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G(10,30): A Minor-Minimal Intrinsically Knotted Graph
Get PDFIn this paper, we shall lay the groundwork for a proof of the minor-minimal intrinsic knotting of the graph G(10, 30). We show that this graph is in fact minor minimal with respect to the property of intrinsic knotting, i.e that no minor of G(10, 30) is intrinsically knotted. Moreover, we discuss the procedure for showing that G(10, 30) itself is intrinsically knotted, and provide a collection of subgraphs that can be used to aid in a proof. In this way, we hope to contribute to the growing list of known minor-minimal intrinsically knotted graphs
A Framework for the Verification and Validation of Artificial Intelligence Machine Learning Systems
Get PDFAn effective verification and validation (V&V) process framework for the white-box and black-box testing of artificial intelligence (AI) machine learning (ML) systems is not readily available. This research uses grounded theory to develop a framework that leads to the most effective and informative white-box and black-box methods for the V&V of AI ML systems. Verification of the system ensures that the system adheres to the requirements and specifications developed and given by the major stakeholders, while validation confirms that the system properly performs with representative users in the intended environment and does not perform in an unexpected manner. Beginning with definitions, descriptions, and examples of ML processes and systems, the research results identify a clear and general process to effectively test these systems. The developed framework ensures the most productive and accurate testing results. Formerly, and occasionally still, the system definition and requirements exist in scattered documents that make it difficult to integrate, trace, and test through V&V. Modern system engineers along with system developers and stakeholders collaborate to produce a full system model using model-based systems engineering (MBSE). MBSE employs a Unified Modeling Language (UML) or System Modeling Language (SysML) representation of the system and its requirements that readily passes from each stakeholder for system information and additional input. The comprehensive and detailed MBSE model allows for direct traceability to the system requirements. xxiv To thoroughly test a ML system, one performs either white-box or black-box testing or both. Black-box testing is a testing method in which the internal model structure, design, and implementation of the system under test is unknown to the test engineer. Testers and analysts are simply looking at performance of the system given input and output. White-box testing is a testing method in which the internal model structure, design, and implementation of the system under test is known to the test engineer. When possible, test engineers and analysts perform both black-box and white-box testing. However, sometimes testers lack authorization to access the internal structure of the system. The researcher captures this decision in the ML framework. No two ML systems are exactly alike and therefore, the testing of each system must be custom to some degree. Even though there is customization, an effective process exists. This research includes some specialized methods, based on grounded theory, to use in the testing of the internal structure and performance. Through the study and organization of proven methods, this research develops an effective ML V&V framework. Systems engineers and analysts are able to simply apply the framework for various white-box and black-box V&V testing circumstances
U2.31.29 L humerus Lateral view.JPG
No full texthttps://jagworks.southalabama.edu/baphoto-yr3_enthesealarm/1004/thumbnail.jp
U2.31.1027 R humerus Superior view.JPG
No full texthttps://jagworks.southalabama.edu/baphoto-yr3_enthesealarm/1009/thumbnail.jp
Explainable Artificial Intelligence: Approaching it From the Lowest Level
Get PDFThe increasing complexity of artificial intelligence models has given rise to extensive work toward understanding the inner workings of neural networks. Much of that work, however, has focused on manipulating input data feeding the network to assess their affects on network output or pruning model components after the often-extensive time-consuming training. It is postulated in this study that understanding of neural network can benefit from model structure simplification. In turn, it is shown that model simplification can benefit from investigating network node, the most fundamental unit of neural networks, evolving trends during training. Whereas studies on simplification of model structure have mostly required repeated model training at prohibitive time costs, assessing evolving trends in node weights toward model stabilization may circumvent that limitation.
Node Positional and magnitude stabilities were the central construct to investigate neuronal patterns in time for this study and to determine node influence in model predictive ability. Positional stability was defined as the number of epochs wherein nodes held their location compared to those from the stable model, defined in this study as a model with accuracy \u3e0.90. Node magnitude stability was defined as the number of epochs where node weights retained their magnitude within a tolerance value when compared to the stable model. To test evolving trends, a manipulated, a contrived, two life science data sets were used. Data sets were run convolutional (CNN) and deep neural network (DNN) models. Experiments were conducted to test neural network training for patterns as a predicate for investigating node evolving trends. It was postulated that highly stable nodes were most influential in determining model prediction, measured by accuracy. Furthermore, this study suggested that influential node addition to model during training followed a biological growth curve.
Findings indicated that neural network weight assignment, weight spatial structure, and progression through time were not random, strongly by model choice and choice of data set. Moreover, progress toward stability differed by model, where CNNs added influential nodes more evenly during training. The CNN model runs generally followed a biological growht curve covering an entire life, whereas for DNN model runs, the growth curve shape was more characteristic of an organism during its early life or a population unconstrained by resources, where growth tends to be exponential.
The stability approach of this study showed superior time efficiencies when compared to competing methods. The contributions of this work may assist in making AI models more transparent and easier to understand to all stakeholders, adding to the benefits of AI technologies by minimizing and dispelling the fears associated with adoption of black-box automation approaches in science and industry
U1.31.107_TA3 06.22.21 Dist. Humerus.ta3
No full textObjectives We estimate adult age distributions from Unar 1 and Unar 2, two late Umm an-Nar (2400-2100 BCE) tombs in the modern-day Emirate of Ras al-Khaimah, United Arab Emirates. These collective tombseach contained hundreds of skeletons in commingled, fragmented, and variably cremated states. Previous studies placed the vast majority of this mortuary community in a generalized “adult” category, as have most analyses of similar tombs from this period. We sought to test how adult age estimation methods compare in identifying young, middle, and old age individuals in commingled assemblages.
