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Navigation between initial and desired community states using shortcuts

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arXiv 2022年

作者： Blonder, Benjamin W. Lim, Michael H. Sunberg, Zachary Tomlin, Claire Department of Environmental Science Policy and Management University of California Berkeley BerkeleyCA United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Aerospace Engineering Sciences Department University of Colorado Boulder BoulderCO United States

Ecological management problems often involve navigating from an initial to a desired community state. We ask whether navigation is possible without brute-force additions and deletions of species, using actions of varying costs: adding/deleting a small number of individuals of a species, changing the environment, and waiting. Navigation can yield direct paths (single sequence of actions) or shortcut paths (multiple sequences of actions with lower cost than a direct path). We ask (1) when is non-brute-force navigation possible?;(2) do shortcuts exist and what are their properties?;and (3) what heuristics predict shortcut existence? Using several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs;and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices. State diagrams thus unveil hidden strategies for efficiently shifting between states. © 2022, CC BY.

关键词： Navigation

A Group Decision-Making and Optimization Method Based on Relative Inverse Number

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SSRN

SSRN 2023年

作者： Liu, Chuanbin Yu, Lean Liu, Bin Wang, Dan Yang, Jianan School of Economics and Management Harbin Engineering University Harbin150000 China Center for Scientific Research and Development in Higher Education Institutes Ministry of Education Beijing10010 China College of Computer Science Chongqing University Chongqing40044 China School of Public Policy and Administration Chongqing University Chongqing40044 China

Group decision-making is an important branch of decision-making theory, and choosing the most appropriate decision-making theory under the subjective cognition of individuals has become a research hotspot. The Borda rule and Copeland rule are common ranking rules in group decision-making. This paper demonstrates that the ranked output of these two methods is unreasonable and do not accurately reflect the inner structure of the ranked input. In order to objectively quantify the difference between different rules, the concept of the relative inverse number (RIN) is proposed. A ranking decision algorithm, called the voting rule, based on relative inversions is then proposed. The RIN rule reflects differences in the ranked input structure and the ranked output more effectively than the Borda and Copeland rules. However, the complexity of the RIN rule increases exponentially with the number of alternatives. Fortunately, algorithms have been designed, combined with the Borda rule, to solve the RIN rule output. What's more, the applications of the RIN rule in science and technology evaluation are introduced. © 2023, The Authors. All rights reserved.

关键词： Inverse problems

Understanding policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address public Safety

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arXiv 2023年

作者： Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bhaskararayuni, Sai Datta Ravindran, Arun Reid, Shannon Tabkhi, Hamed Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Electrical Engineering and Computer Systems University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States Criminal Justice University of North Carolina at Charlotte 9201 University City Blvd North Carolina Charlotte28223 United States

Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48 to 73.72 in anomaly detection and 96 to 83.07 in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

DOMINO: Domain-aware Loss for Deep Learning Calibration

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arXiv 2023年

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu United States Department of Mechanical and Aerospace Engineering Herbert Wertheim College of Engineering UF United States Center for Cognitive Aging and Memory McKnight Brain Institute UF United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF United States Department of Neuroscience College of Medicine UF United States United States Air Force Research Laboratory Eglin Air Force Base Florida United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF United States

Deep learning has achieved the state-of-the-art performance across medical imaging tasks;however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even when incorrect. The code for this software is available at https://***/lab-smile/DOMINO. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

MABSplit: Faster Forest Training Using Multi-Armed Bandits

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arXiv 2022年

作者： Tiwari, Mo Kang, Ryan Lee, Je-Yong Thrun, Sebastian Piech, Chris Shomorony, Ilan Zhang, Martin Jinye Department of Computer Science Stanford University United States Oxford University United Kingdom Electrical and Computer Engineering University of Illinois at Urbana-Champaign United States Department of Epidemiology Harvard T.H. Chan School of Public Health United States

Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget. All of our experimental results are reproducible via a one-line script at https://***/ThrunGroup/FastForest. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision trees

Comparative Analysis of Common Carotid Artery Intima-Media Thickness and Lagrangian Wall Strain in Relation to Cardiovascular Risk Factors Using Ultrasound

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Ultrasound in Medicine and Biology 2025年

作者： Kiernan, Maxwell J. Mitchell, Carol Lee, Yurim Dou, Yimeng Zhang, Zhiwei Fletcher, Erica Hansen, Kristin Kliewer, Mark Lee, Matthew H. Stein, James H. Korcarz, Claudia Hess, Timothy Varghese, Tomy Department of Medical Physics University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Medicine University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI United States Department of Regional Services UW Health MadisonWI United States Department of Radiology University of Wisconsin School of Medicine and Public Health MadisonWI United States

Objective: This study examines the relationship between carotid intima-media thickness (CIMT), carotid artery wall strain indices measured via ultrasound, and their associations with cardiovascular disease (CVD) risk factors in a low CVD risk population. Methods: Eighty healthy volunteers (median age 35 y, 50% female) underwent carotid ultrasound imaging for CIMT and strain indices. Participants’ clinical data, including total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, high-sensitivity C-reactive protein, hemoglobin A1c, systolic blood pressure (SBP), diastolic blood pressure, body mass index and smoking status, were collected. Spearman Rho correlations assessed relationships between CIMT and strain indices. Multiple linear regression examined associations between strain indices and CVD risk factors, while Mann–Whitney U tests compared median differences between sexes. Statistical analyses were performed using SPSS. Results: No significant associations were seen between CIMT measurements and strain parameters. After adjustment for CVD risk factors, several strain indices were significantly associated with CVD risk factors. Age was inversely related to all strain parameters. SBP was significantly positively related to axial and shear strain. Hemoglobin A1c was significantly inversely related to most axial and shear strain (all p values © 2025 World Federation for Ultrasound in Medicine & Biology

