國立虎尾科技大學 |

Statistical Methods for Nonproliferation and Counterproliferation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Statistical Methods for Nonproliferation and Counterproliferation./
作者:	Hollis, Andrew Nathan.
面頁冊數:	1 online resource (139 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
Contained By:	Dissertations Abstracts International85-01A.
標題:	Military studies. -
電子資源:	click for full text (PQDT)
ISBN:	9798379879402

Statistical Methods for Nonproliferation and Counterproliferation.
Hollis, Andrew Nathan.

Statistical Methods for Nonproliferation and Counterproliferation. - 1 online resource (139 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2023.

Includes bibliographical references

With the advent of nuclear weapons in the middle of the 20th century, the United States and other foreign powers have pursued nuclear nonproliferation and nuclear counterproliferation policies. Nuclear nonproliferation and counterproliferation efforts focus on a wide variety of issues including preventing the spread of nuclear materials and nuclear weapons technology to countries that do not yet have them and neutralizing or mitigating threats posed by those who have illegitimately obtained or are in the process of obtaining nuclear materials or a nuclear device. The aim of this dissertation is to propose statistical methods that can address technical challenges in implementing nonproliferation and counterproliferation policy. We focus on the problems of urban nuclear source localization, arms-control monitoring, and proliferation detection in dual-use scientific research (research the can be used for military or civilian applications).Effective urban source localization requires constructing a configuration of radiation detectors that is optimal for estimating the source location. We propose constructing a Gaussian process surrogate model of the mutual information design criterion used for configuration selection. We demonstrate that optimizing this inexpensive surrogate model leads to effective source localization over the space of all possible configurations for a simulated source localization scenario in downtown Washington, DC. For the arms control monitoring problem, we propose a novel Bayesian hidden Markov model that represents a concealed arms-development process being monitored as well observable data emitted by the process. Our model reparameterizes the HMM transition probabilities in terms of activity duration and allows us to answer key questions about the monitored process. We also propose a novel framework for incorporating subject matter expertise about the process and potential noise into the inference. We demonstrate the proposed approach on an underground explosives test case study. In addressing the dual-use technology problem, we propose two new variational inference loss functions for fitting a deep learning text classification model with uncertainty quantification. The framework is computationally tractable for large models and meets important uncertainty quantification objectives including producing predicted class probabilities that reflect our prior conception of how different classes are related.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379879402Subjects--Topical Terms:

1148605
Military studies.
Index Terms--Genre/Form:

554714
Electronic books.

Statistical Methods for Nonproliferation and Counterproliferation.
LDR:03698ntm a2200325K 4500 001 1141151
005 20231130111627.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379879402
035 $a (MiAaPQ)AAI30516356
035 $a (MiAaPQ)NCState_Univ18402040797
035 $a AAI30516356
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Hollis, Andrew Nathan. $3 1464780
245 1 0 $a Statistical Methods for Nonproliferation and Counterproliferation.
264 0 $c 2023
300 $a 1 online resource (139 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
500 $a Advisor: Mattingly, John ; Wilson, Alyson.
502 $a Thesis (Ph.D.)--North Carolina State University, 2023.
504 $a Includes bibliographical references
520 $a With the advent of nuclear weapons in the middle of the 20th century, the United States and other foreign powers have pursued nuclear nonproliferation and nuclear counterproliferation policies. Nuclear nonproliferation and counterproliferation efforts focus on a wide variety of issues including preventing the spread of nuclear materials and nuclear weapons technology to countries that do not yet have them and neutralizing or mitigating threats posed by those who have illegitimately obtained or are in the process of obtaining nuclear materials or a nuclear device. The aim of this dissertation is to propose statistical methods that can address technical challenges in implementing nonproliferation and counterproliferation policy. We focus on the problems of urban nuclear source localization, arms-control monitoring, and proliferation detection in dual-use scientific research (research the can be used for military or civilian applications).Effective urban source localization requires constructing a configuration of radiation detectors that is optimal for estimating the source location. We propose constructing a Gaussian process surrogate model of the mutual information design criterion used for configuration selection. We demonstrate that optimizing this inexpensive surrogate model leads to effective source localization over the space of all possible configurations for a simulated source localization scenario in downtown Washington, DC. For the arms control monitoring problem, we propose a novel Bayesian hidden Markov model that represents a concealed arms-development process being monitored as well observable data emitted by the process. Our model reparameterizes the HMM transition probabilities in terms of activity duration and allows us to answer key questions about the monitored process. We also propose a novel framework for incorporating subject matter expertise about the process and potential noise into the inference. We demonstrate the proposed approach on an underground explosives test case study. In addressing the dual-use technology problem, we propose two new variational inference loss functions for fitting a deep learning text classification model with uncertainty quantification. The framework is computationally tractable for large models and meets important uncertainty quantification objectives including producing predicted class probabilities that reflect our prior conception of how different classes are related.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Military studies. $3 1148605
650 4 $a Sensors. $3 1003702
650 4 $a Nuclear weapons. $3 661862
650 4 $a Arms control & disarmament. $3 1464782
650 4 $a Weapons of mass destruction. $3 654461
650 4 $a Text categorization. $3 1464781
655 7 $a Electronic books. $2 local $3 554714
690 $a 0750
710 2 $a North Carolina State University. $3 845424
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-01A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30516356 $z click for full text (PQDT)