Materials and Methods We employed Transition Analysis 3 (TA3) and traditional age estimation methods to generate adult age distributions for each tomb. We compared these distributions between tomb contexts as well as by method.
Results Unar 1 and Unar 2 had similar adult age distributions within each method, but TA3 age distributions included significantly more middle and older adult individuals than those generated by traditional methods.
Discussion These results support findings of earlier iterations of Transition Analysis in regard to sensitivity in old adult age estimation, compared to traditional methods. Our findings indicate a potential use of TA3 in reconstructing age distributions and mortality profiles in commingled skeletal assemblages. Increasing our understanding of everyday life in the distant past necessitates better understandings of adult age, and here, we illustrate how age estimation method choice significantly changes bioarchaeological interpretations of aging in Bronze Age Arabia
U1.31.598_TA3 06.22.21 Dist. Humerus.ta3
No full textObjectives We estimate adult age distributions from Unar 1 and Unar 2, two late Umm an-Nar (2400-2100 BCE) tombs in the modern-day Emirate of Ras al-Khaimah, United Arab Emirates. These collective tombseach contained hundreds of skeletons in commingled, fragmented, and variably cremated states. Previous studies placed the vast majority of this mortuary community in a generalized “adult” category, as have most analyses of similar tombs from this period. We sought to test how adult age estimation methods compare in identifying young, middle, and old age individuals in commingled assemblages.
Materials and Methods We employed Transition Analysis 3 (TA3) and traditional age estimation methods to generate adult age distributions for each tomb. We compared these distributions between tomb contexts as well as by method.
Results Unar 1 and Unar 2 had similar adult age distributions within each method, but TA3 age distributions included significantly more middle and older adult individuals than those generated by traditional methods.
Discussion These results support findings of earlier iterations of Transition Analysis in regard to sensitivity in old adult age estimation, compared to traditional methods. Our findings indicate a potential use of TA3 in reconstructing age distributions and mortality profiles in commingled skeletal assemblages. Increasing our understanding of everyday life in the distant past necessitates better understandings of adult age, and here, we illustrate how age estimation method choice significantly changes bioarchaeological interpretations of aging in Bronze Age Arabia
U2.31.286_TA3 06.22.21 Dist. Humerus.ta3
No full textObjectives We estimate adult age distributions from Unar 1 and Unar 2, two late Umm an-Nar (2400-2100 BCE) tombs in the modern-day Emirate of Ras al-Khaimah, United Arab Emirates. These collective tombseach contained hundreds of skeletons in commingled, fragmented, and variably cremated states. Previous studies placed the vast majority of this mortuary community in a generalized “adult” category, as have most analyses of similar tombs from this period. We sought to test how adult age estimation methods compare in identifying young, middle, and old age individuals in commingled assemblages.
Materials and Methods We employed Transition Analysis 3 (TA3) and traditional age estimation methods to generate adult age distributions for each tomb. We compared these distributions between tomb contexts as well as by method.
Results Unar 1 and Unar 2 had similar adult age distributions within each method, but TA3 age distributions included significantly more middle and older adult individuals than those generated by traditional methods.
Discussion These results support findings of earlier iterations of Transition Analysis in regard to sensitivity in old adult age estimation, compared to traditional methods. Our findings indicate a potential use of TA3 in reconstructing age distributions and mortality profiles in commingled skeletal assemblages. Increasing our understanding of everyday life in the distant past necessitates better understandings of adult age, and here, we illustrate how age estimation method choice significantly changes bioarchaeological interpretations of aging in Bronze Age Arabia
U2.31.520_TA3 06.22.21 Dist. Humerus.ta3
No full textObjectives We estimate adult age distributions from Unar 1 and Unar 2, two late Umm an-Nar (2400-2100 BCE) tombs in the modern-day Emirate of Ras al-Khaimah, United Arab Emirates. These collective tombseach contained hundreds of skeletons in commingled, fragmented, and variably cremated states. Previous studies placed the vast majority of this mortuary community in a generalized “adult” category, as have most analyses of similar tombs from this period. We sought to test how adult age estimation methods compare in identifying young, middle, and old age individuals in commingled assemblages.
Materials and Methods We employed Transition Analysis 3 (TA3) and traditional age estimation methods to generate adult age distributions for each tomb. We compared these distributions between tomb contexts as well as by method.
Results Unar 1 and Unar 2 had similar adult age distributions within each method, but TA3 age distributions included significantly more middle and older adult individuals than those generated by traditional methods.
Discussion These results support findings of earlier iterations of Transition Analysis in regard to sensitivity in old adult age estimation, compared to traditional methods. Our findings indicate a potential use of TA3 in reconstructing age distributions and mortality profiles in commingled skeletal assemblages. Increasing our understanding of everyday life in the distant past necessitates better understandings of adult age, and here, we illustrate how age estimation method choice significantly changes bioarchaeological interpretations of aging in Bronze Age Arabia