关键词： Cardiology

Improving Confidence in the Estimation of Values and Norms

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Improving Confidence in the Estimation of Values and Norms

International Workshop on Coordination, Organizations, Institutions, and Norms for Governance of Multi-Agent Systems, COIN 2017, co-located with AAMAS 2017 and the International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems, COINE 2020, co-located with AAMAS 2020

作者： Siebert, Luciano Cavalcante Mercuur, Rijk Dignum, Virginia van den Hoven, Jeroen Jonker, Catholijn Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands AiTech Delft University of Technology Delft Netherlands Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands Department of Computing Sciences Umeå University Umeå Sweden Leiden Institute of Advance Computer Science Leiden University Leiden Netherlands

ISBN: (纸本)9783030723750

Autonomous agents (AA) will increasingly be interacting with us in our daily lives. While we want the benefits attached to AAs, it is essential that their behavior is aligned with our values and norms. Hence, an AA will need to estimate the values and norms of the humans it interacts with, which is not a straightforward task when solely observing an agent’s behavior. This paper analyses to what extent an AA is able to estimate the values and norms of a simulated human agent (SHA) based on its actions in the ultimatum game. We present two methods to reduce ambiguity in profiling the SHAs: one based on search space exploration and another based on counterfactual analysis. We found that both methods are able to increase the confidence in estimating human values and norms, but differ in their applicability, the latter being more efficient when the number of interactions with the agent is to be minimized. These insights are useful to improve the alignment of AAs with human values and norms. © 2021, Springer Nature Switzerland AG.

关键词： Autonomous agents

Channel capacity of the ribosome

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Physical Review E 2023年第4期108卷 044404-044404页

作者： Daniel A. Inafuku Kay L. Kirkpatrick Onyema Osuagwu Qier An David A. Brewster Mayisha Zeb Nakib Department of Physics University of Illinois Urbana-Champaign Urbana Illinois 61801 USA Department of Mathematics University of Illinois Urbana-Champaign Urbana Illinois 61801 USA Electrical and Computer Engineering Department Morgan State University Baltimore Maryland 21251 USA Cybersecurity Assurance and Policy Center Morgan State University Baltimore Maryland 21251 USA Department of Physics University of California Santa Barbara Santa Barbara California 93106 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA

Translation is one of the most fundamental processes in the biological cell. Because of the central role that translation plays across all domains of life, the enzyme that carries out this process, the ribosome, is required to process information with high accuracy. This accuracy often approaches values near unity experimentally. In this paper, we model the ribosome as an information channel and demonstrate mathematically that this biological machine has information-processing capabilities that have not been recognized previously. In particular, we calculate bounds on the ribosome's theoretical Shannon capacity and numerically approximate this capacity. Finally, by incorporating estimates on the ribosome's operation time, we show that the ribosome operates at speeds safely below its capacity, allowing the ribosome to process information with an arbitrary degree of error. Our results show that the ribosome achieves a high accuracy in line with purely information-theoretic means.

关键词： Biomolecular processes Emergent biological functions from complex interactions Gene expression Nucleic acid replication & information processes Biomolecules Enzymes Nucleic acids Peptides Proteins RNA Stochastic analysis

A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs

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arXiv 2024年

作者： Mayemba, Christian N. Nkashama, DJeff K. Tshimula, Jean Marie Dialufuma, Maximilien V. Muabila, Jean Tshibangu Didier, Mbuyi Mukendi Kanda, Hugues Galekwa, René Manassé Fita, Heber Dibwe Mundele, Serge Kalala, Kalonji Ilunga, Aristarque Ntobo, Lambert Mukendi Muteba, Dominique Abedi, Aaron Aruna Global GRIC Université de Sherbrooke Canada Democratic Republic Congo LISV-UVSQ Université Paris-Saclay France University of Klagenfurt Austria School of Electrical Engineering and Computer Science University of Ottawa Canada Canada Biomedical Research Unit Hospital Monkole Kinshasa Democratic Republic Congo University of Florida United States Direction of Epidemiological Surveillance Ministry of Public Health of the DRC Direction of Epidemiological Surveillance Ministry of Public Health of the DRC

This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics. Understanding how people move during epidemics is essential for modeling the spread of diseases and devising effective response strategies. Forecasting population movement is crucial for informing epidemiological models and facilitating effective response planning in public health emergencies. Predicting mobility patterns can enable authorities to better anticipate the geographical and temporal spread of diseases, allocate resources more efficiently, and implement targeted interventions. We review a range of approaches utilizing both pretrained language models like BERT and Large Language Models (LLMs) tailored specifically for mobility prediction tasks. These models have demonstrated significant potential in capturing complex spatio-temporal dependencies and contextual patterns in textual data. © 2024, CC BY.

关键词： Forecasting