Segmented face detection using clustering

Perception forms a very important part of learning. The way we perceive things has a lot to do with how we understand. It forms a very crucial link in our build-up of knowledge. Living organisms have a remarkable ability of understanding spatial information. It is due to their inherent ability of generating native organizations, models, etc, and most importantly, their ability to generalize and infer - based upon symmetry, probability, familiarity, etc, that allows them to instantly adapt their knowledge to the given surroundings. It forms a very basic step in survival. When trying to make machines intelligent, one of the first hurdles faced is the problem of perception - questions like which data is important (light, color, texture, sound, etc.)? how much importance should each data be given? etc come up. The purpose of this work is to observe the workings, and results, of trying to detect faces in images, by searching separately for the eyes, nose and mouth regions of a face. The regions are searched independently of one another, using clustering and Neural Networks. Broadly speaking Clustering is used to locate generalized face regions, and Neural Networks are used to map accurately the decision surface. The search for eyes, nose and mouth is done separately, in an attempt to reduce the complexity of the intensity map being searched, thus hoping to improve upon the accuracy and reliability of the detection process. Also it provides for simultaneous parallel search, which is of high importance for real-time tasks like face-detection. To observe the effectiveness, and generalization capability of the process, a very small and localized dataset was used for training pur poses. Also, no feature sets or such abstractions were used in the training and implementation of the work - only raw data was used for detection - this was done to reduce the effects of selection of a wrong feature set

Shaping Health Policy for Low-Income Populations: An Assessment of Public Comments in a New Medicaid Waiver Process

Since the Supreme Court decided that the Affordable Care Act\u27s (ACA) Medicaid expansion is optional for the states, several have obtained federal approval to use Section 1115 waivers to expand Medicaid while changing its coverage and benefits design. There has long been concern that policy making for Medicaid populations may lack meaningful engagement with low-income constituents, and therefore the ACA established a new process under which the public can submit comments on pending Medicaid waiver applications. We analyzed 291 comment letters submitted to federal regulators pertaining to Medicaid Section 1115 waiver applications in the first five states to seek such waivers: Arkansas, Indiana, Iowa, Michigan, and Pennsylvania. We found that individual citizens, including those who identified as Medicaid-eligible, submitted a sizable majority of the comment letters. Comment letters tended to mention controversial provisions of the waivers and reflected the competing political rhetoric of “personal responsibility” versus “vulnerable populations.” Despite the fact that the federal government seemed likely to approve the waiver applications, we found robust public engagement, reflecting the salience of the issue of Medicaid expansion under the ACA. Our findings are consistent with the argument that Medicaid is a program of growing centrality in US health politics

Finding the most navigable path in road networks

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks

A Multi-Threading Algorithm for Constrained Path Optimization Problem on Road Networks

The constrained path optimization (CPO) problem takes the following input: (a) a road network represented as a directed graph, where each edge is associated with a "cost" and a "score" value; (b) a source-destination pair and; (c) a budget value, which denotes the maximum permissible cost of the solution. Given the input, the goal is to determine a path from source to destination, which maximizes the "score" while constraining the total "cost" of the path to be within the given budget value. CPO problem has applications in urban navigation. However, the CPO problem is computationally challenging as it can be reduced to an instance of the arc orienteering problem, which is known to be NP-hard. The current state-of-the-art algorithms for this problem are essentially serial in nature and cannot take full advantage (i.e., achieve good load balance) of the increasingly available multi-core systems to solve a CPO query. Our proposed parallel algorithm (with its intelligent task-assignment scheme) achieves both superior solution quality and very low execution times (via good load balancing). Moreover, our approach is also able to demonstrate an almost linear speed-up with an increase in the number of cores.Comment: 10 pages, 14 figures, accepted as a short paper in the 23rd International Conference on Web Information Systems Engineerin

Hybrid Decision making model using linear programming and analytical hierarchical process for comparison of manufacturing choices (additive and traditional) ( a pilot study)

This research is built upon existing knowledge of additive manufacturing and traditional manufacturing to gain insights into the cost differences associated with different manufacturing processes as a pilot study. The researcher proposed a novel mathematical framework comprising a hybrid decision-making model comprising a linear optimization part entailed by the two distinct manufacturing procedures and an Analytical Hierarchical Process (AHP) part for choosing the best technology based on a set of qualitative factors. The model integrates diverse cost components, including but not limited to labor, materials, and equipment costs. Through a hybrid decision-making model, the research study analyzes additive manufacturing and traditional manufacturing in light of quantitative factors (cost) and qualitative factors (quality, speed of production, sustainability, and flexibility). By using a pilot case study, the results suggest that AM provides a reduction in cost due to optimization in various cost components but also considers TM as a preferable alternative over AM using the integrated qualitative criteria

An observational study to evaluate the additional benefit of dual-energy CT and phonation CT in the pre-operative local staging of laryngeal and hypopharyngeal cancers

