tumor size, therapeutic interventions carried out (including surgery-either mastectomy or lumpectomy-and adjuvant radiotherapy), final tumor grade, recurrence status, type of recurrence, and time to recurrence.ResultsWe analyzed data on a total of 87 patients who were pathologically proven to have PTs, and 46 patients (52.87%) were found to have recurrences. All patients were female, with a mean age at diagnosis of 39 years (range 15-70). Patients aged 40 years, with a rate of recurrence of 45.65% (n = 21/46). A total of 55.4% of patients presented with primary PTs and 44.6% had recurrent PTs at presentation. The average time to local recurrence (LR) from the completion of treatment was 13.8 months, whereas for systemic recurrence (SR), it was 15.29 months. Surgery (mastectomy/lumpectomy) was the major determinant for local recurrence (p ConclusionPatients who received adjuvant radiotherapy (RT) had a minimal recurrence of PTs. Patients who were found to have a malignant biopsy on initial diagnosis (triple assessment) had a higher incidence of PTs and were more prone to SR than LR. Surgery was a determining factor in the increased rate of LR, with lumpectomy associated with a higher incidence of LR than mastectomy