Article
Quantum Science & Technology
Maurice Weber, Nana Liu, Bo Li, Ce Zhang, Zhikuan Zhao
Summary: Quantum machine learning models have the potential to be faster and more accurate than classical models, but they are also vulnerable to input perturbations. A fundamental link between binary quantum hypothesis testing and provably robust quantum classification has been formalized, leading to a tight robustness condition that puts constraints on the amount of noise a classifier can tolerate. This robustness condition against worst-case noise scenarios extends to known noise sources, providing a framework to study the reliability of quantum classification protocols beyond adversarial attacks.
NPJ QUANTUM INFORMATION
(2021)
Article
Automation & Control Systems
Seongil Hong, Keunwoo Jang, Sanghyun Kim, Jaeheung Park
Summary: The article aims to find an optimal and robust solution for on-line hierarchical least-squares optimization, focusing on task regularization for convergence and robustness. By formulating a regularized hierarchical quadratic programming and leveraging singular value decomposition and active set method, the effectiveness of the proposed algorithm is validated through numerical simulations and experimental tests in real-world robot missions.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2021)
Article
Engineering, Electrical & Electronic
Yulu Jin, Lifeng Lai
Summary: This paper investigates the adversarial robustness of hypothesis testing rules, where decision makers need to determine the underlying hypothesis of generated samples after being modified by an adversary. The study reveals that decision makers can design different decision rules to minimize error probability under different scenarios of whether the adversary is aware of the true underlying hypothesis.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Nursing
Courtney Keeler, Alexa Colgrove Curtis
Summary: This article is part of a series that aims to provide nurses with a comprehensive understanding of the concepts and principles essential to clinical research. It covers a wide range of topics from research design to data interpretation. To access all articles in the series, visit the provided link.
AMERICAN JOURNAL OF NURSING
(2023)
Article
Biotechnology & Applied Microbiology
Pauline Trinh, David S. Clausen, Amy D. Willis
Summary: This paper introduces a new method called happi for testing hypotheses about gene enrichment. The method takes into account genome quality and demonstrates its advantages over existing approaches through experiments using Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and simulations.
Article
Computer Science, Artificial Intelligence
Vo Nguyen Le Duy, Takuto Sakuma, Taiju Ishiyama, Hiroki Toda, Kazuya Arai, Masayuki Karasuyama, Yuta Okubo, Masayuki Sunaga, Hiroyuki Hanada, Yasuo Tabei, Ichiro Takeuchi
Summary: This study proposes a novel statistical approach, called Stat-DSM, to evaluate the statistical significance of discriminative sub-trajectory mining results. The proposed method properly controls the statistical significance of the extracted sub-trajectories and addresses the computational and statistical challenges of massive trajectory datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Yuan Sun, Xu Wang, Dezhong Peng, Zhenwen Ren, Xiaobo Shen
Summary: With the development of video network, image set classification (ISC) has gained attention and is used for practical applications. However, existing methods have high complexity and ignore complex structural information and hierarchical semantics. Therefore, this paper proposes a novel Hierarchical Hashing Learning (HHL) method that gradually refines discriminative information using a two-layer hash function and incorporates bidirectional semantic representation with orthogonal constraint.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Editorial Material
Obstetrics & Gynecology
Philip M. Sedgwick, Anne Hammer, Ulrik Schioler Kesmodel, Lars Henning Pedersen
Summary: Traditional null hypothesis significance testing (NHST) is widely used in obstetric and gynecological research, but its application in inferring clinical significance is controversial. Misinterpretation of statistical significance and ignorance of NHST limitations may lead to false claims and dismissal of important factors.
ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA
(2022)
Article
Computer Science, Artificial Intelligence
Chong-Xiao Shi, Guang-Hong Yang
Summary: In this paper, the resilient distributed hypothesis testing problem under multi-agent networks is studied. A novel filtering mechanism is designed and an effective resilient distributed algorithm is developed to solve the problem. The convergence analysis of the algorithm proves that the normal agents successfully learn the true state. Simulation results verify the theoretical findings and demonstrate the superiority of the proposed algorithm in tolerating adversarial agents.
Article
Computer Science, Artificial Intelligence
Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga
Summary: This paper proposes a new approach for formally describing the requirement for statistical inference and checking the appropriate use of statistical methods in programs. The authors define a belief Hoare logic (BHL) for formalizing and reasoning about statistical beliefs acquired through hypothesis testing. Examples demonstrate the usefulness of BHL in reasoning about practical issues in hypothesis testing, while also discussing the importance of prior beliefs in acquiring statistical beliefs.
ARTIFICIAL INTELLIGENCE
(2024)
Article
Health Care Sciences & Services
Richard McNulty
Summary: NHST's internal logic can be analyzed using propositional calculus, with the testable H-0 determined by analyzing the range of P-values; The correspondence between H-0 and H-A must be exhaustive to avoid false dichotomies; The conclusions derived from NHST only justify that the results are not due to chance alone, rather than proving the research hypothesis is true.
BMC MEDICAL RESEARCH METHODOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Kuen-Suan Chen, Shui-Chuan Chen, Chang-Hsien Hsu, Wei-Zong Chen
Summary: This study utilizes mathematical tools to find the confidence interval for the asymmetric tolerance index of important machine tool components, and conducts statistical hypothesis testing.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Goekhan Guel
Summary: The study focuses on decentralized detection in parallel-access sensor networks, where sensors have incomplete knowledge of statistics. It is shown that certain fundamental rules, such as joint stochastic boundedness property, do not always hold for certain uncertainty classes. However, a solution to the minimax robust decentralized detection problem is still possible, leading to a generalization of existing work.
IEEE TRANSACTIONS ON INFORMATION THEORY
(2021)
Article
Computer Science, Software Engineering
Shaoyan Guo, Huifu Xu
Summary: This study focuses on qualitative robustness in utility preference robust optimization, proposing moderate sufficient conditions for the robustness of optimal values and solutions against perturbations of exogenous uncertainty data, and examining how the tail behavior of utility functions affects robustness. Additionally, quantitative robustness of statistical estimators under the Kantorovich metric is established under certain conditions, and uniform consistency of optimal values and solutions of the models is investigated, covering utility selection and stochastic optimization problems as special cases.
MATHEMATICAL PROGRAMMING
(2021)
Article
Engineering, Biomedical
Yuan Gao, Huaqing Ni, Ying Chen, Yibin Tang, Xiaofeng Liu
Summary: In this study, a hierarchical binary hypothesis testing framework using brain functional connectivity as input biomarkers was proposed to improve the accuracy of ADHD subtype diagnosis and obtain biomarkers. Discriminative functional connectivity between ADHD subtypes was found by comparing the P-values of typical functional connectivity.
JOURNAL OF NEURAL ENGINEERING
(2023)