OBJECTIVE: To evaluate the additional benefit and diagnostic accuracy of both phonation CT and dual-energy CT in comparison to the weighted average CT imaging in the local staging of laryngeal and hypopharyngeal cancers. METHODS: The institutional review board approved this prospective study and written informed consent was obtained from all patients. 90 consecutive patients underwent 64-section dual-energy CT to stage laryngeal (n=84) and hypopharyngeal (n=6) cancers. Additional limited “eee” phonation CT was performed through the larynx. Iodinedensity images was interpreted using AW Server 2.0 software, in GSI general mode. Endoscopy findings were obtained for all patients. 13 patients underwent surgery (14%) and findings from histopathological examination were obtained. Endoscopy and histopathology findings when available were used as the standard of reference for the evaluation of diagnostic performance with receiver operating characteristic (ROC) curve analysis and in terms of sensitivity and specificity. RESULTS: For pyriform sinus involvement, “eee” phonation CT showed a slightly higher accuracy than weighted-average imaging (AUC: 0.9 vs 0.878), respectively. Dualenergy CT showed a slightly higher accuracy than weighted-average imaging for thyroid cartilage invasion (AUC: 0.567 vs 0.467 respectively). Prevertebral muscle invasion was more often identified on dual-energy CT than weighted-average images, however we were not able to provide diagnostic accuracies for prevertebral muscle invasion as surgery was precluded in these patients due to locally advanced disease. Although, mild added accuracy for dual-energy and phonation was found, we do not recommend routine use of these modalities due to poor statistical significance

Phylogeny and antiquity of M macrohaplogroup inferred from complete mt DNA sequence of Indian specific lineages

BACKGROUND: Analysis of human complete mitochondrial DNA sequences has largely contributed to resolve phylogenies and antiquity of different lineages belonging to the majorhaplogroups L, N and M (East-Asian lineages). In the absence of whole mtDNA sequence information of M lineages reported in India that exhibits highest diversity within the sub-continent, the present study was undertaken to provide a detailed analysis of this macrohaplogroup to precisely characterize and unravel the intricate phylogeny of the lineages and to establish the antiquity of M lineages in India. RESULTS: The phylogenetic tree constructed from sequencing information of twenty-four whole mtDNA genome revealed novel substitutions in the previously defined M2a and M6 lineages. The most striking feature of this phylogenetic tree is the recognition of two new lineages, M30 and M31, distinguished by transitions at 12007 and 5319, respectively. M30 comprises of M18 and identifies a potential new sub-lineage possessing substitution at 16223 and 16300. It further branches into M30a sub-lineage, defined by 15431 and 195A substitution. The age of M30 lineage was estimated at 33,042 YBP, indicating a more recent expansion time than M2 (49,686 YBP). The M31 branch encompasses the M6 lineage along with the previously defined M3 and M4 lineages. Contradictory to earlier reports, the M5 lineage does not always include a 12477 substitution, and is more appropriately defined by a transversion at 10986A. The phylogenetic tree also identifies a potential new lineage in the M* branch with HVSI sequence as 16223,16325. Substitutions in M25 were in concordance with previous reports. CONCLUSION: This study describes five new basal mutations and recognizes two new lineages, M30 and M31 that substantially contribute to the present understanding of macrohaplogroup M. These two newly erected lineages include the previously independent lineages M18 and M6 as sub-lineages within them, respectively, suggesting that most mt DNA genomes might arise as limited offshoots of M trunk. Furthermore, this study supports the non existence of lineages such as M3 and M4 that are solely defined on the basis of fast mutating control region motifs and hence, establishes the importance of coding region markers for an accurate understanding of the phylogeny. The deep roots of M phylogeny clearly establish the antiquity of Indian lineages, especially M2, as compared to Ethiopian M1 lineage and hence, support an Asian origin of M majorhaplogroup

Load Balanced Demand Distribution under Overload Penalties

Input to the Load Balanced Demand Distribution (LBDD) consists of the following: (a) a set of public service centers (e.g., schools); (b) a set of demand (people) units and; (c) a cost matrix containing the cost of assignment for all demand unit-service center pairs. In addition, each service center is also associated with a notion of capacity and a penalty which is incurred if it gets overloaded. Given the input, the LBDD problem determines a mapping from the set of demand units to the set of service centers. The objective is to determine a mapping that minimizes the sum of the following two terms: (i) the total assignment cost between demand units and their allotted service centers and, (ii) total of penalties incurred. The problem of LBDD finds its application in the domain of urban planning. An instance of the LBDD problem can be reduced to an instance of the min-cost bi-partite matching problem. However, this approach cannot scale up to the real world large problem instances. The current state of the art related to LBDD makes simplifying assumptions such as infinite capacity or total capacity being equal to the total demand. This paper proposes a novel allotment subspace re-adjustment based approach (ASRAL) for the LBDD problem. We analyze ASRAL theoretically and present its asymptotic time complexity. We also evaluate ASRAL experimentally on large problem instances and compare with alternative approaches. Our results indicate that ASRAL is able to scale-up while maintaining significantly better solution quality over the alternative approaches. In addition, we also extend ASRAL to para-ASRAL which uses the GPU and CPU cores to speed-up the execution while maintaining the same solution quality as ASRAL.Comment: arXiv admin note: text overlap with arXiv:2009.0176

Spatiotemporal Impact Analysis of Hurricanes and Storm Surges on Power Systems

This paper develops a spatiotemporal probabilistic impact assessment framework to analyze and quantify the compounding effect of hurricanes and storm surges on the bulk power grid. The probabilistic synthetic hurricane tracks are generated using historical hurricane data, and storm surge scenarios are generated based on observed hurricane parameters. The system losses are modeled using a loss metric that quantifies the total load loss. The overall simulation is performed on the synthetic Texas 2000-bus system mapped on the geographical footprint of Texas. The results show that power substation inundation due to storm surge creates additional load losses as the hurricane traverses inland.Comment: 5 pages, 9 figures, submitted to 2023 IEEE Power and Energy Society General Meeting for revie